Graph Neural Networks (GNNs) have made significant strides in the analysis and modeling of complex network data, particularly excelling in graph and node classification tasks. However, the "closed box" nature of GNNs impedes user understanding and trust, thereby restricting their broader application. This challenge has spurred a growing focus on demystifying GNNs to make their decision-making processes more transparent. Traditional methods for explaining GNNs often rely on selecting subgraphs and employing combinatorial optimization to generate understandable outputs. However, these methods are closely linked to the inherent complexity of GNNs, leading to higher explanation costs. To address this issue, we introduce a lower-complexity proxy model to explain GNNs. Our approach leverages knowledge distillation with inter-layer alignment, specifically targeting the challenge of over-smoothing and its detrimental impact on model explanation. Initially, we distill critical insights from complex GNN models into a more manageable proxy model. We then apply an inter-layer alignment-based distillation technique to ensure alignment between the proxy and the original model, facilitating the extraction of node or edge-level explanations within the proxy framework. We theoretically prove that the explanations derived from the proxy model are faithful to both the proxy and the original model. Additionally, we show that the upper bound of unfaithfulness between the proxy and the original model remains consistent when the distillation error is infinitesimal. This inter-layer alignment knowledge distillation technique enables the proxy model to retain the knowledge learning and topological representation capabilities of the original model to the greatest extent. Experimental evaluations on numerous real-world datasets confirm the effectiveness of our method, demonstrating robust performance.

This paper addresses the intricate control challenges posed by unknown Euler–Lagrange systems operating under actuator faults and subject to asymmetric error constraints. A novel prescribed performance control (PPC) strategy is proposed, leveraging a dual-error transformation technique. The first transformation is designed to decouple the initial tracking errors from the predefined performance functions, thereby significantly relaxing the stringent initial condition dependencies typical of conventional PPC schemes and ensuring errors remain confined within adjustable asymmetric boundaries. The second transformation introduces exponential decay constraint functions to dynamically regulate the convergence rate and steady-state accuracy of the tracking errors. Theoretical analysis rigorously demonstrates that the proposed strategy, without requiring prior knowledge of the system’s complex nonlinearities, guarantees global boundedness of all closed-loop signals. Furthermore, it ensures that tracking errors converge to prespecified asymmetric residual zones at a preset rate, even in the presence of actuator faults. The efficacy and superiority of the proposed strategy are validated through comparative simulations conducted on a two-link robotic manipulator. The experimental source code is available at https:// github.com/hclll22/Dual-Error-Transformation-Approach-PPC.

The main focus of this work is on deriving a prescribed-time optimization control solution for a family of networked systems with uncertain nonlinearities and local time-varying cost functions. The underlying problem becomes much more challenging if the communication topology is not only local but also frequently switching. To overcome the technique difficulty arising from the local and switching network, a casted observer is introduced into the proposed distributed prescribed-time optimization control scheme, which allows the necessary non-local information to be localized within a prescribed time under the switching topology. Furthermore, the proposed prescribed-time optimization control algorithm is based on finite time-varying gain, avoiding excessive control input (especially excessive initial control input) caused by sustained high gain based method, without which the prescribed-time optimization result can not be derived. In addition, the gain switching time in the switching controller can be pre-assigned, distinguishing itself from most existing finite gain based prescribed-time control methods. Finally, the effectiveness of the method is validated through both numerical simulation and real-world experiment, demonstrating its potential for practical applications in networked control systems.

This paper investigates tracking control for the cooperative manipulation of dual-arm robots without velocity measurement, where prescribed performance specifications are imposed on the motion of the grasped object. An integrated dynamic model within the task space is formulated by establishing the interrelations that link the position and force constraints of the robotic arms to the grasped object. Subsequently, a motion model of the dual-arm system is derived, which remains unaffected by internal forces. To quickly obtain the object’s velocities with high precision, a practical prescribed-time velocity observer is constructed by incorporating a time-dependent scaling function. A revised performance boundary function is utilized to integrate the tracking errors associated with the grasping system, unlike existing approaches that define it solely as a time-dependent function. The barrier function is employed to map the complex output error constraints to new error variables, thereby facilitating the simplification of the controller design. Finally, simulation results are presented to illustrate and evaluate the effectiveness of the proposed method.

This work presents an estimator-based distributed preset-time algorithm that effectively addresses the equality-constrained optimization problem on directed graphs (digraphs). Initially, we propose an innovative distributed preset-time estimator to accurately estimate the global information related to the cost function. Building on this, we develop an estimator-based distributed robust preset-time optimization algorithm incorporating integral sliding mode control, which is specifically tailored for strongly connected networks. Compared with existing algorithms, the proposed algorithm features three key innovations: improved precision in convergence time, enhanced robustness against disturbances, and expanded applicability to network topologies. Finally, we validate the preset-time optimization algorithm through numerical simulations, demonstrating that its convergence rate significantly outperforms those of current finite- and fixed-time algorithms.

This article addresses the fundamental issues of solvability and normalization in singular Boolean networks (SBNs) from a new perspective based on the admissible and normal initial state sets. It presents novel results and removes the restrictive conditions found in existing literature. First, the state transition matrix of SBNs is constructed by defining a new operator, and the admissible initial state set with the normal initial state set, of SBNs is introduced. Second, the problems of the solvability and uniqueness of the solution to a general SBN are converted into computing its admissible and normal initial state sets, which can be analytically computed using the derived formulas. Third, based on the normal initial state set, a necessary and sufficient condition for solving the normalization problem of general SBNs is established for the first time, which removes the restrictions in the existing literature. Finally, the results obtained are compared with existing literature and illustrated with examples.

A novel approach is proposed for flexible performance-based control of strict-feedback systems subject to input saturation. The core design consists of three key components. First, the regulation of the performance function (PF) is achieved by reformulating it as an adaptive modification of its exponential index, exploiting its inherent structural properties. Second, a performance indicator function (PIF) is constructed based on output-side information by analyzing the system behavior in the absence of saturation, thereby avoiding the reliance on input-side compensation signals with limited differentiability. Third, a first-order auxiliary system is designed to adaptively adjust the exponential index in real time, driving the PIF to closely track the upper envelope of the actual tracking error. As a result, the proposed self-adjusting PF (SAPF) is able to maintain a dynamic balance between input and output behaviors by relaxing performance boundaries when constraint violations are imminent while actively accelerating PF contraction to enhance transient performance under saturation constraints. Building on this framework, an SAPF-based control algorithm is developed with rigorous closed-loop stability guarantees. Finally, the effectiveness and superiority of the proposed method are demonstrated through quantitative simulation comparisons with two advanced algorithms in a vehicle lane-keeping task.

For uncertain strict-feedback nonlinear systems with self-restructuring structures and nonaffine dynamics, this article addresses the challenge of achieving exact full state zero-error stabilization within a prescribed finite time. An adaptive prescribed-time control scheme is proposed, which guarantees that all system states converge to zero within the user-specified settling time, irrespective of initial conditions. The controller is designed based on a time-varying scaling state transformation and incorporates the Nussbaum function to handle unknown self-restructuring control gains. The self-restructuring structures are treated as a time-state-dependent lump, which is effectively estimated by freezing both time and system states and then applying an adaptive estimation strategy. Numerical simulations on a piezoelectric-actuated stage and a second-order nonlinear system are conducted to demonstrate the effectiveness of the proposed scheme.

In this article, a prescribed-time output feedback controller is proposed for a class of uncertain nonlinear systems with unknown control coefficients and mismatched nonvanishing disturbances. Both unknown control coefficients and mismatched disturbances are tricky to address by the existing prescribed-time output feedback control frameworks. Differently, a novel prescribed-time control criterion in conjunction with Nussbaum functions is proposed, and prescribed-time stability is achieved. Furthermore, design methods for a state observer and a prescribed-time output feedback controller are developed. With the proposed control design, both the system output and observer errors are rigorously proved to converge to zero within a prescribed time. Moreover, the unified prescribed performance (UPP) of the system output and the satisfaction of output constraints are simultaneously achieved. Numerical simulations and experiments are provided to illustrate the effectiveness of the proposed control design.

This article presents a novel nonlinear adaptive proportional-integral (PI)-like tracking control approach designed for a class of uncertain nonlinear systems. The proposed method offers several key advantages: 1) it maintains a simple PI structure while incorporating nonlinear elements; 2) unlike traditional PI control, which typically employs fixed PI gains and is susceptible to integration saturation, this approach utilizes self-tuning PI gains to effectively eliminate saturation issues, thereby addressing the long-standing windup problem; and 3) it skillfully manages both the transient behavior and steady-state accuracy of the tracking error through a new prescribed performance function that is independent of initial conditions. This ensures that, for any unknown bounded initial tracking errors, the proposed PI-like control can uniformly confine the tracking error to a specified boundary (accuracy) within a predetermined time rather than over an infinite duration. The effectiveness and advantages of this method are validated through simulation results.

In this article, we address the problem of prescribed-time distributed convex optimization (DCO) for a class of networked Euler-Lagrange systems (NELSs) operating over undirected connected graphs. By utilizing position-dependent measured gradient values of local objective functions and facilitating local information exchanges among neighboring agents, we construct a set of auxiliary systems that collaboratively seek the optimal solution. The prescribed-time DCO problem is then reformulated as a prescribed-time stabilization challenge of an interconnected error system. We propose a prescribed-time small-gain criterion to characterize the prescribed-time stabilization of the system, presenting a novel approach that enhances effectiveness beyond existing asymptotic or finite-time stabilization methods for interconnected systems. Based on this criterion and the auxiliary systems, we design innovative adaptive prescribed-time local tracking controllers for the subsystems. The prescribed-time convergence is achieved through the introduction of time-varying gains that increase to infinity as time approaches the prescribed deadline. The Lyapunov function, along with prescribed-time mapping, is employed to establish the prescribed-time stability of the closed-loop system and the boundedness of internal signals. Finally, the theoretical results are validated through a numerical example.

This work considers the prescribed-time control problem of uncertain nonlinear systems with unknown control coefficients via an event-triggering strategy. In contrast to existing event-based prescribed-time works where the control coefficients are supposed to be known, in this work, not only the magnitude but also the sign of the control coefficient is allowed to be unknown. Based on a newly established Nussbaum-type lemma, the underlying problem is addressed by proposing an adaptive prescribed-time control scheme, which is characterized by the joint design of the triggering condition and actual controller. Moreover, the developed control scheme enables the execution time to be extended to infinity, distinguishing itself from those where the valid execution time can only be finite (ended at the prescribed time). It is proved that the closed-loop signals are globally bounded and system states converge to the origin within a prespecified time. Besides, the Zeno-free behavior is ensured. A robotic manipulator is exploited to demonstrate the validity of the theoretical findings.

Developing structurally simple and functionally trustworthy control strategies for multi-input multi-output (MIMO) nonlinear dynamic systems has always been an interesting yet challenging research topic in the control community. In this note, we present a tracking control design approach embedded with the key intelligent elements/actions (IEs/ICs). More specifically, by properly exploiting and processing fundamental IEs/ICs, such as “penalty/punishment”, “experience/memory”, and “forecasting/prediction” often observed from and utilized in human decision making, we develop an interpretable PID-like control strategy capable of ensuring asymptotic tracking for nonaffine systems in the presence of modeling uncertainties, MIMO couplings, and unexpected actuation faults. The key design steps consist of analytically characterizing the fundamental IEs/ICs via certain mathematical representations, introducing generalized error, selecting and converting the related IEs/ICs into a signal carrying intelligence ingredients, and adaptively weighting such a signal to eventually produce the control action. The proposed framework of blending intelligence-like ingredients into control synthesis proves promising and is expected to stimulate interest in developing explainable IEs/ICs-driven control strategies for nonlinear dynamic systems.

This work addresses the prescribed-time consensus tracking problem for multiagent systems (MASs) under denial-of-service (DoS) attacks, where certain communication links between agents are intermittently disrupted or rendered unavailable. Due to the DoS attack, although appropriate defense mechanisms may be employed to recover some of the attacked or backup connections, the communication topology evolves from a fixed graph to a time-varying switching topology. This ever-changing network introduces significant challenges, leading to discontinuities in the Laplacian matrix, which complicates both prescribed-time controller design and stability analysis. To tackle these challenges, a prescribed-time stability lemma (Lemma 5) is developed along with a vital inequality (Lemma 6) that establishes the quantitative relationship between Lyapunov functions across switching instants. Building on these results, a novel distributed observer is designed to accurately estimate the state of the leader within the prescribed time, despite the occurrence of DoS attacks. Subsequently, a coordinate transformation and a fractional-power backstepping technique are introduced to construct a control protocol that achieves prescribed-time consensus tracking. The proposed approach is rigorously supported by theoretical analysis and is further validated through its application to multiple unmanned vehicles.

This article investigates the prescribed-time leader-following consensus problem for heterogeneous multiagent systems (MASs) with unknown sensor sensitivity. Considering a connected undirected topology, we introduce a time-varying dual observer/controller design framework that leverages both regular local and inaccurate feedback to achieve consensus tracking within a prescribed time. The proposed analytical framework applies to MASs equipped with sensors exhibiting uncertain sensitivities. A key innovation of our design is the framework of a distributed matrix pencil formulation based on the worst case sensor, leading to control parameters that exhibit sufficient robustness and relatively low conservativeness. Additionally, we establish a bounded time-varying feedback (TVF) scheme that extends the prescribed-time distributed protocol to an infinite time domain without compromising final control accuracy. This includes a detailed discussion of the analytical relationship between switching time and the upper bound of the time-varying gain. In particular, we employ the proportional coefficient obtained from several matrix pencil formulations along with a monotonically increasing time-varying (blow-up) function to derive the feedback gain, simplifying the complexity of control design. Simulations validate the effectiveness of the methodology through a series of electromechanical systems and single-link robot manipulators.

This work provides an exponential tracking control solution for a class of nonlinear systems characterized by unmatched, non-vanishing uncertainties and deferred full-state constraints. The presence of both non-vanishing and unmatched uncertainties complicates the task of achieving exponential tracking rather than regulation, particularly in scenarios involving deferred full-state asymmetric constraints alongside steady-state and transient performance requirements. To address these challenges, several key techniques are employed. Firstly, a timevarying feedback gain technique is utilized to ensure exponential tracking of the strict-feedback system. Secondly, we develop an asymmetric constraint mapping function that integrates the system state, tracking error, a finite-time adjustment function (AF), and an exponential AF to tackle performance issues without violating the deferred full-state asymmetric constraints, even when the initial conditions are unknown. Thirdly, an important lemma (Lemma 2) is derived to guarantee the boundedness of virtual controller derivatives, even as the exponential AF approaches infinity. Additionally, all signals in the closed-loop system are ultimately uniformly bounded. The effectiveness of the proposed scheme is validated through two examples.

This paper investigates the consensus tracking control problem for a class of nonlinear multi-input multi-output (MIMO) heterogeneous multi-agent systems (HMASs), where the dimension of the dynamics of each agent is allowed to be different from each other. In addition, the control gain matrices (CGMs) with unknown time-varying coefficients and actuator faults with unpredictable jumps are also involved in the considered MIMO HMASs, all of which would cause damage for the system performance. First, a novel distributed auxiliary filter by feat of time-varying technology is introduced, which allows the zero-error estimation for the desired trajectory to be achieved and the asymptotic consensus tracking result to be further realized. Then, a cooperative adaptive control solution is proposed to ensure the asymptotical consensus tracking control result, in spite of the inherent unknown time-varying coefficients, unpredictable jumps caused by the unknown actuator faults, unknown disturbances and uncertain system parameters, distinguishing itself from those existing cooperative control works for HMASs where only ultimately uniformly bounded (UUB) result is derived. This is achieved mainly by the introduction of a series of Nussbaum functions and the employment of the adaptive estimation techniques. The effectiveness of the proposed control algorithm is confirmed by the simulation conducted on a group of HMASs involving unmanned aerial vehicles (UAVs) and autonomous surface vessels (ASVs).Note to Practitioners—In large-scale complex communication networks, uncertain HMASs with different structures and functions are capable of exchanging information and collaborating with each other to accomplish more complex and diverse tasks. Simultaneously, the probability of actuator faults within the HMASs increases dramatically, and the fault of a single agent may evolve into the failure of the whole system. Thus, the safety and reliability of HMASs are extremely important. Additionally, the control direction (the symbol of control gain) caused by both CGMs with coupling property of MIMO systems and actuator faults with unpredictable jumps, may not be guaranteed to be known in practical application, such as ship autopilot systems or uncalibrated visual servoing. This will have a considerable influence on the system’s control performance. On account of the threat of nonlinear uncertainties, and unknown control direction yield CGMs and actuator faults to HMASs, an adaptive fault-tolerant control solution based on an effective distributed time-varying auxiliary filter, is developed for MIMO HMASs to guarantee the asymptotical consensus tracking control result. Further, the proposed control scheme has been illustrated to be feasible through simulation experiment conducted on a group of HMASs consisting of UAVs and ASVs.

