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.

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.

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.

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 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 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.

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 provides an adaptive asymptotic exponential regulation solution for nonparametric uncertain strict-feedback systems under asymmetric time-varying output constraints. Unlike most existing methods for achieving exponential regulation, where the persistent excitation (PE) condition is in need, here in this work the restriction on the PE condition is removed by introducing a novel parameter estimation error deceleration transformation technique. In addition, more general nonparametric uncertainties but not parametric uncertainties are considered in this article. Furthermore, a new nonlinear state-dependent function (NSDF) is employed to ensure that the asymmetric time-varying output constraint cannot be violated all the time, which also allows the same convergence rate between the system state and transformed function. Additionally, all signals in the closed-loop system are ensured to be bounded. Both theoretical analysis and simulation examples validate the effectiveness of the proposed control algorithm.

Accurate estimation of the Lithium-ion batteries (LiBs) state of health (SOH) is essential to ensure the safe and reliable operation of battery-powered devices. Most of the current data-driven SOH estimation models are designed under fixed ambient temperatures, overlooking the high sensitivity of LiBs to changing ambient temperatures. To bridge this gap, a novel method is proposed with transfer learning (TL) to model and estimate the SOH at varying ambient temperatures. First, canonical variate analysis is employed to capture the temporal dynamics in the time-series data of LiBs and extract temporal features. Second, at the reference temperature, an interpretable SOH estimation model with a long short-term memory network is utilized to learn the regression relationship between the temporal features and the labeled capacities. Third, at the new temperature, a small amount of data is projected into the key feature domain at the reference temperature to obtain the common temporal features. Afterward, TL is employed to take advantage of the existing process knowledge through keeping all functional layers of the previous well-trained SOH estimation model. Finally, the efficacy of the proposed method is verified on the NASA dataset with two discrete temperatures, 24°C and 44°C. Evaluated by the index of root mean squared error, the proposed method is capable of improving prediction accuracy by 94.18% when the model is transferred from 44°C to 24°C.

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.

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.

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.

In this work, an online solution for reconstructing and predicting the uncertain target trajectory in real-time is proposed based on general regression neural network (GRNN). On this basis, an adaptive tracking control scheme guaranteeing prescribed performance is suggested for a class of strict-feedback systems with unknown control directions. In contrast to existing trajectory reconstruction methods, the one presented in this note does not require prior modeling of the uncertain target or offline training. Contrary to most current state-of-the-art prescribed performance control (PPC) technology, a novel time-varying scaling function and its corresponding translation function are introduced such that no strict constraints on initial conditions are needed, that is, global stability is achieved. The proposed control scheme allows the output of the system to chase the predicted value of the uncertain target, and the tracking error converges to a prescribed small set within a preassigned time, despite unmatched uncertainties and unknown control directions. The benefits of the proposed control scheme are confirmed by numerical simulations.

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.

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 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.

This article investigates the tracking control problem for Euler-Lagrange (EL) systems subject to output constraints and extreme actuation/propulsion failures. The goal here is to design a neural network (NN)-based controller capable of guaranteeing satisfactory tracking control performance even if some of the actuators completely fail to work. This is achieved by introducing a novel fault function and rate function such that, with which the original tracking control problem is converted into a stabilization one. It is shown that the tracking error is ensured to converge to a pre-specified compact set within a given finite time and the decay rate of the tracking error can be user-designed in advance. The extreme actuation faults and the standby actuator handover time delay are explicitly addressed, and the closed signals are ensured to be globally uniformly ultimately bounded. The effectiveness of the proposed method has been confirmed through both theoretical analysis and numerical simulation.

This article addresses the finite-time attitude formation-containment control problem for networked uncertain rigid spacecraft under directed topology. A unified distributed finite-time attitude control framework, based on the sliding-mode control (SMC) principle, is developed. Different from the current state of the art, the proposed attitude control method is suitable for not only the leader spacecraft but also the follower spacecraft, and only the neighbor state information among spacecraft is required, allowing the resulting control scheme to be truly distributed. Furthermore, the proposed method is inherently continuous, which eliminates the undesired chattering problem. Such features are deemed favorable in practical spacecraft applications. In addition, upon using the proposed neuro-adaptive control technique, the attitude formation-containment deployment can be achieved in finite time with sufficient accuracy, despite the involvement of both the uncertain inertia matrices and external disturbances. The effectiveness of the developed control scheme is confirmed by numerical simulations.

In this study, a biologically inspired echo state network (ESN)-based method is established for the asymptotic tracking control of a class of uncertain multi-input multi-output (MIMO) systems. By mimicking the characters of real biological systems, a diversified multiclustered echo state network (DMCESN) is proposed in this work and then it is applied to deal with the modeling uncertainties and coupling nonlinearities in the control systems. Different from the most existing neural network (NN)-based control methods that only ensure the uniform ultimate boundedness result, the proposed method can allow the tracking error to achieve asymptotic convergence through rigorous theoretical analysis. The effectiveness of the proposed method is also confirmed by numerical simulation by comparing with multilayer feedforward network-based control scheme and traditional ESN-based control, admitting better tracking performance of the proposed control.

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.

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.

This article addresses the distributed finite-time attitude tracking control problem for a group of uncertain rigid spacecraft in the presence of unavailable angular velocity under the directed topology condition. First, a finite-time adaptive neural network observer is proposed for each follower to estimate its own unavailable angular velocity. Unlike existing velocity-observer-based design methods, the proposed one does not need the exact knowledge of the system model, and works well for the systems with both vanishing and nonvanishing uncertainties. Further, another finite-time observer is provided to obtain the precise angular velocity information of the dynamic leader in a distributed manner. Based on these two observers and adding a power integrator technique, a continuous distributed finite-time control scheme with only attitude measurements is finally established. A rigorous theoretical proof shows that the entire finite-time stability of the combined observer-controller closed-loop system is ensured. Simulation results illustrate the benefits and effectiveness of the developed control scheme.

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.

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 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.

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 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.

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.