Real-time multi-agent internet data transmission enhances the service capabilities of diverse applications, however, it poses a risk of sensitive location information being compromised. This study proposes a privacy-enhanced multi-agent framework integrating a randomized masking algorithm with a prescribed-time fault-tolerant (PTFT) consensus control strategy. The core contribution is the design of a jointly generated dynamic masking mechanism, which uses random numbers from both parties to obfuscate sensitive state data. This approach enhances the security of transmitted network data through increased randomness while eliminating the non-zero constraint of traditional privacy protection methods at the initial time. The piecewise mask function design ensures immediate recovery of genuine states post-initial masking, achieving precision control without privacy compromise. Additionally, the PTFT controller enables semi-global consensus within the prescribed time despite actuator faults, and ensures the boundedness of all signals in the closed-loop systems. Finally, the algorithm simulation experiment verifies the effectiveness of the proposed scheme and compares it with the traditional privacy protection method.

This paper introduces a novel privacy mask framework for securing network communication processes, specifically applied to consensus tracking. First, an innovative output mask map is presented to achieve time-selective privacy protection for dynamic network location data. The designed mask map overcomes key limitations of existing approaches, such as the inability to assign zero values to masked locations, the restriction of protection only to initial data, and the lack of rapid mask termination mechanisms. Second, the proposed framework is applied to multi-agent adaptive consensus tracking control to ensure the security of agents’ information and the boundedness of all signals in the closed-loop system. To counteract potential tracking performance degradation due to the computational complexity introduced by the privacy mask, prescribed-performance control theory is incorporated. Third, the dynamic surface control technique is used to avoid the derivation of the received encrypted position information, so as to handle the uncertainty originating from the unknown mask map. Ultimately, simulations comparing our framework with existing mask mechanisms highlight its advantages.

This article primarily investigates the tracking control problem of high-order uncertain nonlinear systems with odd-rational-power under false data injection (FDI) attacks and malicious attacks, based on the fully actuated system (FAS) theory. Due to the corruption of the state information by an additional attack signal, the true state information cannot be directly used for controller design. To mitigate the impact of unknown FDI attacks, a coordinate transformation is applied using the attacked state. In addition, using a piecewise smooth function approaching a saturation function, a new lemma is proposed to deal with the unknown control gain of the prescribed-time control input saturation and malicious attacks problem. Theoretical analysis demonstrates that the tracking errors converge in the prescribed time and all closed-loop system signals remain bounded. Finally, a numerical example is provided, along with a practical case study based on a single-link robotic manipulator, to validate the effectiveness of the proposed method.

This paper presents an asymptotic tracking control strategy for an n-link flexible-joint (FJ) robotic system subject to full-state constraints and external disturbances. First, the original system is transformed into a chained system structure, then a state-dependent function (SDF) is applied to convert the constrained system into an unconstrained one. Subsequently, a novel disturbance observer (DO) is constructed to asymptotically estimate and compensate for unknown disturbances. Concurrently, a neural adaptive tracking controller incorporating a first-order filter is designed, with an adaptive law utilized to approximate the maximum value between the norm of weight vector and the upper bound of approximation error. Compared with the existing DOs and backstepping-based methods to realize the tracking control, the developed method guarantees the tracking error asymptotically converges to zero without violating the predefined constraints. The efficacy of the proposed control algorithm is validated through simulation and experimental studies on a two-link manipulator system.

A data-drivenH∞control method with dynamic performance constraints is proposed for the problem of region stabilization control in the case of unknown system matrix. By establishing a system characterization model based on measured data, the generalized pole configuration theory is ingeniously combined with the data-driven control framework. Firstly, the real-time data generated from the system operation is used to construct a data-driven dynamic characterization. A generalized pole constraint-based regional constraints condition is established to accurately regulate the state convergence rate and damping response characteristics while ensuring the stability of the closed-loop system. The classicalH∞control framework is further integrated to construct a novel data-drivenH∞performance criterion to achieve the synergistic optimization of dynamic performance constraints and disturbance suppression capability. The method gets rid of the dependence on the a priori model of the system and solves the co-optimization problem of unknown system dynamic performance regulation and robustness enhancement under a unified framework. Numerical simulations and power system control case validations show that the integrated control performance of uncertain complex systems is significantly improved.