This paper proposes a low-complexity tracking control framework for uncertain nonlinear systems in strict feedback and normal forms, respectively. By leveraging a smooth scaling function, these control schemes ensure unified prescribed performance for the output tracking error of strict feedback nonlinear systems and the full-state tracking errors of normal form nonlinear systems. The notion of unified prescribed performance allows for different performance behaviors via performance functions, which can be either constant or time-varying with arbitrarily large initial values. The main contribution is achieving unified prescribed performance for full-state tracking errors without imposing feasibility conditions, a limitation of existing approaches. To eliminate these strict conditions, we introduce a uniform transformation independent of initial conditions. Additionally, the proposed control schemes are low-complexity since they do not require adaptive mechanisms or function approximation to deal with uncertainties and disturbances. The effectiveness of these frameworks is demonstrated through comparative analysis.

This paper proposes an adaptive tracking control framework for nonlinear systems with unmodeled dynamics, ensuring both practical prescribed-time convergence and prescribed performance for full-state errors. Existing methods often depend on unbounded gains, focus only on output tracking error, or rely on initial conditions, restricting their practical applicability. To overcome these issues, we propose a novel adaptive control framework that constrains full-state errors independent of initial conditions and drives them to a prescribed region within a predefined time. This is achieved by using a bounded, continuously differentiable, prescribed-time gain. An adaptive mechanism with a dissipating term is designed to handle unmodeled dynamics and guarantee zero tracking error even under nonvanishing disturbances. Moreover, a smooth scaling function is introduced to enforce desired transient and steady-state performance while reducing large initial control effort. Numerical simulations demonstrate the superiority of the proposed method compared to existing approaches.

This article presents an adaptive neural network-based sliding mode control (SMC) strategy aimed at achieving predefined-time and predefined-accuracy (PTPA) convergence of tracking errors. Notably, the proposed approach does not require prior knowledge of the upper bounds of uncertainties or the control direction. To ensure PTPA convergence and prevent singularity, a novel piecewise PTPA sliding mode manifold incorporating a nonlinear compensating term is introduced. This compensating term is specifically designed to address the bounded sliding-mode errors induced by model uncertainties and external disturbances. Furthermore, by enforcing a PTPA constraint on the sliding mode function and utilizing the Nussbaum function, the system states can converge to a predefined neighborhood of the sliding mode surface within a predefined time. An adaptive neural network-based PTPA SMC law is then developed, eliminating discontinuous terms and effectively mitigating the chattering issue. The proposed control scheme is rigorously proven to achieve PTPA convergence and asymptotic convergence. The efficacy of the designed control strategy is validated through two numerical examples, demonstrating its superior performance.

For quaternion-based spacecraft attitude tracking control, most existing prescribed performance control (PPC) schemes require that the scalar part of the error quaternion remain non-zero during the attitude maneuvering, meaning that only local operational range is allowed, which is too restrictive from practical point of view. Differently, by imposing a novel performance constraint, a piecewise virtual control law without singular term is designed, which is singular in most of the existing control schemes if the scalar part of the error quaternion is equal to zero, thus naturally obviating the classical assumption and resting in a global solution. Moreover, the unified prescribed performance constraints are imposed on both the attitude error and virtual angular velocity error. With such design, the performance boundary is uniform with respect to initial error, which implies that off-line computation of performance boundary for initial error can be avoided. Particularly, the initial value of the virtual angular velocity error is difficult to obtain off-line. In addition, by utilizing neural network approximation method, the Nussbaum gain technique and a positive integrable function, the proposed control is able to achieve asymptotic attitude tracking in the presence of inertia uncertainties, external disturbances and actuators fault, as rigorously authenticated by Lyapunov stability theory. A numerical example is provided to verify the effectiveness of proposed control scheme.

This paper investigates the prescribed-time cooperative output regulation (PTCOR) for a class of linear heterogeneous multi-agent systems (MASs) under directed communication graphs. As a special case of PTCOR, the necessary and sufficient condition for prescribed-time output regulation of an individual system is first explored, whereas only sufficient conditions are developed in the literature. A PTCOR algorithm is subsequently developed, composed of prescribed-time distributed observers, local state observers, and tracking controllers, utilizing a distributed feedforward method. This approach converts the PTCOR problem into the prescribed-time stabilization problem of a cascaded subsystem. The criterion for the prescribed-time stabilization of the cascaded system is proposed, differing from that of traditional asymptotic or finite-time stabilization of a cascaded system. It is proven that the regulated outputs converge to zero within a prescribed time and remain at zero afterward, while all internal signals in the closed-loop MASs are uniformly bounded. Finally, the theoretical results are validated through two numerical examples.

Angle-constrained formation control has garnered significant attention owing to the advantage of interedge angles invariant under translation, rotation, and scaling. However, most existing approaches addressing this problem are applicable only to single- or double-integrator dynamics, which are often impractical in real-world scenarios. In this article, an angle rigidity-based adaptive formation control framework is introduced for nonlinear multiagent systems subject to mismatched uncertainties. The proposed control framework integrates a prescribed performance control approach with a recursive backstepping procedure, offering several key advantages: the capability to handle unmatched system uncertainties, the preservation of angle rigidity throughout the formation process, and the assurance that the triangulated formation shape is asymptotically achieved without risking collisions between neighboring agents. Furthermore, since the control input of each agent only requires local information related to its neighbors, which can be obtained locally from its own sensors, the proposed control method can be deployed in a communication-free environment. The effectiveness of the proposed control algorithms is validated by extensive numerical simulation.

Most existing prescribed performance formation control methods impose performance requirements on the consensus error rather than directly on the relative states between agents, which limits the physical interpretability of their solutions. This article proposes a novel adaptive prescribed performance formation control strategy that ensures prescribed performance of relative errors in uncertain high-order multiagent systems under both directed and undirected graphs. Since performance constraints are considered for relative errors, the error dynamics involve a coupled nonlinear interaction term that contains global graphical information among agents, making the design of a fully distributed control strategy more challenging. By proposing a series of nonlinear mappings and utilizing the edge Laplacian along with Lyapunov stability theory, the presented formation control scheme offers several advantages over existing approaches. Different performance requirements can be accommodated in a unified manner by solely tuning the design parameters a priori, eliminating the need for control redesign and stability reanalysis under the proposed fixed control protocol. This enhances user-friendliness and reduces implementation complexity. Furthermore, the verification process for the initial constraint, which is often complex and burdensome in existing prescribed performance control methods, is entirely avoided when the performance requirements are global. Additionally, the proposed approach fully decouples nonlinear interactions and ensures the asymptotic stability of the formation manifold through an adaptive parameter estimation technique. The effectiveness of the theoretical results is demonstrated through simulations.

This article is concerned with dynamic analysis and neural-adaptive prescribed-time control of the magnetic-field electromechanical transducer incorporating a memristor. First, a fractional-order (FO) mathematical model is developed, which comprehensively characterizes fractional properties of various dielectrics and establishes the relationship between magnetic flux and electric charge. The dynamical analysis explores internal evolution and complexity performance concerning a single factor or double factors among the FO, system parameter, and memristor configuration by the Bifurcation diagram, sample entropy, and complexity from multiple perspectives. Subsequently, a neural-adaptive prescribed-time control scheme is proposed to transform detrimental chaotic oscillations into orderly motions, achieve the pregiven tracking precision and accommodating both actuator fault and system uncertainty. The controller design consists of three key steps: 1) a deferred constraint function is imposed on the tracking error starting from anywhere to get assignable tracking precision within a specified time, ensuring collision avoidance; 2) a type-2 fuzzy wavelet neural network (FWNN) is utilized effectively to handle parameter perturbations and system uncertainties; and 3) a second-order FO tracking differentiator (TD) is utilized to address the "explosion of complexity" of traditional backstepping under actuator fault model. It is shown that the proposed scheme is able to ensure the boundness of all signals of the closed-loop system. Finally, extensive simulation experiments are conducted to validate the effectiveness and robustness of the rendered scheme.

The control of a humanoid robot equipped with one arm and multiple fingers, designed primarily for grasping and manipulating various objects, is investigated. Synchronizing the movements of the fingers is a challenging task, as each joint must reach the desired angle simultaneously to ensure a firm grasp. The success of this task hinges on the synchronization of convergence times for each finger joint; otherwise, the object may slip or escape. This challenge is further intensified by uncertainties in the dynamics of the hand or the object. We present decentralized prescribed-time tracking control strategies for the dynamical system comprising the arm-finger combination. In this system, the fingers are primarily used for grasping the object while the arm is responsible for moving, tilting, or flipping it. To streamline the controller structure and simplify the stability analysis, we design a linear controller based on the maximum eigenvalue of a parameter matrix and establish a new technical lemma, which paves the way for the stability analysis of the prescribed-time tracking and the reduction of the input efforts of the actuator. We develop robust and decentralized adaptive control schemes separately for the arm and fingers, achieving better transient performance with less prior knowledge and lower computation costs. Finally, we validate the proposed controller's performance through kinematic simulations of grasping and moving tasks in 3-D, alongside numerical simulations that demonstrate the tracking performance of our algorithm in the joint space.

In this article, a double-channel event-triggered control method is developed for nonlinear uncertain interconnected systems using backstepping techniques, which introduces event-triggering mechanisms at both the sensor and controller sides. Using event-triggering mechanism at the sensor side presents a challenge to the backstepping control design as the discontinuous state/output signals received at the controller side result in nondifferentiable virtual control signals. This challenge becomes more pronounced when considering more general types of event-triggering mechanisms. Compared with existing methods, this article proposes a different idea with three innovative features: 1) the proposed event-triggering mechanism does not require the calculation of virtual control signals at the sensor side before transmitting them to the controller side; 2) the output triggering is considered directly, and there is no need to design separate controllers for the two communication scenarios without and with event-triggering, thereby avoiding the effect of errors caused by processing substitutions; and 3) it necessitates the online update of only one parameter estimator, avoiding the issue of over-parameterization. Finally, we validate the effectiveness and advantages of the proposed decentralized event-triggered control approach through a numerical case study.

This article presents a double-channel (sensor-to-controller channel and controller-to-actuator channel) event triggered control method for nonlinear interconnected systems subject to sensor and actuator faults via the backstepping technique. It should be emphasized that the utilization of triggering mechanism at the sensor side poses a challenge to the design of backstepping control, as it leads to nondifferentiable virtual control signals due to the discontinuous nature of the state/output signals received at the controller side. In contrast to existing methods, the proposed event triggering mechanism eliminates the need for computing virtual control signals at the sensor side before transmitting them to the controller side. By establishing the relationships of the corresponding variables in two communication scenarios (namely, without and with event triggering) and introducing dynamic filtering technique, the problem of nondifferentiable virtual control signals in backstepping design is solved. We present a numerical case study to validate the effectiveness and advantages of the proposed decentralized event triggered control approach.

This article addresses the distributed prescribed-time convex optimization (DPTCO) problem for high-order nonlinear multiagent systems (MASs) under undirected connected graphs. A cascade design framework is proposed that divides the DPTCO implementation into distributed optimal trajectory generator design and local reference trajectory tracking controller design. The DPTCO problem is then transformed into the prescribed-time stabilization problem of a cascaded system. Using changing Lyapunov functions and time-varying state transformations with sufficient conditions, we establish criteria for prescribed-time stabilization and prove the boundedness of internal signals in closed-loop MASs. The framework addresses robust DPTCO for chain-integrator MASs with disturbances through the introduction of novel sliding-mode variables and time-varying gains. It also solves adaptive DPTCO for strict-feedback MASs with parameter uncertainty via backstepping method and descending power state transformation. Two numerical examples verify the theoretical results.

This article investigates the stability of fully quantized nonlinear systems under irregular denial-of-service (IDoS) attacks with unpredictable targets and frequencies. Unlike most existing works that focus only on DoS attacks in fixed or single-type channels, this work considers a more general attack mode, where attacks can occur in the input, output, or both simultaneously. During an attack, the signal in the affected channel is interrupted, rendering it inaccessible. Even if the system is safe, only quantized output signal is available. To address this, a novel state estimator is designed that switches different observer gains depending on the attacked channel. On this basis, we develop an output-feedback adaptive control algorithm that utilizes the estimated signals and incorporates a dynamic filtering technique. The control strategy effectively resists IDoS attacks, ensuring that all closed-loop signals remain semiglobally uniformly ultimately bounded (SUUB), and the stability error converges to a small residual set near the origin. Numerical simulation further confirmed the advantages and effectiveness of the proposed scheme.

Most existing barrier Lyapunov function (BLF)-based control schemes are only able to handle box-type constraints. However, many physical constraints are ellipsoidal constraints in real-world applications. Therefore, an asymptotic tracking control scheme embedded with an improved command filter is proposed for MIMO nonlinear systems under irregular ellipsoidal output constraints. A novel transformation function, explicitly depending on original constraints, is constructed. With such a design, not only ellipsoidal constraints but also partial ellipsoidal constraints, box-type constraints, and their combination-type constraints can be handled. Moreover, an innovative adaptive nonlinear filter is designed to resolve the complexity explosion problem caused by the repeated differentiations of virtual controllers. Different from the existing filters, the boundary layer errors of the proposed adaptive filter are fully compensated. Furthermore, tracking error is proved to be asymptotically converged to zero with the existence of model uncertainties and external disturbances. In addition, all signals within the closed-loop system are rigorously proved to be bounded. A numerical example is presented to verify the effectiveness of the designed control strategy.

In long-horizon tasks, leveraging prior knowledge to streamline task execution is essential. However, navigating complex environments to achieve long-term objectives poses a significant challenge due to the vast exploration space involved. To address this issue, we propose a skill-based hierarchical reinforcement learning (RL) framework, termed LeSkill. This framework utilizes a conditional generative model to pretrain a comprehensive and generalizable skill repository from heterogeneous datasets, facilitating skill inference across diverse contexts. This strategy enhances transferability to novel tasks, thereby minimizing the need for extensive, task-specific training. Subsequently, a concise set of task-specific demonstrations is employed to guide the selection process, allowing the model to efficiently sample relevant skills from the pre-existing skill repository, which effectively reduces the exploration space. This approach accelerates the acquisition of highly effective policies tailored for task completion. Our framework undergoes rigorous evaluation on two challenging long horizon, multistep tasks: a standard task and a distribution mismatch task. The results highlight the framework’s superior performance in mastering intricate tasks and its remarkable generalization capabilities.

Enhancing the dexterity and agility of integrated arm-hand robotic systems to emulate human-like capabilities is a highly desirable yet challenging task that existing control schemes struggle to accomplish. In this article, we introduce a prescribed-time fault-tolerant targeted tracking control method for integrated arm-hand robotic systems with actuator faults. The control strategy is based on dynamics modeling and the inherent properties of the robotic plants. The proposed method guarantees that the tracking error converges to a predetermined accuracy level within a specified timeframe, regardless of the system’s initial condition. Moreover, the closed-loop robotic systems can continue to operate seamlessly in the presence of actuator faults without requiring interference. Simulation experiments conducted on a single-finger robot and an anthropomorphic arm-hand robotic system validate the efficacy of the targeted tracking control algorithm proposed in this study.