This study addresses the adaptive prescribed-time consensus tracking control problem for high-order multi-agent nonlinear systems with an improved privacy protection mechanism. A distinctive feature of the control method lies in the utilization of the fully actuated system approach to study high-order multi-agent systems, enabling system control without the need to simplify the high-order systems into the first-order systems. For agents requiring state information protection, the flexible privacy protection treats the output mask function as the transformation function, thereby protecting the privacy of all signals in the systems and allowing users to define the protection time. In addition, a prescribed-time scale function suitable for high-order systems is proposed by incorporating a constant term to avoid the singularity issue. This prescribed-time tracking control strategy ensures that the synchronization error converges to the prescribed region within the prescribed time, and prescribed time aligns with user-defined time in the privacy protection mechanism. Finally, the superiority of the proposed control method is verified through a numerical simulation comparison with different privacy protection method.

This study develops an advanced self-triggered predefined-time safety learning control algorithm for switched multi-agent systems with full-state mask. To strengthen encryption while reducing the impact to system performance, an improved settling time privacy preservation mechanism based on the full-state mask function is designed, which encrypts the true information of the system and enhances the privacy of information delivery. Unlike traditional learning control schemes, a novel actor-critic weight update law is designed to guarantee that the system energy cost is minimized resulting in predefined time optimization. Besides, an improved self-triggered condition with a compensation term is developed to overcome the complex challenges posed by full-state privacy preservation mechanism. It not only eliminates the necessity to continually monitor the triggered state of the system but also saves communication resources. Finally, the validity of the designed control scheme can be proven by a simulation experiment.

In this study, we propose an innovative prescribed-time consensus control strategy for nonlinear strict-feedback multi-agent systems (MASs) with privacy protection requirements. Firstly, compared with the existing privacy protection strategies, the mask function adopted in this paper remains unknown to all agents, including the sender, thus greatly improving the security level of information transmission. Secondly, the existing related research results basically overlook prescribed-time control in the context of privacy preservation, based on the backstepping method, a prescribed time performance function is adopted in this paper, so that the systems can make the tracking error within the defined accuracy range within a user-defined time. Finally, through the verification of MATLAB simulation experiments, the proposed control strategy not only effectively realizes the privacy-preserving consensus control of multi-agent systems, but also shows better control performance compared with the existing schemes. Note to Practitioners—This paper aims to develop a mask privacy protection prescribed-time control algorithm for information transmission between multiple agents. In the automation industry, the demand for privacy protection in multi-agent systems is critical, necessitating the implementation of robust measures during agent collaboration and data sharing to safeguard data confidentiality. Employing advanced privacy-preserving technologies is essential to prevent the exposure of sensitive information, thereby ensuring the security of corporate secrets and operational integrity, in compliance with the evolving stringent privacy regulations. In addition, prescribed-time control enables users to achieve preset accuracy within a predefined time, reducing industrial resource consumption and improving resource utilization in the automation industry.