Achieving full state regulation within a prescribed-time for uncertain nonlinear systems under any initial condition is rather challenging although highly desirable, whereas most existing prescribed-time control results are literally contingent upon infinite feedback gain at the equilibrium. This article presents a new prescribed-time control design method that is able to ensure prescribed-time stability with bounded feedback gain and bounded control action during the entire process of system operation, elegantly circumventing the infinite feedback gain problem. As a nonscaling-based method with structural adaptation is utilized, the proposed control scheme is able to regulate all the states to zero well before the prescribed-time, yet in the presence of time-varying and mismatched structural uncertainties, substantially reducing the numerical computational complexity induced by scaling-based methods. The theoretical results are supported by two numerical simulations.

This article investigates an adaptive bipartite consensus tracking control algorithm for a class of heterogeneous nonaffine nonlinear multiagent systems (MASs) with prescribed finite-time tracking performance under an unbalanced communication topology. In the case of an unbalanced digraph, a novel locally optimal bipartition strategy is proposed to transform the unbalanced communication topology into a structurally balanced one, thereby enabling the implementation of bipartite consensus tracking control. To achieve the expected tracking performance, the design philosophy focuses on developing a prescribed finite-time performance function (PFTPF), capable of preassigning the convergence time and accuracy precisely beforehand. The explored adaptive control algorithm can ensure that the whole signals concerning the closed-loop MASs remain bounded while the bipartite consensus errors converge to a predetermined range around zero within the prescribed finite time. Ultimately, the simulation results on robotic systems prove the availability of the developed design solution.

Dexterous manipulation of anthropomorphic multifinger robotic hands (MFRHs) is crucial for performing diverse and intricate tasks, where collaboration among the fingers is essential. This article presents a novel neural network-based composite learning strategy tailored for the synchronous control of multiple fingers in anthropomorphic MFRHs subjected to unknown dynamics and disturbances. By leveraging graph theory, the interconnections among fingers are delineated and integrated into the dynamic equations. The modified nonsingular terminal sliding mode (TSM) technique is employed to achieve fixed-time convergence of error variables without triggering singularity. Within the framework of composite learning, a novel computable prediction error is formulated by harnessing online historical data alongside the regression matrix. The combination of prediction errors and the regression matrix is utilized for parameter estimation, which, under a milder interval excitation (IE) condition, facilitates accurate parameter estimation without the requirement for the stringent persistent excitation (PE) condition. The feasibility and effectiveness of the proposed technique are demonstrated through simulation experiments.

In this work, a unified state feedback control scheme is proposed for Multi-Input Multi-Output (MIMO) nonlinear systems subject to irregular output constraints and unknown control directions. In contrast to most existing state-of-the-art works, the constraints considered in this work can not only be asymmetric but can also appear in stages, sometimes being positive or negative. The designed controller can be applied to scenarios with or without constraints, without the need to modify or switch its structure. Moreover, it does not rely on the minimum-maximum values (MMVs) of the constraints, making it easier to implement. In addition, the issue of continuously increasing parameters in the Nussbaum function is addressed by integrating the designed bounding functions with the proposed barrier functions. This integration ensures the effectiveness of our approach regardless of the presence or absence of constraints. The effectiveness and benefits of the proposed method are verified by simulations on a planar two-link robotic manipulator.Note to Practitioners—In the majority of existing works, only scenarios with consistently present or consistently absent constraints on the system output or states are typically considered. As a result, the control methods proposed in those works are limited to either one of these scenarios, but not both. The objective of this paper is to provide a practical control solution that is suitable for both cases without the need for any switching. Moreover, in most existing works, it is necessary to possess the minimum-maximum values (MMVs) of the constraint functions. However, acquiring this information in practice entails a substantial computational burden. In this work, this restriction has been removed, thereby reducing the application conditions and increasing practicality. In addition, this work is applicable even when the control direction is unknown. This approach has been shown to be feasible through preliminary simulation experiments. In future research, the physical reality and its application to the collision avoidance control of multi-agent systems will be tried.

In this paper, a robust adaptive control scheme, capable of guaranteeing unified prescribed performances on the output tracking error and virtual errors, is developed for a class of multiple-input multiple-output (MIMO) strict-feedback nonlinear systems in presence of input quantization, which exhibits some features. Firstly, by constructing a series of function transformations multiple performance behaviors can be ensured under a fixed control framework by properly selecting the performance parameters, without the need for control redesign. Secondly, by constructing a novel performance function for the virtual errors, the demanding constraint on the initial values of virtual errors is completely circumvented. Consequently, there is no need for the tedious offline computations for the initial verification, making the control algorithm more user-friendly in design and implementation. Thirdly, due to the considerations of prescribed performance and quantization simultaneously, some additional product terms and drift terms occur in Lyapunov function differential inequality, further complicating the control design and stability analysis. To address this issue, useful Lemmas introduced, which ensure that under the normally used assumptions the closed-loop system is stable. The numerical simulations show the advantages and effectiveness of the proposed control.

This paper investigates the synchronization control problem for a family of high-order nonlinear multi-agent systems with mismatched and nonparametric uncertainties. Under the directed interconnection topology, a distributed robust control protocol with low complexity is proposed such that the desired performance indexes, such as the fast convergence speed and the small steady-state error, can be pre-specified irrespective of initial conditions, distinguishing itself from the existing related literatures where the prescribed performance is subject to certain initial conditions. Further, no knowledge regarding the bounds of uncertainties and no approximation operators are involved in the control laws. It is shown that all the closed-loop signals are ensured to be globally uniformly bounded. A numerical example is provided to demonstrate the effectiveness of this approach.

This article addresses the synchronization tracking problem for high-order uncertain nonlinear multiagent systems via intermittent feedback under a directed graph. By resorting to a novel storer-based triggering transmission strategy in the state channels, we propose an event-triggered neuroadaptive control method with quantitative state feedback that exhibits several salient features: 1) avoiding continuous control updates by making the parameter estimations updated intermittently at the trigger instants; 2) resulting in lower-frequency triggering transmissions by using one event detector to monitor the triggering condition such that each agent only needs to broadcast information at its own trigger times; and 3) saving communication and computation resources by designing the intermittent updating of neural network weights using a dual-phase technique during the triggering period. Besides, it is shown that the proposed scheme is capable of steering the tracking/disagreement errors into an adjustable neighborhood close to the origin, and the existence of a strictly positive dwell time is proved to circumvent Zeno behavior. Both theoretical analysis and numerical simulation authenticate and validate the efficiency of the proposed protocols.

In this article, we present an echo state network (ESN)-based tracking control approach for a class of affine nonlinear systems. Different from the most existing neural-network (NN)-based control methods that are focused on the feedforward NN, the proposed method adopts a bioinspired recurrent NN fusing with multiple cluster and intrinsic plasticity (IP) to deal with modeling uncertainties and coupling nonlinearities in the systems. The key features of this work can be summarized as follows: 1) the proposed control is built upon the ESN embedded with multiclustered reservoir inspired from the hierarchically clustered organizations of cortical connections in mammalian brains; 2) the developed neuroadaptive control scheme utilizes unsupervised learning rules inspired from the neural plasticity mechanism of the individual neuron in nervous systems, called IP; 3) a multiclustered reservoir with IP is integrated into the algorithm to enhance the approximation performance of NN; and 4) the multiclustered reservoir is constructed offline and is task-independent, rendering the proposed method less expensive in computation. The effectiveness of the method is also confirmed by comparison with the existing neuroadaptive methods via numerical simulations, demonstrating that better tracking precision is achieved by the proposed method.

This article presents a global performance guaranteed tracking control method for a class of general strict-feedback multi-input and multi-output (MIMO) nonlinear systems with unknown nonlinearities and unknown time-varying input delays. By introducing a novel error transformation embedded with the Lyapunov-Krasovskii functional (LKF), the developed control scheme exhibits several appealing features: 1) it is able to achieve global prescribed performance tracking for uncertain MIMO systems with delayed inputs, while at the same time eliminating the constraint conditions imposing on initial values between the tracking/virtual error and the performance function; 2) there is no need for any a priori knowledge regarding the nonlinearities of the system nor a prior knowledge of time derivatives of the desired trajectory, making the resultant controller simpler in structure and less expensive in computation; 3) the control scheme includes a new differentiable time-varying feedback term, which gracefully compensates the unknown input delays and unknown control gain coefficient matrices; and 4) the controllability condition is relaxed, which enlarges the applicability of the proposed strategy. Finally, a two-link robotic manipulator example is provided to demonstrate the reliability of the theoretical results.

Quantized signal-driven control for nonlinear systems is of special interest in practice. However, it is nontrivial in the presence of mismatched uncertainties and intermittent denial of service (DoS) attacks. The underlying problem becomes even more complicated when both the input and output signals are attacked, rendering the state variables and the input signal inaccessible or unavailable for the control design. Only the quantized (and thus nondifferentiable) output signal is available in the absence of attack, making regular backstepping design inapplicable. This article introduces a novel adaptive output feedback control method to tackle the aforementioned challenges. First, we design a gain-switched quantized observer to estimate the unmeasurable state variables. Second, by employing a first-order dynamic filtering technique, we circumvent the nondifferentiability issue of virtual controller arising from the signal quantization. Third, we establish design conditions for the controller parameters. Fourth, we utilize a more comprehensive sector quantizer and develop adaptive estimators to deal with the unknown quantization parameters. Finally, we demonstrate that with the proposed control method, all the closed-loop signals are semiglobally uniformly ultimately bounded (SUUB), and the regulation error can be made small enough by appropriately tuning the design parameters. Numerical simulations confirm the efficacy of the proposed approach.

It is technically challenging to maintain stable tracking for multiple-input-multiple-output (MIMO) nonlinear systems with modeling uncertainties and actuation faults. The underlying problem becomes even more difficult if zero tracking error with guaranteed performance is pursued. In this work, by integrating filtered variables into the design process, we develop a neuroadaptive proportional-integral (PI) control with the following salient features: 1) the resultant control scheme is of the simple PI structure with analytical algorithms for auto-tuning its PI gains; 2) under a less conservative controllability condition, the proposed control is able to achieve asymptotic tracking with adjustable rate of convergence and bounded performance index collectively; 3) with simple modification, the strategy is applicable to square or nonsquare affine and nonaffine MIMO systems in the presence of unknown and time-varying control gain matrix; and 4) the proposed control is robust against nonvanishing uncertainties/disturbances, adaptive to unknown parameters and tolerant to actuation faults, with only one online updating parameter. The benefits and feasibility of the proposed control method are also confirmed by simulations.

This article proposes a nonaugmented method for investigating the minimal observability problem of Boolean networks (BNs). This method can be applied to more general BNs and reduce the computational and space complexity of existing results. First, unobservable states concerning an unobservable BN are classified into three categories using the vertex-colored state transition graph, each accompanied by a necessary and sufficient condition for determining additional measurements to make them distinguishable. Then, an algorithm is designed to identify the additional measurements that would render an unobservable BN observable using the conditions. Next, to determine the minimum added measurements, a necessary and sufficient condition and an algorithm based on a constructed matrix are presented. Finally, the results obtained are compared with existing literature and illustrated with examples.

In this article, we investigate the distributed tracking control problem for networked uncertain nonlinear strict-feedback systems with unknown time-varying gains under a directed interaction topology. A dual phase performance-guaranteed approach is established. In the first phase, a fully distributed robust filter is constructed for each agent to estimate the desired trajectory with prescribed performance such that the control directions of all agents are allowed to be nonidentical. In the second phase, by establishing a novel lemma regarding Nussbaum function, a new adaptive control protocol is developed for each agent based on backstepping technique, which not only steers the output to track the corresponding estimated signal asymptotically with arbitrarily prescribed transient response but also extends the application scope of the proposed control scheme largely since the unknown control gains are allowed to be time-varying and even state-dependent. In such a way, the underlying problem is tackled with the output tracking error converging into an arbitrarily preassigned residual set exhibiting an arbitrarily predefined convergence rate. Besides, all the internal signals are ensured to be semi-globally ultimately uniformly bounded (SGUUB). Finally, two examples are provided to illustrate the effectiveness of the co-designed scheme.

This article presents a novel dual-phase based approach for distributed event-triggered control of uncertain Euler-Lagrange (EL) multiagent systems (MASs) with guaranteed performance under a directed topology. First, a fully distributed robust filter is designed to estimate the reference signal for each agent with guaranteed observation performance under continuous state feedback, which transforms the distributed event-triggered control problem into a centralized one for multiple single systems. Second, an event-triggered controller is constructed via intermittent state feedback, making the output of each agent follow the corresponding estimated signal with guaranteed tracking performance. The proposed co-design scheme is of relatively low complexity in structure and cheap in computation since a priori knowledge of system nonlinearities or estimation of their bounds is not required in building the control scheme, and yet neither approximating structures nor adaptive online updating algorithms are needed. It is shown that the output tracking error of each agent is ensured to shrink into a prescribed precision set at an arbitrarily assignable convergence rate, although the plant states and the actuation signal are triggered simultaneously. All the internal signals are uniformly bounded and the occurrence of Zeno behavior is precluded. The efficiency of the proposed method is verified via numerical simulation.

It is an interesting open problem to enable robots to efficiently and effectively learn long-horizon manipulation skills. Motivated to augment robot learning via more effective exploration, this work develops task-driven reinforcement learning with action primitives (TRAPs), a new manipulation skill learning framework that augments standard reinforcement learning algorithms with formal methods and parameterized action space (PAS). In particular, TRAPs uses linear temporal logic (LTL) to specify complex manipulation skills. LTL progression, a semantics-preserving rewriting operation, is then used to decompose the training task at an abstract level, informs the robot about their current task progress, and guides them via reward functions. The PAS, a predefined library of heterogeneous action primitives, further improves the efficiency of robot exploration. We highlight that TRAPs augments the learning of manipulation skills in both learning efficiency and effectiveness (i.e., task constraints). Extensive empirical studies demonstrate that TRAPs outperforms most existing methods.

This article investigates the dynamical analysis, circuit design, and fuzzy prescribed performance backstepping control of fractional-order (FO) weakly coupled micro-electro-mechanical system (MEMS) resonators with an event-triggered input. The math model of such MEMS resonators coupled by bridge-type coupling beam is constructed based on the Lagrange motion equation and Caputo definition. The dynamical analysis reveals evolution rules and a tendency of system dynamics involved periodic/multiperiodic state and chaotic oscillation for the coupling stiffness, alternating voltage and FO. The designed FO analog circuit and digital circuit based on the field-programmable gate array further validate the mentioned dynamics and are convenient for latter engineering development. To realize stabilization control purposes like chaos suppression, accelerated convergence, high accuracy tracking, performance constraint, and communication resource saving, a fuzzy prescribed performance backstepping controller, in which theβ-cut type-2 fuzzy logic system is used to deal with unknown system function, a transfer function with prescribed performance function is proposed to guarantee the constraint boundary of the tracking error, an accelerated tracking differentiator with a speed function is established to increase convergence rate and solve the “complexity explosion,” an event trigger mechanism is provided to ease the communication burden and a continuous frequency distributed model is utilized to reflect the essence of infinite dimension of such FO system, is constructed in the framework of a backstepping. The proposed scheme not only guarantees that all system signals in closed-loop system are bounded, but also achieves the mentioned stabilization control purposes. Finally, abundant simulation results validate the feasibility of the presented scheme.

Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning methods based on deep learning have been reported in the literature. However, current methods ignore the deep temporal features of fMRI data and fail to fully employ the spatial topological relationship between brain regions. In this article, we propose a novel method for learning brain ECN based on spatiotemporal graph convolutional models (STGCM), named STGCMEC, in which we first adopt the temporal convolutional network to extract the deep temporal features of fMRI data and utilize the graph convolutional network to update the spatial features of each brain region by aggregating information from neighborhoods, which makes the features of brain regions more discriminative. Then, based on such features of brain regions, we design a joint loss function to guide STGCMEC to learn the brain ECN, which includes a task prediction loss and a graph regularization loss. The experimental results on a simulated dataset and a real Alzheimer's disease neuroimaging initiative (ADNI) dataset show that the proposed STGCMEC is able to better learn brain ECN compared with some state-of-the-art methods.