In practice, many mechanical systems are underactuated, such as naval vessels, cranes, and helicopters, to reduce energy consumption and enhance flexibility. However, compounded by strong nonlinearity arising from state coupling, the underactuated nature and high-order unavailable states pose great challenges to motion control (particularly for unactuated states lacking independent actuators or kinematic constraints). In this article, an adaptive controller based on fully-actuated system methods is proposed, together with a general and extensible analysis method. First, a group of high-order auxiliary variables, consisting of actuated/unactuated states, their derivatives, and proportional-differential terms, are designed to rearrange the nonlinear underactuated system as a high-order linear fully-actuated system without any linearization operations. The asymptotic convergence of auxiliary variables theoretically eliminates the steady-state errors of actuated/unactuated states together. For high-order unmeasurable variables, they are recovered by the constructed neural network observer to estimate high-order dynamics, which avoids discontinuous robust terms and improves the accuracy of compensation/positioning. Motivated by the inherent features and advantages of fully-actuated systems, this article proposes the first fully-actuated system-based continuous adaptive controller for a class of underactuated robots. Moreover, it is convenient to extend the proposed controller to handle more practical problems, such as state delay, without the need to reconduct Lyapunov-based analysis. In addition to complete theoretical frames, this article also provides several experimental validation.

This study concentrates on adaptive prescribed-time tracking control forn-link flexible-joint (FJ) manipulators with unmeasurable state variables. First of all, auxiliary signals are constructed utilizing measurable variables. Based on auxiliary signals, the observer is directly designed to estimate the system states, which allows the observer dynamics to incorporate unknown terms. Owing to the uniqueness of the designed observer, the observation errors converge to zero. Furthermore, with the aim of enhancing control efficiency, the prescribed-time scale function is introduced into the controllers, and the unknown terms are processed based on the fuzzy logic system (FLS), so that the tracking error of FJ manipulators converges to the specified range within the prescribed time. Meanwhile, with the help of positive integrable time-varying functions, asymptotic tracking is further achieved. In the whole control design, the tuning functions are adopted to avoid overparameterization. In theory, it is ensured that all signals in the closed-loop system are bounded, and the tracking error converges to the small neighborhood of the zero within a specified time and gradually converges to zero. Finally, the simulation example confirms the feasibility of the control design.

This article discusses the decentralized tracking control problem of large-scale nonlinear systems characterized by unknown disturbances, time-varying delays, and polynomial growth conditions. To tackle the problem, a novel robust decentralized tracking method is developed. First, scaling gain transformations are introduced to obtain a transformed system. Subsequently, some stable decentralized controllers are skillfully constructed for an auxiliary system utilizing a recursive control design approach. By presenting a new domination control method together with the utilization of Lyapunov–Krasovskii (L–K) functional, the decentralized tracking controllers are designed and the system stability is guaranteed. Finally, the chemical reactor system model is employed to validate the feasibility of the strategy.

This article proposes a prescribed-time fuzzy optimal control approach for flexible-joint (FJ) robot systems utilizing the reinforcement learning (RL) strategy. The uniqueness of this method lies in its ability to ensure optimal tracking performance for n-link flexible joint robots within the prescribed-time frame, while the actor and critic fuzzy logic system effectively approximate the optimal cost and evaluates system performance. First, the optimal controllers with the auxiliary compensation term are constructed by utilizing the online approximation of the modified performance index function and RL actor-critic structure. The designed controller can deal with unknown structure impacts and avoid model identification. Besides, in designing the prescribed-time scale function, the introduced constant term not only prevents singularity but also allows flexible setting of constraint regions. The proposed scheme is theoretically verified to satisfy the Bellman optimality principle and ensure the tracking error converges to the desired zone within the prescribed time. Finally, the practicability of the designed control scheme is further demonstrated by the 2-link FJ robot simulation example.

This study presents an advanced adaptive fuzzy optimal bridge-hole constraint control method for large-scale interconnected systems under quantized input. To address the conflict in constraint ranges caused by the combined effect of both results in the bridge-hole and performance constraints, a new prescribed time function with parameter requirements is proposed, which bridges the balance between them and keeps the tracking error within a desired zone in a prescribed time. Meanwhile, output constraint is realized by building a new bridge-hole constraint function, which ensures the time interval for the constraint behavior to occur by the flexible setting of the switching time. Unlike traditional optimal control schemes, the designed optimal controller is further quantized by a hysteresis quantizer, which minimizes energy cost and saves bandwidth. Besides, a reinforcement learning (RL) scheme based on an actor–critic-identifier fuzzy logic system (FLS) structure is designed; its overall control idea is to optimize the entire backstepping control system by using all virtual and actual backstepping control as the optimal solution of their respective subsystems. Finally, the effectiveness of the proposed scheme is confirmed by simulation experiments.