This article addresses the global prescribed performance tracking control problem for uncertain nonlinear systems with delayed input and unmatched nonlinearity. By introducing a new error transformation consisting of a normalized function and a time-varying scaling function, together with an auxiliary function, we establish a robust state-feedback control scheme capable of handling the unknown and time-varying input delays without imposing any constraints on the initial condition between tracking/virtual error and performance function, leading to a global control solution to the challenging problem. Besides, the unknown delayed input is handled skillfully by the union of Lyapunov–Krasovskii functional and proof by contradiction at the final step. Furthermore, the designed controller is simple in structure and inexpensive in calculation since it does not involve any approximation tools/estimation techniques and avoids iterative computation in backstepping-like methods. It is shown that all internal signals are globally ultimately uniformly bounded, and the tracking/virtual error converges to a preassigned arbitrarily small region at a certain predefined rate. Both theoretical analysis and numerical example confirm the validity of the developed method.

It is highly desirable yet challenging to maintain stable system operation in the presence of unexpected malfunctioning sensors and actuators arising from internal component faults and/or external malicious attacks. In this note, we investigate the reliable control problem for a class of nonlinear systems with mismatched modeling uncertainties and unknown control gain matrix as well as abnormal actuating and sensoring units. Such malfunctioning sensors and actuators not only bring about additional modeling uncertainties but also literally pollute the original control influence gain matrix, making the underlying problem further complicated. By using the backstepping-like design procedure, we present an adaptive control solution with relaxed controllability conditions, capable of achieving asymptotic stabilization under severely twisted feedback information due to malicious attacks/sensor failures. Besides a primary actuator, we also propose a strategy to use additional actuators as the backup ones to enhance system survivability, where the actuator replacement automatically and seamlessly takes place from the primary actuator to a backup one, once a severe failure at the primary actuator is detected. Numerical simulation also confirms the effectiveness and benefits of the proposed method.

The particular interest of this article is mainly focusing on the prescribed performance control (PPC) problem for a class of single-input single-output (SISO) nonlinear systems subject to serious mismatched uncertainties and unknown time-varying control coefficients. In contrast to most existing PPC works where the control coefficients are known or unknown while constant, the proposed control method allows multiple unknown and time-varying coefficients with nonidentical directions to be dealt with. The technical difficulty in the stability analysis arising from the multiple unknown and nonidentical directions is circumvented by proving that the effects of multiple Nussbaum-type gains in a single Lyapunov inequality can be quantified according to a newly established lemma (Lemma 2) regarding the enhanced Nussbaum functions. In addition, by resorting to a novel prescribed-time scaling function and an error transformation, it is proved that the current control method guarantees global prescribed performance in the sense that the output tracks the reference trajectory asymptotically with the tracking error converging into an arbitrarily predefined residual set at any rate of convergence within preset finite time irrespective of initial conditions and any design parameters, distinguishing itself from those semi-global results dependent on the initial conditions. The validity of the designed control scheme is confirmed by both theoretical analysis and numerical simulation.

Most existing results on full-state error prescribed performance control for multiple-input multiple-output (MIMO) strict-feedback nonlinear systems typically impose demanding constraining conditions on the initial full-state errors, rendering the performance boundary nonuniform with respective to initial conditions, and consequently tedious offline computations for initial error (especially the initial virtual error) constraint verification is inevitable, which is highly undesirable or even impractical for the design and implementation of the corresponding controls. In this article, we present a novel adaptive control solution that allows the performance uniformness (with respect to the initial condition) and the transient behavior (with respect to error overshoot) to be addressed simultaneously under a unified framework. The key design steps and features include: 1) by constructing a nonlinear transformation based on the time-varying scaling function, the developed performance boundary is uniform to any initial condition; 2) the demanding condition on the initial values of full-state errors in the existing prescribed performance works is removed, allowing the designer more freedom to select design parameters and rendering the solution more user-friendly and less demanding in design and implementation; and 3) by making use of the minimum eigenvalues of the resultant diagonal matrix and imposing a critical negative feedback term in the control design, the stability of closed-loop system is ensured by the developed uniform control strategy. The effectiveness of the proposed approach is verified by simulations.

This article mainly investigates the model design, field programmable gate array (FPGA) implementation, and prescribed-time fault-tolerant control of a fractional-order (FO) decoupled dual-mass micro-electro-mechanical system (MEMS) gyro with deferred constraints. First, the structure of such MEMS gyro is designed to eliminate the linear acceleration in the sensing direction and its mathematical model is built based on the Lagrange’s equation. The dynamical analysis shows that such gyro can generate unpredictable, random, and disorder motions under various FOs, stiffness cross the coupling coefficients and proof masses. The designed FPGA circuit further demonstrates the undesirable chaotic oscillations of such MEMS gyro and good hardware resources utilization, avoiding the time consuming and board redesign. Second, to better solve the problems of constraints, actuator faults, uncertainties, drive couplings, and chaotic oscillations, a dependent deferred-error function superimposed to a prescribed-time function is used to guarantee no violation of constraints after a finite time. Furthermore, a$\beta $-cut type-2 fuzzy logic system (T2FLS) is employed to solve the uncertainty, and an FO hyperbolic tangent tracking differentiator (HTTD) is utilized to deal with the direct FO derivative and repeated derivative in the framework of the backstepping control. Then, a prescribed-time fault-tolerant control scheme of the FO decoupled dual-mass MEMS gyro is proposed under the actuator fault. Finally, the abundant simulation experimental results verify the feasibility and effectiveness of our scheme.

In this article, we present a global output tracking control method for nonlinear strict-feedback systems under deferred asymmetric time-varying constraints and various initial conditions. Different from most existing results that require initial satisfaction of the constraining conditions, the proposed solution makes use of the proof-by-contradiction method, which, with the aid of the tunable normalized tracking signal and two novel error transformation functions, enables the control scheme with several salient features: 1) the deferred constraint (time-varying and asymmetric) is directly addressed, while completely avoiding the explosion of complexity arising from repeatedly/recursively differentiating virtual controllers as typically involved in the backstepping control of nonlinear strict-feedback systems; 2) stable output tracking with tunable transient behavior and prescribed steady-state performance is ensured under any unknown initial conditions without the need for human intervention; 3) there is no need for neural network (NN)-based approximators, command filters or auxiliary dynamic surface control (DSC)-based design and analysis, thus substantially reducing the control synthesis complexity; and 4) neither a priori knowledge of system nonlinearities nor estimation of their bounds is required. Two numerical examples are provided to verify the benefits and efficiency of the proposed method.

This article develops an intermittent feedback optimal control scheme for nonlinear systems with asymmetric input saturation using a dynamic event-triggering mechanism. First, an infinite horizon nonquadratic value function with a novel integrand is formulated for the studied system to evaluate the performance, tackle the asymmetric input saturation, and remove certain rigorous assumptions in prior related studies. Second, a critic neural network (CNN) in the adaptive dynamic programming framework is constructed to obtain the optimal event-triggered control (ETC). An improved concurrent learning technique is then developed to update the CNN’s weights without requiring the persistence of excitation condition. Compared with the static ETC scheme, the present dynamic ETC strategy consumes fewer computational resources. Third, the uniform ultimate boundedness of the state, the weight estimation error, and the internal dynamic variable are assured, and the Zeno behavior is excluded. Finally, a rotational-translational actuator system is given to validate the developed intermittent feedback optimal control scheme.

For systems with soft state constraints, initial violation in such constraints is acceptable if no feasible control strategy capable of maintaining such constraints exists or an excessively large amount of energy consumption is required for constraint satisfaction. However, whenever the constraint violation is detected, it is highly desirable that the states be regulated back to satisfy the constraints as soon as possible. In this paper, an approximation-free state feedback control scheme is proposed for unknown second-order nonlinear systems, capable of recovering the states from constraint violation to constraint satisfaction within the prescribed time. Besides, the implicit assumption of compatibility between different constraints in most existing works is removed, and a sufficient condition for compatibility of state constraints is established for unknown second-order systems. The proposed self-recovery control scheme has the following features: 1) the recovery time of the states can be preset by the user regardless of the initial conditions; 2) the proposed controller does not need any explicit information on system model, thus has strong robustness against model uncertainties and disturbances; and 3) no approximation technique (e.g., neural network and fuzzy system) is involved, rendering the proposed control structurally simple and computationally inexpensive.

This article investigates the tracking control problem for a class of self-restructuring systems. Different from existing studies on systems with fixed structure, this work focuses on systems with varying structures, arising from, for instance, biological self-developing, unconsciously switching, or unexpected subsystem failure. As the resultant dynamic model is complicated and uncertain, any model-based control is too costly and seldom practical. Here, we explore a nonmodel-based low-complexity proportional-integral-derivative (PID) control. Unlike traditional PID with fixed gains, the proposed one is embedded with neural-network (NN)-based self-tuning adaptive gains, where the tuning strategy is analytically built upon system stability and performance specifications, such that transient behavior and steady-state performance are ensured. Both square and nonsquare systems are addressed by using the matrix decomposition technique. The benefits and feasibility of the proposed control method are also validated and confirmed by the simulations.

In this article, we investigate the prescribed performance tracking control problem for high-order nonlinear multiagent systems (MASs) under directed communication topology and unknown control directions. Different from most existing prescribed performance consensus control methods where certain initial conditions are needed to be satisfied, here the restriction related to the initial conditions is removed and global tracking result irrespective of initial condition is established. Furthermore, output consensus tracking is achieved asymptotically with arbitrarily prescribed transient performance in spite of the directed topology and unknown control directions. Our development benefits from the performance function and prescribed-time observer. Both theoretical analysis and numerical simulation confirm the validity of the developed control scheme.

This article investigates the neuroadaptive optimal fixed-time synchronization and its circuit realization along with dynamical analysis for unidirectionally coupled fractional-order (FO) self-sustained electromechanical seismograph systems under subharmonic and superharmonic oscillations. The synchronization model of the coupled FO seismograph system is established based on drive and response seismic detectors. The dynamical analysis reveals this coupled system generating transient chaos and homoclinic/heteroclinic oscillations. The test results of the constructed equivalent analog circuit further testify its complex nonlinear dynamics. Then, a neuroadaptive optimal fixed-time synchronization controller integrated with the FO hyperbolic tangent tracking differentiator (HTTD), interval type-2 fuzzy neural network (IT2FNN) with transformation, and prescribed performance function (PPF) together with the constraint condition is developed in the backstepping recursive design. Furthermore, it is proved that all signals of this closed-loop system are bounded, and the tracking errors fall into a trap of the prescribed constraint along with the minimized cost function. Extensive studies confirm the effectiveness of the proposed scheme.

This work presents a neuroadaptive tracking control scheme embedded with memory-based trajectory predictor for Euler–Lagrange (EL) systems to closely track an unknown target. The key synthesis steps are: 1) using memory-based method to reconstruct the behavior of the unknown target based on its past trajectory information recorded/stored in the memory; 2) blending both speed transformation and barrier Lyapunov function (BLF) into the design and analysis; and 3) introducing a virtual parameter to reduce the number of online update parameters, rendering the strategy structurally simple and computationally inexpensive. It is shown that the resultant control scheme is able to ensure prescribed tracking performance in which close target tracking is achieved without the need for detailed information about system dynamics and the target trajectory; the tracking error converges to the prescribed precision set within a prespecified finite time at an assignable rate of convergence; and the full-state constraints are never violated. Furthermore, all the signals in the closed-loop system are bounded and the control action is$C^{1}$smooth. The benefits and feasibility of the developed control are also verified and confirmed by simulation.

This article addresses the practical prescribed-time leaderless consensus problem for multiple networked strict-feedback systems under directed topology. Different from most existing protocols for finite-time consensus that rely on the signum function or fractional power state feedback (thus, the finite convergence time is contingent upon the initial positions of the agents or other design parameters), the proposed distributed neuroadaptive consensus solution is based on a two-phase performance adjustment approach, which exhibits several salient features: 1) the consensus error is ensured to converge to a preassigned arbitrarily small residual set within prescribed time; 2) the tunable transient behavior and desired steady-state control performance of the consensus error is maintained under any unknown initial conditions; and 3) the control scheme involves only one parameter estimation, significantly reducing the design complexity and online computation. Furthermore, we extend the result to practical prescribed-time leader-following consensus control under directed communication topology. Numerical simulation verifies the benefits and efficiency of the proposed method.

This article presents a novel adaptive bipartite consensus tracking strategy for multiagent systems (MASs) under sensor deception attacks. The fundamental design philosophy is to develop a hierarchical algorithm based on shortest route technology that recasts the bipartite consensus tracking problem for MASs into the tracking problem for a single agent and eliminates the need for any global information of the Laplacian matrix. As the sensors suffer from malicious deception attacks, the states cannot be measured accurately, we thus construct a novel dynamic estimator to estimate the actual states, which, together with a new coordinate transformation involving the attacked and estimated state variables, allows a distributed security control scheme to be developed, in which the singularity of the adaptive iterative process involved in existing works is completely avoided. Furthermore, the Nussbaum functions are included in the controller to account for the influence of the unknown control gains caused by sensor deception attacks. It is shown that the distributed consensus tracking errors converge to a small neighborhood of the origin, and all the signals in the closed-loop system remain bounded. Simulation on a forced damped pendulums (FDPs) is conducted to demonstrate and verify the effectiveness of the proposed strategy.

This article investigates the problem of prescribed-time tracking control for a class of self-switching systems subject to nonvanishing/nonparametric uncertainties and unknown control directions. Due to the existence of the unknown inherent nonlinear dynamics and the undetectable actuation faults, the resultant control gain of the system becomes unknown and time varying, making the control impact on the system uncertain and the prescribed-time control synthesis nontrivial. The underlying problem becomes further complex as the switching is arbitrary and unknown. To circumvent the aforementioned difficulties, the following major steps are employed. First, by integrating a novel time-varying feedback gain and performance function into the control synthesis, the nonvanishing uncertainties are completely rejected and the transient performance is guaranteed. Second, to facilitate the stability analysis under arbitrarily switching, the concept of the constraining function is introduced and incorporated into a skillfully chosen common Lyapunov function. Third, to deal with the uncertain control gain, a new Nussbaum-related lemma is derived. The proposed control is shown to be capable of ensuring that the tracking error not only evolves within the prescribed bound during all the operation time but also converges to zero at the rate of convergence that can be preassigned as fast as desired, in the presence of self-switching dynamics and unknown control directions. Both theoretical analysis and numerical simulation confirm the effectiveness of the proposed method.

It is nontrivial to achieve exponential stability even for time-invariant nonlinear systems with matched uncertainties and persistent excitation (PE) condition. In this article, without the need for PE condition, we address the problem of global exponential stabilization of strict-feedback systems with mismatched uncertainties and unknown yet time-varying control gains. The resultant control, embedded with time-varying feedback gains, is capable of ensuring global exponential stability of parametric-strict-feedback systems in the absence of persistence of excitation. By using the enhanced Nussbaum function, the previous results are extended to more general nonlinear systems where the sign and magnitude of the time-varying control gain are unknown. In particular, the argument of the Nussbaum function is guaranteed to be always positive with the aid of nonlinear damping design, which is critical to perform a straightforward technical analysis of the boundedness of the Nussbaum function. Finally, the global exponential stability of parameter-varying strict-feedback systems, the boundedness of the control input and the update rate, and the asymptotic constancy of the parameter estimate are established. Numerical simulations are carried out to verify the effectiveness and benefits of the proposed methods.

This work is concerned with the prescribed performance tracking control for a family of nonlinear nontriangular structure systems under uncertain initial conditions and partial measurable states. By combining neural network and variable separation technique, a state observer with a simple structure is constructed for output-based finite-time tracking control, wherein the issue of algebraic loop arising from a nontriangular structure is circumvented. Meanwhile, by using an error transformation, the developed control scheme is able to ensure tracking with a prescribed accuracy within a pregiven time at a preassigned convergence rate under any bounded initial condition, eliminating the long-standing initial condition dependence issue inherited with conventional prescribed performance control methods, and guaranteeing the predeterminability of convergence time simultaneously. Two simulation examples also demonstrate the effectiveness of the presented control strategy.

This article focuses on dynamic analysis and the fuzzy fixed-time optimal synchronization control problem of unidirectionally coupled fractional-order (FO) permanent magnet synchronous generator (PMSG) system. The synchronization model between FO master and slave PMSGs with capacitive and resistive couplings is built. The dynamic analysis fully reveals its abundant dynamical behaviors including chaotic oscillations and gives stability/instability boundaries with the designed numerical method. In controller design, the hierarchical type-2 fuzzy neural network (HT2FNN) with a transformation is designed to approximate unknown functions, the fixed-time command filter matched up with the compensating signal is proposed to achieve precise estimate and fast convergence, and a fixed-time preconfigured performance function integrated with a smooth and invertible function is built to realize fixed-time convergence and performance constraint. Then a fuzzy fixed-time optimal synchronization control scheme fusing with the HT2FNN, filter, performance function and optimal control is developed under the FO backstepping theory. The stability analysis proves that all signals of the closed-loop system are bounded along with the cost function being minimized. Finally, numerical simulation results verify the feasibility and advantages of our scheme.

Communication and computation resources are normally limited in remote/networked control systems, and thus, saving either of them could substantially contribute to cost reduction and life-span increasing as well as reliability enhancement for such systems. This article investigates the event-triggered control method to save both communication and computation resources for a class of uncertain nonlinear systems in the presence of actuator failures and full-state constraints. By introducing the triggering mechanisms for actuation updating and parameter adaptation, and with the aid of the unified constraining functions, a neuroadaptive and fault-tolerant event-triggered control scheme is developed with several salient features: 1) online computation and communication resources are substantially reduced due to the utilization of unsynchronized (uncorrelated) event-triggering pace for control updating and parameter adaptation; 2) systems with and without constraints can be addressed uniformly without involving feasibility conditions on virtual controllers; and 3) the output tracking error converges to a prescribed precision region in the presence of actuation faults and state constraints. Both theoretical analysis and numerical simulation verify the benefits and efficiency of the proposed method.

Human brain effective connectivity characterizes the causal effects of neural activities among different brain regions. Studies of brain effective connectivity networks (ECNs) for different populations contribute significantly to the understanding of the pathological mechanism associated with neuropsychiatric diseases and facilitate finding new brain network imaging markers for the early diagnosis and evaluation for the treatment of cerebral diseases. A deeper understanding of brain ECNs also greatly promotes brain-inspired artificial intelligence (AI) research in the context of brain-like neural networks and machine learning. Thus, how to picture and grasp deeper features of brain ECNs from functional magnetic resonance imaging (fMRI) data is currently an important and active research area of the human brain connectome. In this survey, we first show some typical applications and analyze existing challenging problems in learning brain ECNs from fMRI data. Second, we give a taxonomy of ECN learning methods from the perspective of computational science and describe some representative methods in each category. Third, we summarize commonly used evaluation metrics and conduct a performance comparison of several typical algorithms both on simulated and real datasets. Finally, we present the prospects and references for researchers engaged in learning ECNs.

Although quite natural for human beings to communicate based on their own personality in daily life, it is rather challenging for neural dialog systems to do the same. This is because the general dialog systems are difficult to generate diverse responses while at the same time maintaining consistent persona information. Existing methods basically focus on merely one of them, ignoring either of them will reduce the quality of dialog. In this work, we propose a two-stage generation framework to promote the persona-consistency and diversity of responses. In the first stage, we propose a persona-guided conditional variational autoencoder (persona-guided CVAE) to generate diverse responses, and the main difference when compared with general CVAE-based model is that we use additional dialog attribute to assist the latent variables to encode the effective information in the response and further use it as a guiding vector for response generation. In the second stage, we employ persona-consistency checking module and the response rewriting module to mask the inconsistent word in the generated response prototype and rewrite it to more consistent. Automatic evaluation results demonstrate that the proposed model is able to generate diverse and persona-consistent responses.

I would like to take this opportunity to sincerely wish you and your loved ones all the best for the holidays and a very happy, healthy, and prosperous New Year of 2023! It has been my greatest honor and privilege to have this opportunity in serving this role. Thank you so much for your trust, support, and encouragement.

This work focuses on the issue of event-triggered practical prescribed time tracking control for a type of uncertain nonlinear systems subject to actuator saturation and unmeasurable states as well as time-varying unknown control coefficients. First, a state observer with simple structure is constructed by means of neural network technology to estimate the unmeasurable system states under time-varying control coefficients. Then, with the help of one-to-one nonlinear mapping of the tracking error, an event-triggered output feedback control scheme is developed to steer the tracking error into a residual set of predefined accuracy within a preassigned settling time. Unlike existing related control methods, there is no need to involve finite-time state observer or fractional power feedback of system states, and thus, the control solution presented here is less complex and more acceptable. The key technique in control design lies in the establishment of an alternative first-order auxiliary system for dealing with the impact arisen from the input saturation. In our proposed approach, a new bounded function related to auxiliary variable and new dynamics of the auxiliary system are skillfully utilized such that the upper bound of the difference between actual input and designed input signal is not involved in implementation of the controller.

It is still an open problem to achieve asymptotic tracking meanwhile maintaining specific performance for nonlinear systems with structurally mismatched uncertainties and strictly constrained inputs. In this work, we present a solution to this problem by using neural network (NN)-based adaptive control embedded with the robust integral of the sign of the error (RISE) technique. Most existing prescribed performance control (PPC) can only ensure uniformly ultimately bounded stability, and the RISE-based control, although capable of achieving asymptotic stability, does not guarantee transient behavior (especially, when the system is in strict-feedback form with saturated input). Here, in this study, we make use of NNs to accommodate the unknown nonlinearities, where the NN approximation error, together with other uncertainties, is fully compensated by using a RISE unit. The constraints imposed on the inputs are addressed by the hyperbolic tangent function, resulting in a solution capable of guaranteeing asymptotic tracking with prescribed transient performance, in the presence of mismatched modeling uncertainties and actuation saturation. A numerical simulation is carried out to verify the effectiveness of the proposed method.

Prescribed-time (PT) control for nonlinear systems, originated from Song et al., has gained increasing attention among the control community. The salient feature of PT control lies in its ability to achieve system stability within a finite settling time user-assignable in advance irrespective of initial conditions. It is such a unique feature that has enticed many follow-up studies on this technically important area, motivating numerous research advancements. In this article, we provide a comprehensive survey on the recent developments in PT control. Through a concise introduction to the concept of PT control, and a unique taxonomy covering: 1) from robust PT control to adaptive PT control; 2) from PT control for single-input–single-output (SISO) systems to multi-input–multioutput (MIMO) systems; and 3) from PT control for an isolated system to multiagent systems, we present an accessible review of this interesting topic. We highlight key techniques, and fundamental assumptions adopted in various developments as well as some new design ideas. We also discuss several possible future research directions toward PT control.

For systems in canonical form with nonvanishing uncertainties/disturbances, this work presents an approach to full-state regulation within prescribed time irrespective of initial conditions. By introducing the smooth hyperbolic-tangent-like function, a nonlinear and time-varying state-feedback control scheme is constructed, which is further extended to address output-feedback-based prescribed-time regulation by invoking the prescribed-time observer, all are applicable over the entire operational time zone. As an alternative to full-state regulation within the user-assignable time interval, the proposed method analytically bridges the divide between linear and nonlinear feedback-based prescribed-time control and is able to achieve asymptotic stability, exponential stability, and prescribed-time stability with a unified control structure.

Many important engineering applications involve control design for Euler-Lagrange (EL) systems. In this article, the practical prescribed time tracking control problem of EL systems is investigated under partial or full state constraints. A settling time regulator is introduced to construct a novel performance function, with which a new neural adaptive control scheme is developed to achieve pregiven tracking precision within the prescribed time. With the specific system transformation techniques, the problem of state constraints is transformed into the boundedness of new variables. The salient feature of the proposed control methods lies in the fact that not only the settling time and tracking precision are at the user's disposal but also both partial state and full state constraints can be accommodated concurrently without the need for changing the control structure. The effectiveness of this approach is further verified by the simulation results.

This article solves the problem of optimal synchronization, which is important but challenging for coupled fractional-order (FO) chaotic electromechanical devices composed of mechanical and electrical oscillators and electromagnetic filed by using a hierarchical neural network structure. The synchronization model of the FO electromechanical devices with capacitive and resistive couplings is built, and the phase diagrams reveal that the dynamic properties are closely related to sets of physical parameters, coupling coefficients, and FOs. To force the slave system to move from its original orbits to the orbits of the master system, an optimal synchronization policy, which includes an adaptive neural feedforward policy and an optimal neural feedback policy, is proposed. The feedforward controller is developed in the framework of FO backstepping integrated with the hierarchical neural network to estimate unknown functions of dynamic system in which the mentioned network has the formula transformation and hierarchical form to reduce the numbers of weights and membership functions. Also, an adaptive dynamic programming (ADP) policy is proposed to address the zero-sum differential game issue in the optimal neural feedback controller in which the hierarchical neural network is designed to yield solutions of the constrained Hamilton-Jacobi-Isaacs (HJI) equation online. The presented scheme not only ensures uniform ultimate boundedness of closed-loop coupled FO chaotic electromechanical devices and realizes optimal synchronization but also achieves a minimum value of cost function. Simulation results further show the validity of the presented scheme.

Existing schemes for systems with state constraints require the bounds of the constraints for controller design and may result in conservativeness or even become invalid when they are applied to systems without such constraints. In this paper, we study the problem of event-triggered control for a class of uncertain nonlinear systems by considering the cases with or without state constraints in a unified manner. By introducing a new universal-constrained function and using certain transformation techniques, the original-constrained system is converted into an equivalent totally unconstrained one. Then, an event-triggered adaptive neural-network (NN) controller is designed to stabilize the unconstrained system and compensate for the control sampling errors caused by event-triggered transmission of control signals. Unlike some existing control schemes developed for systems with state constraints, which need to check whether each virtual control meets certain feasibility conditions at every design step, our proposed unified method enables such feasibility conditions to be relaxed. In addition, a suitable event-triggering rule is designed to determine when to transmit control signals. It is theoretically shown that the designed controller can achieve the desired tracking ability and reduce the communication burden from the controller to the actuator at the same time. Simulation verification also confirms the effectiveness of the proposed approach.

This article proposes a neural networks (NNs)-based tracking control approach for a class of uncertain high-order self-restructuring nonaffine dynamic systems. Unlike most existing NN-based works that normally ignore the precondition on the functionality and reliability of NN unit and thus can only ensure semiglobal stability, the proposed method explicitly addresses the issue of reliable in-loop operation of NN approximation-based control unit, resulting in a safeguarded NN-based control solution capable of ensuring globally stable tracking. Furthermore, the control method proposed guarantees exponentially globally stable tracking for systems with self-restructuring nonlinearities and uncertainties, distinguishing itself from those that only yield uniformly ultimately bounded (UUB) regulation/tracking results for nonlinear systems with fixed structures. All of these features are achieved by the proposed strategy consisting of two cooperative control units: 1) safeguard control and 2) NN-based control. The role of the safeguard control is to force the states (starting from any initial condition) to enter a stable region, so that the NN-based control can be activated trustworthily and safely. It is such cooperation of the two units that not only ensures the tracking error entering the stable region first within a prespecified finite time but also guarantees the tracking error converging to zero exponentially thereafter, resulting in global zero-error tracking. Both the theoretical analysis and numerical simulation authenticate the effectiveness of the proposed method.

In this paper, we investigate the consensus tracking control problem for networked multiagent systems (MASs) with unknown nonaffine dynamics. Our goal is to achieve asymptotic (rather than ultimately uniformly bounded) consensus tracking, which is quite challenging especially if nonvanishing/nonparametric uncertainties are involved and at the same time the control protocol is required to be fully distributed and continuous everywhere. Here, we present a conceptually new and structurally simple solution with distributed and continuous control action. The developed method is capable of ensuring zero-error tracking with a unique converging feature in that the consensus tracking error first converges to a small adjustable residual set around zero within a prescribed finite time, and then further shrinks to zero exponentially. The key technique lies in the utilization of a state transformation based on certain scaling function. Our method also prevents the restrictive requirement that all subsystems have access to the linearly parameterized information as imposed in most existing consensus tracking results for nonlinear MAS.

In this article, we investigate the issue of the accelerated adaptive fuzzy optimal control of three coupled fractional-order chaotic electromechanical transducers. A small network where every transducer has the nearest-neighbor coupling configuration is used to form the coupled fractional-order chaotic electromechanical transducers. The mathematical model of the coupled electromechanical transducers with nearest-neighbors is established and the dynamical analysis reveals that its behaviors are very sensitive to external excitation and fractional order. In the controller design, the recurrent nonsingleton type-2 sequential fuzzy neural network (RNT2SFNN) with the transformation is designed to estimate unknown functions of dynamics system in the feedforward fuzzy controller, and it is constructed to approximate the critic value and actor control functions by using policy iteration (PI) in the optimal feedback controller. Meanwhile, the speed functions are employed to achieve accelerated convergence within a pregiven finite time and a tracking differentiator is used to solve the explosion of terms associated with traditional backstepping. The whole control strategy consists of a feedforward controller integrating with the RNT2SFNN, tracking differentiator, and speed function in the framework of the backstepping control and a feedback controller fusing with the RNT2SFNN and PI under an actor/critic structure to solve the Hamilton-Jacobi-Bellman equation. The proposed scheme not only guarantees the boundness of all signals and realizes the chaos suppression, synchronization, and accelerated convergence, but also minimizes the cost function. Simulations demonstrate and validate the effectiveness of the proposed scheme.

In this article, we study the performance guaranteed consensus tracking problem for a class of high-order nonlinear multiagent systems subject to mismatched uncertainties and external disturbances. We first construct a finite-time function, with which a performance function is introduced that links the convergence time of the relative consensus errors with the neighbor agents. We then introduce two new lemmas that play a virtual role in addressing the consensus stability of closed-loop multiagent system, where a fully distributed adaptive control without using global information of the topology is developed. Different from most existing works for multiagent systems with prescribed performance that can only achieve uniformly ultimately bounded consensus, the proposed control scheme is able to ensure that the consensus errors converge to the pregiven compact sets within preassigned finite time rather than infinite time and the outputs of all the agents track the leader's trajectory asymptotically. Simulation verification also confirms the effectiveness of the proposed approach.

This brief presents an intrinsic plasticity (IP)-driven neural-network-based tracking control approach for a class of nonlinear uncertain systems. Inspired by the neural plasticity mechanism of individual neuron in nervous systems, a learning rule referred to as IP is employed for adjusting the radial basis functions (RBFs), resulting in a neural network (NN) with both weights and excitability tuning, based on which neuroadaptive tracking control algorithms for multiple-input-multiple-output (MIMO) uncertain systems are derived. Both theoretical analysis and numerical simulation confirm the effectiveness of the proposed method.

With the rapid development from traditional machine learning (ML) to deep learning (DL) and reinforcement learning (RL), dialog system equipped with learning mechanism has become the most effective solution to address human-machine interaction problems. The purpose of this article is to provide a comprehensive survey on learning-based human-machine dialog systems with a focus on the various dialog models. More specifically, we first introduce the fundamental process of establishing a dialog model. Second, we examine the features and classifications of the system dialog model, expound some representative models, and also compare the advantages and disadvantages of different dialog models. Third, we comb the commonly used database and evaluation metrics of the dialog model. Furthermore, the evaluation metrics of these dialog models are analyzed in detail. Finally, we briefly analyze the existing issues and point out the potential future direction on the human-machine dialog systems.