For the existing tracking control schemes of flexible-joint robots, precise sensor measurement is an implicit premise. However, idealized sensors are difficult to achieve due to manufacturing technology or other external factors. To this end, this paper further investigates the tracking control problem for flexible-joint robots with unknown measurement sensitivity. Specifically, for such multi-input multi-output Euler-Lagrange systems with completely unknown system dynamics, a novel measurement values-based adaptive control method is proposed by fusing sensitivity information and system variables into Lyapunov function candidates, where the restriction on system states in other the approximation lemma-based results is removed, since unknown nonlinearities are scaled by the structural characteristics of system variables. Above all, even if there are measurement errors, satisfactory tracking performance can be obtained by adjusting the design parameters, which is proved by rigorous theoretical analysis. Finally, hardware experiments further verify the effectiveness of the proposed method.Note to Practitioners—This work is motivated by the trajectory tracking control problem for flexible-joint robots under imprecise sensor measurements. Due to manufacturing technology limitations and component aging, there is inevitably a deviation between the measured values of sensors and real values, and this problem may become more prominent as the working environment of flexible-joint robots tends to become more complex. To our knowledge, most of the existing solutions for flexible-joint robots are developed based on precise sensor measurements, and they may fail to achieve satisfactory performance when real state information is not available. Moreover, the prior knowledge about model parameters and measurement sensitivity is difficult or impossible to exactly obtain in practice, which seriously hinders the further application of control methods that are dependent on system dynamics. To this end, this paper proposes a novel tracking control scheme based on measurement information for flexible-joint robots with unknown measurement sensitivity, where the dependence on model information is eliminated with the elaborately constructed Lyapunov function candidates, and the real tracking error is still adjusted to an acceptable range even if there are measurement errors. Preliminary experiments on a flexible-joint robot developed by Quanser company demonstrate the feasibility and effectiveness of the proposed method. In future studies, designing an effective scheme to achieve direct preset tracking control is the focus of the work.

This paper investigates the prescribed-time control for nonlinear systems with sensor faults and unknown control directions. Firstly, the prescribed-time control is realized based on scale function and descending power coordinate transformation. Then, effective nonlinear terms are designed in the controllers to compensate for state pollution caused by sensor faults. Furthermore, the Nussbaum gain function is introduced and it can be proven that the derivative of its variable is bounded, which is of significance to analyze the invariant set. The proposed control scheme guarantees that system states converge to zero within the specified time in advance and all signals of the closed-loop system are bounded. Finally, a numerical simulation and a practical simulation based on the Nomoto ship model intuitively show the feasibility of the scheme.Note to Practitioners—This study is motivated by achieving the prescribed-time control for nonlinear systems with sensor faults and unknown control directions. With the improvement of automation, the prescribed-time control that the convergence time can be arbitrarily specified in advance has been applied to many time-critical fields, such as missile guidance, emergency braking, and so on. It is noted that hypersonic weapons, uncalibrated visual servo control, and other practical models have the characteristic of unknown control direction, and most of the existing prescribed-time control schemes are not suitable for these practical systems. In addition, since the hovercraft power systems and aircraft systems are in a harsh environment for a long time, it can easily lead to sensor faults resulting in major accidents. In this study, based on the descending power coordinate transformation, the prescribed-time controllers with unique nonlinear terms are designed to deal with the above challenges.