For systems with irregular (asymmetric and positively-negatively alternating) constraints being imposed/removed during system operation, there is no uniformly applicable control method. In this work, a control design framework is established for uncertain pure-feedback systems subject to the aforementioned constraints. By introducing a novel transformation function and with the help of auxiliary constraining boundaries, the original output-constrained system is augmented to unconstrained one. Unknown nonlinearity is approximated by neural networks (NNs) with not only neural weight updating but also activation online adjustment. The resultant control scheme is able to deal with constraints imposed or removed at some time moments during system operation without the need for altering control structure. When applied to high-speed trains, the developed control scheme ensures position tracking under speed constraints, simulation demands, and confirms the effectiveness of the proposed method.

Many manufacturing systems not only involve nonlinearities and nonvanishing disturbances but also are subject to actuation failures and multiple yet possibly conflicting objectives, making the underlying control problem interesting and challenging. In this article, we present a neuroadaptive fault-tolerant control solution capable of addressing those factors concurrently. To cope with the multiple objective constraints, we propose a method to accommodate these multiple objectives in such a way that they are all confined in certain range, distinguishing itself from the traditional method that seeks for a common optimum (which might not even exist due to the complicated and conflicting objective requirement) for all the objective functions. By introducing a novel barrier function, we convert the system under multiple constraints into one without constraints, allowing for the nonconstrained control algorithms to be derived accordingly. The system uncertainties and the unknown actuation failures are dealt with by using the deep-rooted information-based method. Furthermore, by utilizing a transformed signal as the initial filter input, we integrate dynamic surface control (DSC) into backstepping design to eliminate the feasibility conditions completely and avoid off-line parameter optimization. It is shown that, with the proposed neuroadaptive control scheme, not only stable system operation is maintained but also each objective function is confined within the prespecified region, which could be asymmetric and time-varying. The effectiveness of the algorithm is validated via simulation on speed regulation of extruding machine in tire production lines.

Most existing control methods for quadrotor unmanned aerial vehicles (UAVs) are based on the primary assumption that the center of gravity (CoG) is fixed and is in the same position as the centroid, which is not necessarily true with swing load as continuously making CoG vary with the swing angle and substantially complicating the dynamic model of UAV. This article presents an adaptive learning and fault-tolerant control scheme for quadrotor UAVs with varying CoG and unknown moment of inertia. First, we establish the dynamic model of quadrotor UAVs in the presence of time-varying CoG, input saturation, and actuator fault. Then, we design a fault-tolerant adaptive learning controller for the quadrotor UAVs and show that both linear and angular velocity tracking errors are ensured to converge to a residual set around zero in the presence of full-state constraints. Furthermore, all signals in the closed-loop system are uniformly ultimately bounded. Simulation studies also confirm the effectiveness of the proposed control method.

For pure-feedback nonlinear systems under asymmetric output constraint, we present a low-cost neuroadaptive tracking control solution with salient features benefited from two design steps. In the first step, a novel output-dependent universal barrier function (ODUBF) is constructed such that not only the restrictive condition on constraining boundaries/functions is removed but also both constrained and unconstrained cases can be handled uniformly without the need for changing the control structure. In the second step, to reduce the computational burden caused by the neural network (NN)-based approximators, a single parameter estimator is developed so that the number of adaptive law is independent of the system order and the dimension of system parameters, making the control design inexpensive in computation. Furthermore, it is shown that all signals in the closed-loop system are semiglobally uniformly ultimately bounded, the tracking error converges to an adjustable neighborhood of the origin, and the violation of output constraint is prevented. The effectiveness of the proposed method can be validated via numerical simulation.

This article investigates the issue of neuroadaptive tracking control for a family of unknown multi-input multi-output (MIMO) nonlinear uncertain systems subject to extreme actuation failures. Different from most existing methods that are built upon partial loss of actuation effectiveness, here, in this article, we explicitly consider the situation that some actuators at some particular channel completely fail to work, an issue that has not been well addressed. By integrating the neural network approximation technique with two error transformations, a neuroadaptive fault-tolerant control strategy is developed with two attractive features: 1) it is capable of coping with the scenario that some of the actuators suffer from extreme actuation faults without the need for fault detection and diagnosis (FDD)/fault detection and isolation (FDI) or actuator switching and 2) the tracking error is forced to converge to a prescribed residual (symmetric or asymmetric) boundary at a preassignable decay mode within a prechosen finite settling time despite actuator failures and external disturbances. Numerical simulation studies confirm the effectiveness and benefits of the presented control approach.

In this paper, we investigate the distributed secure state estimation and control problems for interconnected cyber-physical systems (CPSs) with some sensors being attacked. First, by exploring the distinct properties of the unidentifiable attacks to a CPS, an explicit sufficient condition that the secure state estimation problem can be solvable is established. Then distributed preselectors and observers are presented to solve the secure state estimation problems. Furthermore, with the obtained state estimation, fractional dynamic surface-based distributed secure controllers are also proposed for the secure control problem. Theoretical analysis shows that, with the proposed distributed secure observers and controllers, not only the state of the CPS under attacks can be obtained in a given finite time but also the dynamic surface can be achieved and maintained in a finite time. Finally, the results are applied to an islanded micro-grid system as an illustration, which verifies the effectiveness of the proposed schemes.

This paper presents a neuroadaptive tracking control approach for uncertain robotic manipulators subject to asymmetric yet time-varying full-state constraints without involving feasibility conditions. Existing control algorithms either ignore motion constraints or impose additional feasibility conditions. In this paper, by integrating a nonlinear state-dependent transformation into each step of backstepping design, we develop a control scheme that not only directly accommodates asymmetric yet time-varying motion (position and velocity) constraints but also removes the feasibility conditions on virtual controllers, simplifying design process, and making implementation less demanding. Neural network (NN) unit accounting for system uncertainties is included in the loop during the entire system operational envelope in which the precondition on the NN training inputs is always ensured. The effectiveness and benefits of the proposed control method for robotic manipulator are validated via computer simulation.

In this article, we propose a one-step control design approach for pure-feedback nonlinear systems in the presence of unmatched and nonvanishing external disturbances. Different from the commonly utilized backstepping design, the proposed method, integrated with the dynamic surface control (DSC) technique, only involves one-step design with one single Lyapunov function in the whole control synthesis, which derives the actual control and the intermediate controls simultaneously in a collective way, avoiding the repetitive design procedures and multiple Lyapunov functions, yet circumventing the issue of "explosion of complexity." Furthermore, with this method, the increase in system order does not increase the design and analysis complexity. Numerical simulation examples confirm and validate the effectiveness of the proposed method.

Considering both the tracking safety of multi-HSTs and the operational efficiency of a single HST, intelligent safe driving methods (ISDMs) are proposed to obtain better speed-distance curves by integrating hybrid automata (HA) with data mining algorithms in this paper. To begin with, an intelligent safe distance controller is established by using HA to ensure the tracking safety of multi-HSTs' operation in real time. Then, data-driven intelligent driving methods based on ensemble algorithms (Bagging or Adaboost.R) and classification and regression tree (CART) are proposed to discover the potential driving rules from the field driving data. Furthermore, because of the continuous rise of HST's operation mileage, the driving data collected from HST has increased tremendously compared with the subways. So, an iterative pruning error minimization algorithm is designed to reduce the redundancy of the driving data and improve the computational speed of the learning process. Finally, compared with the automatic train operation (ATO) method, the energy consumption of B-CART, A-CART, and S-A-CART algorithms can be decreased by 3.32%, 3.80%, and 4.30%, respectively.

In this paper, we present a new prescribed-time distributed control method for consensus and containment of networked multiple systems. Different from both regular finite-time control (where the finite settling time is not uniform in initial conditions) and the fixed-time control (where the settling time cannot be preassigned arbitrarily), the proposed one is built upon a novel scaling function, resulting in prespecifiable convergence time (the settling time can be preassigned as needed within any physically allowable range). Furthermore, the developed control scheme not only ensures that all the agents reach the average consensus in prescribed finite time under undirected connected topology, but also ensures that all the agents reach a prescribed-time consensus with the root's state being the group decision value under the directed topology containing a spanning tree with the root as the leader. In addition, we extend the result to prescribed-time containment control involving multiple leaders under directed communication topology. Numerical examples are provided to verify the effectiveness and the superiority of the proposed control.

For high-speed train (HST), high-precision of train positioning is important to guarantee train safety and operational efficiency. For improving train positioning accuracy, we develop a mathematical positioning model by analyzing the wireless position report created by HST. To begin with, k-means algorithm is integrated with the least square support vector machine (LSSVM) to differentiate the position data and establish the corresponding prediction model for each position data class. Then, the ant colony optimization (ACO) algorithm is introduced to adaptively optimize the clustering number of position data and solve the over-fitting problem of the single k-means algorithm. So, a better classification of position data can be obtained by ACO-k-means than the single k-means algorithm. Furthermore, the online learning algorithms are designed for improving the adaptability and real-time performance of established positioning model. Finally, the field data of Beijing-Shanghai high-speed railway (BS_HSR) is used to test the performance of the established positioning models. Experiments on real-world positioning data sets from BS_HSR illustrate that the proposed methods can enhance the real-time performance in online updating process on the premise of reducing the positioning error.

In this paper, we investigate the tracking control problem for a class of strict feedback systems with pregiven performance specifications as well as full-state constraints. Our focus is on developing a feasible neural network (NN)-based control method that is able to, under full-state constraints, force the tracking error to converge into a prescribed region within preset finite time and further reduce the error to a smaller and adjustable residual set, while confining the overshoot within predefined small level. Based on two consecutive error transformations governed by two auxiliary functions, named with behavior-shaping function and asymmetric scaling function, respectively, a novel approach to achieve given performance specifications is developed under certain bound condition on the transformed error, such condition, along with the full-stated constraints, is guaranteed by imbedding barrier Lyapunov function (BLF) into the back-stepping design. Furthermore, asymmetric output constraints are maintained with a single symmetric BLF, simplifying the procedure of stability analysis. All internal signals including the stimulating inputs to the NN unit are ensured to be bounded. Both theoretical analysis and numerical simulation verify the effectiveness and the benefits of the design.

In this paper, the dissipative synchronization control problem for Markovian jump memristive neural networks (MNNs) is addressed with fully considering the time-varying delays and the fragility problem in the process of implementing the gain-scheduled controller. A Markov jump model is introduced to describe the stochastic changing among the connection of MNNs and it makes the networks under consideration suitable for some actual circumstances. By utilizing some improved integral inequalities and constructing a proper Lyapunov-Krasovskii functional, several delay-dependent synchronization criteria with less conservatism are established to ensure the dynamic error system is strictly stochastically dissipative. Based on these criteria, the procedure of designing the desired nonfragile gain-scheduled controller is established, which can well handle the fragility problem in the process of implementing the controller. Finally, an illustrated example is employed to explain that the developed method is efficient and available.

This paper investigates the position and attitude tracking control problem of a quadrotor unmanned aerial vehicle subject to modeling uncertainties and actuator failures. A comprehensive mathematical model reflecting the nonlinearity and state-space coupling of the dynamics as well as actuation faults and external disturbances is derived. By combining the radial basis function neural networks (NNs) with virtual parameter estimating algorithms, an indirect NN-based adaptive fault-tolerant control scheme is developed, which exhibits several attractive features as compared with most existing methods: 1) it is not only robust and adaptive to nonparametric uncertainties but also tolerant to unexpected actuation faults; 2) it ensures stable tracking without the need for precise information on system model; and 3) it only involves one lumped parameter adaptation, thus is structurally simpler and computationally less expensive, rendering the resultant scheme less demanding in programming and more affordable for onboard implementation. The effectiveness and benefits of the proposed method are confirmed via computer simulation.

This paper investigates the dissipativity-based fuzzy control problem for nonlinear dynamic systems based on the event-triggered mechanism. For the sake of reducing the number of transmissions while maintaining the closed-loop stability of the system, the event-triggered mechanism is considered. Moreover, the dissipativity is also taken into account in designing a controller that ensures the resulting closed-loop system is asymptotically stable and strictly (X, Y, Z)-θ-dissipative. In view of the fuzzy model, the stability of the resulting system is analyzed in terms of Lyapunov stability theory. According to the stability conditions, a fuzzy controller is designed. Additionally, the explicit expression of the desired controller is given in view of linear matrix inequalities. Finally, the corresponding simulation results are plotted by applying the standard software, and a practical example on a truck-trailer model is provided to verify and illustrate the effectiveness and applicability of the proposed fuzzy controller design scheme.

In this paper, we propose a novel computational architecture of memristor-based echo state network (MESN) with the online least mean square (LMS) algorithm. Newman and Watts small-world network is adopted for the topological structure of MESN network with memristive neural synapses. In the MESN network, the state matrix of the reservoir layer, which is obtained by raising the dimension of input data, is utilized as an input of the LMS algorithm to train the output weight matrix on chip. After certain iterations, the resistance value of memristor is adjusted to a constant. Thus, the final weight output matrix is obtained. To verify the effectiveness of the proposed MESN network, car evaluation and short-term power load forecasting are employed with the effect evaluation of the node number and the connectivity degree of the reservoir layer. The research provides a novel way to design neuromorphic computing systems.

In this paper, we investigate the adaptive consensus control for a class of high-order nonlinear systems with different unknown control directions where communications among the agents are represented by a directed graph. Based on backstepping technique, a fully distributed adaptive control approach is proposed without using global information of the topology. Meanwhile, a novel Nussbaum-type function is proposed to address the consensus control with unknown control directions. It is proved that boundedness of all closed-loop signals and asymptotically consensus tracking for all the agents' outputs are ensured. In simulation studies, a numerical example is illustrated to show the effectiveness of the control scheme.

In this paper, we present a neuroadaptive control for a class of uncertain nonlinear strict-feedback systems with full-state constraints and unknown actuation characteristics where the break points of the dead-zone model are considered as time-variant. In order to deal with the modeling uncertainties and the impact of the nonsmooth actuation characteristics, neural networks are utilized at each step of the backstepping design. By using barrier Lyapunov function, together with the concept of virtual parameter, we develop a neuroadaptive control scheme ensuring tracking stability and at the same time maintaining full-state constraints. The proposed control strategy bears the structure of proportional-integral (PI) control, with the PI gains being automatically and adaptively determined, making its design less demanding and its implementation less costly. Both theoretical analysis and numerical simulation validate the benefits and the effectiveness of the proposed method.

This paper investigates optimal robust output containment problem of general linear heterogeneous multiagent systems (MAS) with completely unknown dynamics. A model-based algorithm using offline policy iteration (PI) is first developed, where the -copy internal model principle is utilized to address the system parameter variations. This offline PI algorithm requires the nominal model of each agent, which may not be available in most real-world applications. To address this issue, a discounted performance function is introduced to express the optimal robust output containment problem as an optimal output-feedback design problem with bounded -gain. To solve this problem online in real time, a Bellman equation is first developed to evaluate a certain control policy and find the updated control policies, simultaneously, using only the state/output information measured online. Then, using this Bellman equation, a model-free off-policy integral reinforcement learning algorithm is proposed to solve the optimal robust output containment problem of heterogeneous MAS, in real time, without requiring any knowledge of the system dynamics. Simulation results are provided to verify the effectiveness of the proposed method.

This paper focuses on the problem of L2-L∞dynamic output feedback controller (DOFC) design for nonlinear switched systems with nonlinear perturbations in the Takagi- Sugeno fuzzy framework. First, the average dwell time approach is used to stabilize a nonlinear switched system exponentially under an arbitrary switching law. Then, based on the technique of piecewise Lyapunov functions, a fuzzy-rule-dependent DOFC is designed to ensure that the overall closed-loop system is exponentially stable with a weighted L2-L∞performance level (γ, α). The solvability condition for the desired DOFC is derived using a linearization technique. It is shown that the controller parameters can be obtained as solutions to a set of strict linear matrix inequalities that are numerically solvable with available standard software. Finally, two simulation examples illustrate effectiveness of the developed technique, including cognitive-radio systems.

In this paper, a new hierarchical decentralized optimization architecture is proposed to solve the economic dispatch problem for a large-scale power system. Conventionally, such a problem is solved in a centralized way, which is usually inflexible and costly in computation. In contrast to centralized algorithms, in this paper we decompose the centralized problem into local problems. Each local generator only solves its own problem iteratively, based on its own cost function and generation constraint. An extra coordinator agent is employed to coordinate all the local generator agents. Besides, it also takes responsibility to handle the global demand supply constraint based on a newly proposed concept named virtual agent. In this way, different from existing distributed algorithms, the global demand supply constraint and local generation constraints are handled separately, which would greatly reduce the computational complexity. In addition, as only local individual estimate is exchanged between the local agent and the coordinator agent, the communication burden is reduced and the information privacy is also protected. It is theoretically shown that under proposed hierarchical decentralized optimization architecture, each local generator agent can obtain the optimal solution in a decentralized fashion. Several case studies implemented on the IEEE 30-bus and the IEEE 118-bus are discussed and tested to validate the proposed method.

This brief investigates the finite-time consensus tracking control problem for networked uncertain mechanical systems on digraphs. A new terminal sliding-mode-based cooperative control scheme is developed to guarantee that the tracking errors converge to an arbitrarily small bound around zero in finite time. All the networked systems can have different dynamics and all the dynamics are unknown. A neural network is used at each node to approximate the local unknown dynamics. The control schemes are implemented in a fully distributed manner. The proposed control method eliminates some limitations in the existing terminal sliding-mode-based consensus control methods and extends the existing analysis methods to the case of directed graphs. Simulation results on networked robot manipulators are provided to show the effectiveness of the proposed control algorithms.

This paper studies the prescribed performance tracking control problem for a class of multi-input multi-output strict-feedback systems with asymmetric nonsmooth actuator characteristics and output constraints as well as unexpected external disturbances. By combining a novel speed transformation with barrier Lyapunov function, a neural adaptive control scheme is developed that is able to achieve given tracking precision within preassigned finite time at prespecified converging mode. At each of the first $n-1$ steps of backstepping design, we make use of the radial basis function neural networks to cope with the uncertainties arising from unknown and time-varying virtual control gains, and in the last step, we introduce a matrix factorization technique to remove the restrictive requirement on the unknown control gain matrix and its NN-approximation, simplifying control design. Furthermore, to reduce the number of parameters to be online updated, we introduce a virtual parameter to handle the lumped uncertainties, resulting in a control scheme with low complexity and inexpensive computations. The effectiveness of the proposed control strategy is validated by systematic stability analysis and numerical simulation.

This paper presents a distributed adaptive finite-time control solution to the formation-containment problem for multiple networked systems with uncertain nonlinear dynamics and directed communication constraints. By integrating the special topology feature of the new constructed symmetrical matrix, the technical difficulty in finite-time formation-containment control arising from the asymmetrical Laplacian matrix under single-way directed communication is circumvented. Based upon fractional power feedback of the local error, an adaptive distributed control scheme is established to drive the leaders into the prespecified formation configuration in finite time. Meanwhile, a distributed adaptive control scheme, independent of the unavailable inputs of the leaders, is designed to keep the followers within a bounded distance from the moving leaders and then to make the followers enter the convex hull shaped by the formation of the leaders in finite time. The effectiveness of the proposed control scheme is confirmed by the simulation.

This paper studies the zero-error tracking control problem of Euler-Lagrange systems subject to full-state constraints and nonparametric uncertainties. By blending an error transformation with barrier Lyapunov function, a neural adaptive tracking control scheme is developed, resulting in a solution with several salient features: 1) the control action is continuous and smooth; 2) the full-state tracking error converges to a prescribed compact set around origin within a given finite time at a controllable rate of convergence that can be uniformly prespecified; 3) with Nussbaum gain in the loop, the tracking error further shrinks to zero as ; and 4) the neural network (NN) unit can be safely included in the loop during the entire system operational envelope without the danger of violating the compact set precondition imposed on the NN training inputs. Furthermore, by using the Lyapunov analysis, it is proven that all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded. The effectiveness and benefits of the proposed control method are validated via computer simulation.

In this paper, a neuroadaptive fault-tolerant tracking control method is proposed for a class of time-delay pure-feedback systems in the presence of external disturbances and actuation faults. The proposed controller can achieve prescribed transient and steady-state performance, despite uncertain time delays and output constraints as well as actuation faults. By combining a tangent barrier Lyapunov-Krasovskii function with the dynamic surface control technique, the neural network unit in the developed control scheme is able to take its action from the very beginning and play its learning/approximating role safely during the entire system operational envelope, leading to enhanced control performance without the danger of violating compact set precondition. Furthermore, prescribed transient performance and output constraints are strictly ensured in the presence of nonaffine uncertainties, external disturbances, and undetectable actuation faults. The control strategy is also validated by numerical simulation.

This paper investigates the distributed fault-tolerant control problem of networked Euler-Lagrange systems with actuator and communication link faults. An adaptive fault-tolerant cooperative control scheme is proposed to achieve the coordinated tracking control of networked uncertain Lagrange systems on a general directed communication topology, which contains a spanning tree with the root node being the active target system. The proposed algorithm is capable of compensating for the actuator bias fault, the partial loss of effectiveness actuation fault, the communication link fault, the model uncertainty, and the external disturbance simultaneously. The control scheme does not use any fault detection and isolation mechanism to detect, separate, and identify the actuator faults online, which largely reduces the online computation and expedites the responsiveness of the controller. To validate the effectiveness of the proposed method, a test-bed of multiple robot-arm cooperative control system is developed for real-time verification. Experiments on the networked robot-arms are conduced and the results confirm the benefits and the effectiveness of the proposed distributed fault-tolerant control algorithms.

This paper investigates the problem of stability analysis and stabilization for Takagi-Sugeno (T-S) fuzzy systems with time-varying delay. By using appropriately chosen Lyapunov-Krasovskii functional, together with the reciprocally convex a new sufficient stability condition with the idea of delay partitioning approach is proposed for the delayed T-S fuzzy systems, which significantly reduces conservatism as compared with the existing results. On the basis of the obtained stability condition, the state-feedback fuzzy controller via parallel distributed compensation law is developed for the resulting fuzzy delayed systems. Furthermore, the parameters of the proposed fuzzy controller are derived in terms of linear matrix inequalities, which can be easily obtained by the optimization techniques. Finally, three examples (one of them is the benchmark inverted pendulum) are used to verify and illustrate the effectiveness of the proposed technique.

This paper investigates the problem of achieving rotating formation and containment simultaneously via finite time control schemes for multiagent systems. It is nontrivial to maintain rotating formation where the desired formation structure is time-varying and only neighboring information is available. The underlying problem becomes even more complicated if containment is imposed yet finite time convergence is required at the same time. To tackle this problem, a polar coordinate-based approach is exploited in this paper. Finite time control protocols are established for leader agents and follower agents, respectively, such that three goals are achieved in finite time concurrently: 1) all the agents maintain a stable rotating motion around a common circular center with a common (possibly time-varying) angular velocity; 2) the leader agents form and maintain a prespecified rotating formation structure; and 3) the follower agents converge to the shifting convex hull shaped by the dynamically moving (circling) leaders. It is the polar coordinate expression that simplifies the formulation of the rotating formation-containment problem and facilitates the finite time control design process. The effectiveness of the proposed control scheme is illustrated via both formative mathematical analysis and numerical simulation.

This paper studies the output containment control of linear heterogeneous multi-agent systems, where the system dynamics and even the state dimensions can generally be different. Since the states can have different dimensions, standard results from state containment control do not apply. Therefore, the control objective is to guarantee the convergence of the output of each follower to the dynamic convex hull spanned by the outputs of leaders. This can be achieved by making certain output containment errors go to zero asymptotically. Based on this formulation, two different control protocols, namely, full-state feedback and static output-feedback, are designed based on internal model principles. Sufficient local conditions for the existence of the proposed control protocols are developed in terms of stabilizing the local followers' dynamics and satisfying a certain H∞ criterion. Unified design procedures to solve the proposed two control protocols are presented by formulation and solution of certain local state-feedback and static output-feedback problems, respectively. Numerical simulations are given to validate the proposed control protocols.

For a high-speed train (HST), quick and accurate localization of its position is crucial to safe and effective operation of the HST. In this paper, we develop a mathematical localization model by analyzing the location report created by the HST. Then, we apply two sparse optimization algorithms, i.e., iterative pruning error minimization (IPEM) and L0-norm minimization algorithms, to improve the sparsity of both least squares support vector machine (LSSVM) and weighted LSSVM models. Furthermore, in order to enhance the adaptability and real-time performance of established localization model, four online sparse learning algorithms LSSVM-online, IPEM-online, L0-norm-online, and hybrid-online are developed to sparsify the training data set and update parameters of the LSSVM model online. Finally, the field data of the Beijing-Shanghai highspeed railway (BS_HSR) is used to test the performance of the established localization models. The proposed method overcomes the problem of memory constraints and high computational costs resulting in highly sparse reductions to the LSSVM models. Experiments on real-world data sets from the BS_HSR illustrate that these methods achieve sparse models and increase the realtime performance in online updating process on the premise of reducing the location error. For the rapid convergence of proposed online sparse algorithms, the localization model can be updated when the HST passes through the balise every time.

In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.

This paper presents a new approach to construct neural adaptive control for uncertain nonaffine systems. By integrating locally weighted learning with barrier Lyapunov function (BLF), a novel control design method is presented to systematically address the two critical issues in neural network (NN) control field: one is how to fulfill the compact set precondition for NN approximation, and the other is how to use varying rather than a fixed NN structure to improve the functionality of NN control. A BLF is exploited to ensure the NN inputs to remain bounded during the entire system operation. To account for system nonlinearities, a neuron self-growing strategy is proposed to guide the process for adding new neurons to the system, resulting in a self-adjustable NN structure for better learning capabilities. It is shown that the number of neurons needed to accomplish the control task is finite, and better performance can be obtained with less number of neurons as compared with traditional methods. The salient feature of the proposed method also lies in the continuity of the control action everywhere. Furthermore, the resulting control action is smooth almost everywhere except for a few time instants at which new neurons are added. Numerical example illustrates the effectiveness of the proposed approach.

This paper considers the tracking control problem for a class of multi-input multi-output nonlinear systems subject to unknown actuation characteristics and external disturbances. Neuroadaptive proportional-integral (PI) control with self-tuning gains is proposed, which is structurally simple and computationally inexpensive. Different from traditional PI control, the proposed one is able to online adjust its PI gains using stability-guaranteed analytic algorithms without involving manual tuning or trial and error process. It is shown that the proposed neuroadaptive PI control is continuous and smooth everywhere and ensures the uniformly ultimately boundedness of all the signals of the closed-loop system. Furthermore, the crucial compact set precondition for a neural network (NN) to function properly is guaranteed with the barrier Lyapunov function, allowing the NN unit to play its learning/approximating role during the entire system operation. The salient feature also lies in its low complexity in computation and effectiveness in dealing with modeling uncertainties and nonlinearities. Both square and nonsquare nonlinear systems are addressed. The benefits and the feasibility of the developed control are also confirmed by simulations.

The "universal" approximating/learning feature of neural network (NN), widely and extensively used for control design, is contingent upon some critical conditions, either of which, if not satisfied, would render such feature vanished. In this paper, we show that these conditions are literally linked with several fundamental issues that have been overlooked in most existing NN-based control designs, either unconsciously or deliberately. We further propose a collective approach to explicitly address these issues, establishing a strategy enabling the NN unit to be fully functional in the control loop during the entire process of system operation and ensuring the more reliable and more effective NN-associated control performance. This is achieved by incorporating the control with a new structural NN unit, consisting of a group of diversified neurons with self-adjusting subneurons, each being driven/stimulated by input signals confined within a compact set. Meanwhile, the continuity of the control signal and the boundedness of all the closed-loop signals are ensured. Both the theoretical analysis and numerical simulation validate the effectiveness of the proposed method.The "universal" approximating/learning feature of neural network (NN), widely and extensively used for control design, is contingent upon some critical conditions, either of which, if not satisfied, would render such feature vanished. In this paper, we show that these conditions are literally linked with several fundamental issues that have been overlooked in most existing NN-based control designs, either unconsciously or deliberately. We further propose a collective approach to explicitly address these issues, establishing a strategy enabling the NN unit to be fully functional in the control loop during the entire process of system operation and ensuring the more reliable and more effective NN-associated control performance. This is achieved by incorporating the control with a new structural NN unit, consisting of a group of diversified neurons with self-adjusting subneurons, each being driven/stimulated by input signals confined within a compact set. Meanwhile, the continuity of the control signal and the boundedness of all the closed-loop signals are ensured. Both the theoretical analysis and numerical simulation validate the effectiveness of the proposed method.

There has been continuously increasing interest in applying neural networks (NNs) to identification and adaptive control of practical systems that are characterized by nonlinearity, uncertainty, communication constraints, and complexity. The past few years have witnessed a variety of new developments in NN-based approaches for behavior learning, information processing, autonomous decision, and system control. Biologically inspired NN structures can significantly enhance the capabilities of information processing, control, and computational performance. New discoveries in neurocognitive psychology, sociology, and elsewhere reveal new neurological learning structures with more powerful capabilities in complex problem solving and fast decision in dynamic environments. The goal of the special issue is to consolidate recent new developments in NN structures for signal processing, autonomous decision, and adaptive control with application to complex systems. It includes contributions from a wide range of research aspects relevant to the topic, ranging from neural computing, adaptive control, cooperative control, autonomous decision systems, mathematical and computational models, neuropsychology decision and control, algorithms and simulation, to applications and/or case studies. This issue contains 24 papers and the contents of which are summarized below.

In this paper, the problem of containment control of networked multiagent systems is considered with special emphasis on finite-time convergence. A distributed neural adaptive control scheme for containment is developed, which, different from the current state of the art, is able to achieve dynamic containment in finite time with sufficient accuracy despite unknown nonaffine dynamics and mismatched uncertainties. Such a finite-time feature, highly desirable in practice, is made possible by the fraction dynamic surface control design technique based on the concept of virtual fraction filter. In the proposed containment protocol, only the local information from the neighbor followers and the local position information from the neighbor leaders are required. Furthermore, since the available information utilized is local and is embedded into the control scheme through fraction power feedback, rather than direct linear or regular nonlinear feedback, the resultant control scheme is truly distributed. In addition, although mismatched uncertainties and external disturbances are involved, only one single generalized neural parameter needs to be updated in the control scheme, making its design and implementation straightforward and inexpensive. The effectiveness of the developed method is also confirmed by numerical simulation.

This paper investigates the antagonistic consensus problem of a class of second-order multi-agent systems with fixed topology. A distributed consensus control algorithm is proposed for each agent to realize the four-group antagonistic consensus. A sufficient condition is derived to ensure that all agents make a four-group antagonistic motion in a distributed manner. It is shown that all agents can be spontaneously divided into four groups, and agents in the same group collaborate while agents in different groups compete. Numerical simulations are included to demonstrate our theoretical results.

This paper develops an adaptive control scheme for position and velocity tracking control of high speed trains under uncertain system nonlinearities and actuator failures. Neural networks with self-organizing capabilities are integrated into control design, where the number of the neurons can be adjusted online automatically, so as not only to avoid the problem inherent in the NN with fixed structure but also to deal with system uncertainties containing of nonlinear in-train forces, traction-braking nonlinearities, as well as the unknown actuation faults. As such, the resultant control algorithms are able to achieve high precision train speed and position tracking under varying operation railway conditions, as validated by theoretical analysis and numerical simulations.