This article addresses adaptive optimized tracking control problem for strict-feedback cyclic switched nonlinear output constrained systems with average cyclic dwell time (ACDT). Different from most of the existing results on optimized control of switched nonlinear systems, a new mode-dependent reinforcement learning (RL) algorithm of identifier-critic-actor architecture is designed. Technically, with the help of neural networks (NNs) switched state observer, the virtual and actual optimal controllers are developed by solving the Hamilton-Jacobi-Bellman (HJB) equation. Meanwhile, to reduce the impact of systems switching on the overall optimization control, the information of the switching signal is considered into the optimal performance index functions. In an attempt to settle the output constraints, a nonlinear output-dependent time-varying function is used to ensure that the system output never transgresses the prescribed regions. More importantly, by combining the improved ACDT method and Lyapunov stability theorem, a novel adaptive optimized control scheme is put forward which ensures that the boundedness of all signals in the closed-loop system. Finally, the effectiveness of the proposed optimized control algorithm is verified by numerical as well as practical simulations.

A slow time-delay assumption restricts the application of control approaches for numerous systems which are constantly affected by multiple uncertainties, including parameters, control coefficients, and the asymmetric dead-zone input. This work presents a new adaptive method for a class of high-order nonlinear delayed systems by removing the so-called slow time-delay assumption and multiple uncertainties. Remarkably, with a novel Lyapunov-Razumikhin (L-R) function and a direct fuzzy adaptive regulation scheme, a memoryless adaptive feedback controller is skillfully constructed to guarantee that the output tracks the given reference signal while keeping the boundedness of all closed-system signals. Finally, the presented scheme is applied to control a single-link robot system.

This article centers around investigating the event-triggered nonsingular fixed-time tracking issue for an n -link rigid robot manipulator with full-state constraints, external disturbances, and model uncertainties. We propose the definition of the constrainedly practically fixed-time stability (CPFTS) and provide a sufficient condition for CPFTS. A novel auxiliary function is developed to address the singularity issue caused by repeated differentiation in achieving the fixed-time tracking control. The uncertain parameters are approximated using the radial basis function neural network (RBFNN). This study proposes the model-based and the neutral network-based tracking control approaches, designed using the scaling function technique and the barrier Lyapunov function, respectively, to ensure that the tracking error systems are CPFTS and the full-state constraints comply. Moreover, the communication transmission load is reduced using the relative threshold event-triggered control strategy. Simulation results demonstrate the effectiveness of the proposed tracking control algorithms.

With the advantages of natural flexibility, large force-weight ratios, and green cleanliness, pneumatic artificial muscle (PAM) actuators that mimic biological skeletal muscles have attracted much attention. However, the inherent defects of PAMs, such as high nonlinearities, limited contraction lengths and frequencies, and multiple input constraints, pose significant challenges to the motion control of PAM-actuated parallel robots; meanwhile, most existing methods do not take into account motion constraints and working efficiency. To this end, a prescribed-time adaptive fuzzy motion control method is developed in this article, where PAM-actuated parallel robots can accurately achieve prescribed tracking performance within an allowable input pressure range. In particular, regardless of the initial values of target trajectories, the expected tracking accuracy is achieved within the prescribed time by restricting the tracking errors to the improved performance constraints; also, the motion velocities remain within the preset dynamic constraints, thereby improving the working safety and efficiency. To the best of authors' knowledge, this article presents thefirstadaptive fuzzy motion control method for PAM-actuatedparallelrobots, which cansimultaneouslyachieve motion constraints and prescribed tracking performance. Moreover, the stability of all signals is proved through theoretical analysis, and then the effectiveness of the proposed method is fully verified by a series of hardware experiments.

This study reports a fixed-time tracking control problem for strict-feedback nonlinear systems with quantized inputs and actuator faults where the total number of faults is allowed to be infinite. By taking advantage of radial basis function neural networks (RBFNNs), unknown nonlinear function terms in the system dynamic model can be effectively approached. In addition, based on the sector property of quantization nonlinearities and the structure of the actuator fault model, novel adaptive estimations and innovative auxiliary design signals are constructed to compensate for the influence caused by actuator faults and quantized inputs properly in the fixed-time convergence settings. Then, rigorous theoretical analysis manifests that the proposed control scheme can make the output tracking error converge to a small neighborhood of the origin within a fixed time, and the upper bound of the setting time not only does not depend on initial states of the system but also can be preassigned by selecting parameters appropriately. Meanwhile, all the signals in the closed-loop system remain bounded. Finally, a numerical example and a practical example of a single-link manipulator are presented to demonstrate the effectiveness of the proposed control algorithm.

This article further investigates the tracking control problem of flexible-joint robot systems. Compared with existing results, all states are assumed to be quantized by a uniform quantizer, which results in discontinuous quantized signals. To address this issue, a new adaptive controller is proposed by introducing a command filter, in which the quantized virtual controller is used as input to obtain continuous signals. Thus, the discontinuity problem is avoided and the quantization influence is further compensated. It can be proven that all signals in the closed-loop system are bounded and tracking errors can converge to an arbitrarily small neighborhood of the origin. Finally, a simulation result is given to verify the practicability of the proposed method.

This article presents an asymptotic tracking controller for high-order uncertain nonlinear systems. Compared to the existing results, some restrictive assumptions are relaxed and the requirement of the variables being bounded is removed. At the same time, a prescribed time tracking control strategy is constructed to make that the system has better-transient performance during operation. Specifically, a global prescribed time function is proposed and an unconstrained variable is constructed to replace the constrained tracking error. Under this framework, an appealing feature of the designed controller is that the tracking error can converge to a specified range within a prescribed time and eventually converge to zero asymptotically starting from any initial conditions, where the prescribed time and accuracy can be directly given. Finally, the simulation results validate the effectiveness of the control strategy.

This article focuses on the adaptive stabilization control of high-order strict-feedback systems (SFSs) with unknown control directions. It addresses the challenge of dealing with unknown control directions in the fully actuated theory. To overcome this challenge, the Nussbaum gain technique and tuning functions directly based on the high-order fully actuated system (FAS) method are proposed to deal with the unknown control directions and system uncertainties, while avoiding the phenomenon of overparameterization. Additionally, a relative threshold event-triggered strategy is utilized to effectively avoid continuous controller updates, which helps conserve network resources used for signal transmission. The proposed controller guarantees the convergence of all system state variables to zero and ensures that all signals within the closed-loop system remain bounded. To validate the effectiveness of this approach, a numerical simulation and a practical example using the Nomoto ship model are conducted. Overall, this study contributes to the FAS field by providing a solution for adaptive stabilization control in high-order SFSs with unknown control directions.

This work focuses on the problem of predefined-time bipartite consensus tracking control for a class of nonlinear MASs with asymmetric full-state constraints. A predefined-time bipartite consensus tracking framework is developed, where both cooperative communication and adversarial communication among neighbor agents are implemented. Different from the finite-time and the fixed-time controller design methods for MASs, the prominent advantage of the controller design algorithm presented in this work is that our algorithm can make the followers track either the output or the opposite output of the leader within the predefined time in accordance to the user requirements. In order to obtain the desired control performance, an improved time-varying nonlinear transformed function is skillfully introduced for the first time to handle the asymmetric full-state constraints and radial basis function neural networks (RBF NNs) are employed to deal with the unknown nonlinear functions. Then, the predefined-time adaptive neural virtual control laws are constructed by using the backstepping technique, while their derivatives are estimated by the first-order sliding-mode differentiators. It is theoretically testified that the proposed control algorithm not only guarantees the bipartite consensus tracking performance of the constrained nonlinear MASs in the predefined time but also remains the boundedness of all the resulting closed-loop signals. Finally, the simulation research on a practical example shows the validity of the presented control algorithm.