Small persistent differences of slip velocities due to different actuation effectiveness of driving motors/braking units may lead to severe nonuniform rolling wear or fatigue damage of a part of actuation wheelsets after long-term operation, which would shorten service life or even endanger operational safety of the high-speed train. How to eliminate the nonuniform rolling wear/fatigue damage of actuation wheelsets using the control method is a very interesting and challenging issue. In this paper, robust adaptive observers are developed to identify uncertain dynamics of the train body and actuation wheelsets, based on which a uniform rolling-wear-based traction/braking control scheme is established. It is shown that with this controller, not only the common objective of traction/braking operation is achieved, but also actuator differences are completely compensated such that the same slip velocity (implying uniform driving load and rolling wear) is ensured for all actuation wheelsets during long-term operation. Both theoretical analysis and numerical simulations validate the effectiveness of the proposed control method.

This paper studies the problem of high-speed train operation with special attention to minimizing the energy consumption. The performance characteristics of a high-speed train, including traction characteristic and regenerative braking, and the railway geographical conditions consisting of slope, curve, and tunnel parameters, are fully considered in the dynamic model in order to make it more effective and practical. A new optimal strategy for train operation is developed, and its novelty lies in the fact that it is the first time to optimize the actual speed curve using the method of dual speed curve optimization, which contains two processes of offline global optimization and online local optimization, thus leading to more energy saving as compared with most existing methods with only one-time optimization process. We utilize combination optimization techniques, in tandem with the speed codes and subsections, to solve the global optimization problem with a genetic algorithm. Predictive control is developed for local optimization to refine the global optimization in real time, more particularly, the train operation modes including traction, cruise, coast, and braking are switched on the base of the line slope information, from which a more energy-efficient speed trajectory is generated under the constraints of fixed time and distance. To verify the effectiveness of the proposed strategy, operation of CHR-3 on high-speed railway is tested. Through the comparison of energy consumption in two typical cases, it verifies that the proposed energy-saving strategy works better than that of single optimization strategy. At the same time, the actual speed deviation can be corrected in a timely manner with the proposed method.

This paper investigates the tracking control problem of dynamic systems consisting of physically connected subsystems with virtual connections through local communication, where unknown unidentical nonlinearities, time-varying yet undetectable actuation faults, and varying actuation authorities are involved. The local communication nature and the physical uncertain interactions among the subsystems, together with the unpredictable actuation failures and control authority variation, make the underlying problem nontrivial, calling for a control solution that is not only decentralized (distributed) but also adaptive and fault tolerant. In this paper, with the aid of the concepts of generalized parameter estimation error and virtual regrouping, a distributed and fault-tolerant control design approach is presented by using local (neighboring) information exchange only. This method is applied to develop tracking and braking control schemes for high-speed trains subject to traction and braking failures. The proposed distributed control is capable of simultaneously coping with the physical interactions among the subsystems, compensating the uncertain control gains, and accommodating the undetectable actuation faults, as authenticated and verified by theoretical analysis and numerical simulations.

This paper presents a distributed cooperative control scheme for voltage unbalance compensation (VUC) in an islanded microgrid (MG). By letting each distributed generator (DG) share the compensation effort cooperatively, unbalanced voltage in sensitive load bus (SLB) can be compensated. The concept of contribution level (CL) for compensation is first proposed for each local DG to indicate its compensation ability. A two-layer secondary compensation architecture consisting of a communication layer and a compensation layer is designed for each local DG. A totally distributed strategy involving information sharing and exchange is proposed, which is based on finite-time average consensus and newly developed graph discovery algorithm. This strategy does not require the whole system structure as a prior and can detect the structure automatically. The proposed scheme not only achieves similar VUC performance to the centralized one, but also brings some advantages, such as communication fault tolerance and plug-and-play property. Case studies including communication failure, CL variation, and DG plug-and-play are discussed and tested to validate the proposed method.

Wheel skid is highly undesirable because it could endanger the safe operation of high-speed trains. How to avoid excessive wheel skid via an active adhesion control method represents an interesting and challenging topic of research. In this work, we first introduce the conditions of antiskid operation and formulate it as a constrained tracking control problem, based on which two model-based antiskid slip velocity control laws are developed. Then, by applying two adaptive force observers to estimate the unknown and varying adhesion force and resistance, we develop an adaptive antiskid adhesion control scheme. The novelty of the proposed method is that control errors of the closed-loop system are used to online update the observer parameters, such that the predefined control precision can be ensured with the proposed observer-based adhesion control. To deal with the constrained antiskid control, a barrier Lyapunov function is constructed, and the effectiveness of the proposed control scheme is theoretically authenticated with confirmation by numerical simulation.

Singh and Potluri suggest that they have spotted three “errors” in the dynamic model presented in our paper. Upon carefully examining the model in our paper and the claims made by Singh and Potluri, we believe that no such errors exist in the model of the paper. The detail explanation is provided.

Excessive lateral and roll motions of a high-speed train might endanger its operational safety. This paper investigates how to suppress those motions via an active-suspension method. By exploiting the structural properties of the system model and the triangular control gain, a new control scheme capable of attenuating immeasurable disturbances, compensating modeling uncertainties, and accommodating actuation faults is developed. Compared with most existing methods, the proposed method does not require precise information on the suspension parameters and the detail system model. Moreover, the magnitude of the actuation fault and the time instant at which the actuation fault occurs are not needed in setting up and implementing the proposed control scheme. The controller is tested and validated via computer simulations in the presence of parametric uncertainties and varying operation conditions.

Variable-speed fixed-pitch (VSFP) wind turbine is widely used in small-to-medium scale wind turbine markets due to its simple structure, low cost and high reliability features. However, power control, especially maximum power limitation control at high wind velocities, of such turbine concept is hard to realize and it has not been studied much in existing researches so far. To overcome this difficulty, we propose a simple power control strategy for VSFP wind turbines, covering the entire range of wind velocity. The proposed strategy contains a novel maximum power point tracking (MPPT) control method, a constant-speed (CS) soft-stalling control method and a constant-power (CP) soft-stalling control method. These three sub-control methods are appropriately combined in a way to realize seamless transitions among different operational modes (MPPT, CS and CP). The system structure and operational principle of the proposed strategy are thoroughly analyzed. Theoretical analysis is verified by simulation and experimental results performed by a 1.2kW fixed-pitch variable-speed wind turbine prototype.

Energy-efficient train operation represents an important issue for daily operational urban rail transit. Most energy-efficient train operation strategies are normally planned according to a timetable, which is designed by offline traffic information. In this paper, a new energy-efficient train operation model based on real-time traffic information is proposed from the geometric and topographic points of view through a nonlinear programming method, leading to an energy-efficient driving strategy with real-time interstation running time monitored by the automatic train supervision system. The novelty of this work lies not only in the establishment of a new model for energy-efficient train operation but also in the utilization of combining analytical and numerical methods for deriving energy-efficient train operation strategies. More specifically, the energy-efficient operation model is built based on trajectory analysis when the energy-efficient optimal controls are applied, from which an energy-efficient reference trajectory is obtained under the running time and distance constraints, in which the nonlinear programming method is utilized. In contrast to most existing methods, the proposed model turns out to be a small-scale problem, and the difficulties of solving partial differential equations or the process of predetermining and reiteratively calculating some key factors as traditionally involved are avoided. Thus, it is more feasible to implement the strategy and easier to make real-time adjustment if needed. The comparative analysis and the simulation verification with the actual operating data confirm the effectiveness of the proposed method. With the proposed method, some delayed trains are able to maintain punctuality at the next station and sometimes even reducing energy consumption.

Advanced control is a key technology for enhancing safe and reliable operation of high-speed trains. This paper presents an automated train control scheme for high-speed trains with combined longitudinal aerodynamics and tracking/braking dynamics, with special emphasis on reliable position and velocity tracking in the face of traction/braking failures. The controller is synthesized using a so-called virtual-parameter-based backstepping adaptive control method, which exhibits several salient features: 1) The inherent coupling effects are taken into account as a result of combining both longitudinal and traction/braking dynamics; 2) fully parameter independent rather than partially parameter independent control algorithms are derived; and 3) closed-loop tracking stability of the overall system is ensured under unnoticeable time-varying traction/braking failures. The effectiveness of the developed control scheme is authenticated via a formative mathematical analysis based on Lyapunov stability theory and validated via numerical simulations.

This paper considers a cooperative tracking problem for a group of nonlinear multiagent systems under a directed graph that characterizes the interaction between the leader and the followers. All the networked systems can have different dynamics and all the dynamics are unknown. A neural network (NN) with flexible structure is used to approximate the unknown dynamics at each node. Considering that the leader is a neighbor of only a subset of the followers and the followers have only local interactions, we introduce a cooperative dynamic observer at each node to overcome the deficiency of the traditional tracking control strategies. An observer-based cooperative controller design framework is proposed with the aid of graph tools, Lyapunov-based design method, self-structuring NN, and separation principle. It is proved that each agent can follow the active leader only if the communication graph contains a spanning tree. Simulation results on networked robots are provided to show the effectiveness of the proposed control algorithms.

This paper focuses on analyzing a new model transformation of discrete-time Takagi–Sugeno (T–S) fuzzy systems with time-varying delays and applying it to dynamic output feedback (DOF) controller design. A new comparison model is proposed by employing a new approximation for time-varying delay state, and then, a delay partitioning method is used to analyze the scaled small gain of this comparison model. A sufficient condition on discrete-time T–S fuzzy systems with time-varying delays, which guarantees the corresponding closed-loop system to be asymptotically stable and has an induced$\ell_{2}$disturbance attenuation performance, is derived by employing the scaled small-gain theorem. Then, the solvability condition for the induced$\ell_{2}$DOF control is also established, by which the DOF controller can be solved as linear matrix inequality optimization problems. Finally, examples are provided to illustrate the effectiveness of the proposed approaches.

This paper investigates the path-tracking control problem of four-wheel-steering and four-wheel-driving (4WS4WD) road vehicles. Of particular interest is the development of an adaptive and fault-tolerant tracking control scheme capable of compensating vehicle uncertain dynamics/disturbances and actuation failures simultaneously. Control algorithms are derived without requiring detail system dynamic information. The control scheme is shown to be effective in coping with unexpected actuation faults without the need for analytically estimating bound on actuator failure variables. The proposed method is validated and demonstrated through its application to a wheeled vehicle with four steering wheels and four driving wheels, where high-precision path tracking is achieved in the face of steering faults.

While FastSLAM algorithm is a popular solution to SLAM problem, it suffers from two major drawbacks: one is particle set degeneracy due to lack of observation information in proposal distribution; the other is errors accumulation caused by inaccuracy linearization of the robot motion model and the observation model. To overcome the problems, we propose a new Jacobian free CFastSLAM algorithm. The main contribution of this work lies in the utilization of Cubature Kalman Filter (CKF), which calculate Gaussian Weight Integral based on Cubature Rule, to design an optimal proposal distribution of the particle filter and to estimate the environment feature landmarks. On the basis of Rao-Blackwellized particle filter, proposed algorithm is comprised by two main parts: in the first part, a Cubature Particle Filter (CPF) is derived to localize the robot; in the second part, a set of CKFs is used to estimate the environment landmarks. The performance of the CFastSLAM is investigated and compared with that of FastSLAM2.0 and UFastSLAM in simulations and experiments. Results verify that the CFastSLAM improves SLAM performance.

In this paper, the problem ofl2-l∞filtering for a class of discrete-time Takagi-Sugeno (T-S) fuzzy time-varying delay systems is studied. Our attention is focused on the design of full- and reduced-order filters that guarantee the filtering error system to be asymptotically stable with a prescribedH∞performance. Sufficient conditions for the obtained filtering error system are proposed by applying an input-output approach and a two-term approximation method, which is employed to approximate the time-varying delay. The corresponding full- and reduced-order filter design is cast into a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

This paper is concerned with the problem of H(∞) model reduction for Takagi-Sugeno (T-S) fuzzy stochastic systems. For a given mean-square stable T-S fuzzy stochastic system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with an H(∞) performance but also translates it into a linear lower dimensional system. Then, the model reduction is converted into a convex optimization problem by using a linearization procedure, and a projection approach is also presented, which casts the model reduction into a sequential minimization problem subject to linear matrix inequality constraints by employing the cone complementary linearization algorithm. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed methods.

This paper studies the lateral and longitudinal path tracking control of four-wheel steering autonomous vehicles. A robust and adaptive fault-tolerant tracking control strategy is proposed to simultaneously counteract modeling uncertainties, unexpected disturbances, coupling effects, as well as actuator failures. By introducing the virtual points along the longitudinal centerline of the vehicle and utilizing a state transformation, a special feature of the control gain matrix is revealed, which allows for the development of structurally simple and computationally inexpensive robust adaptive and fault-tolerant control algorithms. The closed-loop stability issues of the control scheme are analyzed using a Lyapunov-based method. A nonlinear dynamic model of a passenger vehicle is developed to simulate the performance of control design. The controller is tested and validated via computer simulations in the presence of parametric uncertainties and varying driving conditions.

The problem of the position and velocity tracking control of high-speed trains becomes interesting yet challenging when simultaneously considering inevitable factors such as the resistive friction and aerodynamic drag forces, the interactive impacts among the vehicles, and the nonlinear traction/braking notches inherent in train systems. In this paper, a multiple point mass with a single-coordinate dynamic model that reflects resistive and transient impacts is derived, and based on this, computationally inexpensive robust adaptive control designs with optimal task distribution for speed and position tracking are proposed under traction/braking nonlinearities and saturation limitations. It is shown that the proposed method is not only robust to external disturbances, aerodynamic resistance, mechanical resistance, and transient impacts but adaptive to unknown system parameters as well. The effectiveness of the proposed approach is also confirmed through numerical simulations.

Resistive forces such as mechanical frictions and aerodynamic drags are inevitable in high speed train during its operation. This paper investigates the problem of adaptive compensation of such uncertain resistive forces in train systems to achieve high precision speed and position tracking. A control scheme is developed via adaptive backstepping approach to address not only the traction and braking dynamics ignored in most existing methods, but also the uncertain resistive forces arisen from varying operation conditions. Both theoretical and numerical simulation confirms that the resultant control algorithms are able to achieve high precision train position and speed tracking.

The problem of formation control of multiple unmanned ground vehicles (UGVs) is studied in this paper. The underlying problem is essentially a high dimensional multi-vehicle trajectory tracking control problem, its complexity grows significantly as the number of the involved vehicles increases. To address the singularity inherent in regular formation process and the complexity involved in formation of multiple vehicles, this paper attempts an approach that integrates robust adaptive neural network (NN) control with the virtual leader concept. The fundamental idea behind this approach is to use the virtual leader-follower format to convert the problem into the one that only involves each individual vehicle to track the virtual leader. By doing so, the singularity associated with the traditional formation control is also avoided. As the system involves significant nonlinearities and uncertainties, robust adaptive NN control algorithms are developed, which are shown to be able to achieve high precision formation. The feasibility and effectiveness of the proposed method are also verified by simulation studies.

This work addresses the problem of formation control for multiple spacecraft in a Planetary Orbital Environment (POE). Due to the diverse interferences and uncertainties in outer space, traditional control methods encounter great difficulties in this area. A new control approach inspired by human memory system is proposed, which is shown to be capable of learning from past control experience and current behavior to improve its performance and demands much less system dynamic information as compared with traditional controls. Both theoretical analysis and computer simulation verify its effectiveness.

This study investigates the speed control problem of separately excited DC motors via automatically regulating armature and field voltages. Three set of control algorithms are developed to achieve this objective. Theoretical analysis and computer simulation demonstrate that the proposed algorithms are effective to achieve high performance control under varying operation conditions.

With the aim of controlling the motion of robots without involving complex design procedures or extensive online computations, this paper presents a strategy consisting of a linear feedback control and a self-tuning control. The main advantage of the strategy is its simplicity in design, program code and real-time implementation. The only information required in setting up the strategy is the order and the measurable states the system, while detailed description of the model is not needed. Furthermore, uncertain effects such as static and dynamic joint frictions and external disturbances can be easily handled. In addition, all the internal signals are uniformly bounded and the control torque is smooth everywhere. These features are also verified via simulation on the GE-P50 robot.>