This study mainly concentrates on adaptive asymptotic tracking control for input-quantized strict-feedback nonlinear systems subjected to multiple unknown control directions. Novel improved lemmas, which relax the conditions for handling unknown control coefficients in the existing theoretical results, are certificated that can be applied to resolve the tracking problem for nonlinear systems under input quantification and unknown control directions simultaneously. Furthermore, by incorporating positive integral time-varying functions and the disintegration of the hysteresis quantizer into the controller design, the asymptotic tracking control is successfully achieved. Moreover, all signals in the closed-loop system are guaranteed to be bounded. Ultimately, a comparing numerical simulation and a practical simulation of a Nomoto ship model are presented to validate the feasibility of the proposed control algorithm.

This article is devoted to the output feedback control of nonlinear system subject to unknown control directions, unknown Bouc-Wen hysteresis and unknown disturbances. During the control design process, the design obstacles caused by unknown control directions and Bouc-Wen hysteresis are eliminated by introducing linear state transformation and a new coordinate transformation, which avoids using the Nussbaum function with high-frequency oscillation to deal with the issue. Besides, to settle the issue caused by the unknown disturbances, a novel nonlinear disturbance observer is designed, which has the characteristics of simple structure, low coupling, and easy implementation. Especially, a compensation item is constructed to offset the redundant items generated in the backstepping design process. Simultaneously, using the neural network and backstepping technology, an output feedback controller is devised. The controller ensures that all closed-loop signals are bounded, and the system output, state observation error, and disturbance observation error converge to a small neighborhood of the origin. Finally, to illustrate the effectiveness of the proposed scheme, simulation verification is carried out based on a numerical example and a Nomoto ship model.

This study concentrates on the tracking control problem for nonlinear systems subject to actuator saturation. To improve the performance of the controller, we propose a fixed-time tracking control scheme, in which the upper bound of the convergence time is independent of the initial conditions. In the control scheme, first, a smooth nonlinear function is employed to approximate the saturation function so that the controller can be designed under the framework of backstepping. Then, the effect of input saturation is compensated by introducing an auxiliary system. Furthermore, a fixed-time adaptive neural network control method is given with the help of fixed-time control theory, in which the dynamic order of controllers is reduced to a certain extent since there is only one updating law in the entire control design. Through rigorous theoretical analysis, it is concluded that the proposed control scheme can guarantee that: 1) the output tracking error can converge to a small neighborhood near the origin in a fixed time and 2) all signals in the closed-loop system are bounded. Finally, a numerical example and a practical example based on the single-link manipulator are provided to verify the effectiveness of the proposed method.

The problem of finite-time consensus tracking is investigated for multiagent systems with unknown control gain functions and hysteresis quantized inputs. Existing control methods utilizing command filtered technique have certain limitations for unknown control gains. Motived by this, the study is concerned with establishing a novel control strategy based on a modified command filtered technique with estimation-like terms and key compensation terms. Furthermore, fuzzy logic systems participate in command filter design while processing unknown items. It is theoretically testified that the proposed scheme not only guarantees the finite-time consensus tracking performance but also remains that all the resulting closed-loop signals are bounded. Finally, the simulations on numerical and practical examples prove the validity of the control scheme.

This study reports the adaptive asymptotic tracking control problem for flexible-joint (FJ) robot systems, the output tracking error can be kept within the prescribed range in the initial stage of system operation, as time approaches infinity, the asymptotic tracking result can be obtained. The prescribed performance function and the positive integrable time-varying function are introduced simultaneously in the control design of FJ robot systems for the first time. The control scheme is designed under the frame of the adaptive backstepping method and command filtered technique, which successfully avoids the problem of complexity explosion. The radial basis function neural networks are used to deal with unknown uncertainties and the adaptive laws are designed to approximate the norms of weight vectors and approximation errors. Finally, the feasibility of the proposed scheme is proved by the simulation and the experiment of the 2-link FJ robot on the Quanser platform.

This article studies the finite-time tracking control problem for the single-link flexible-joint robot system with actuator failures and proposes an adaptive fuzzy fault-tolerant control strategy. More precisely, the issue of "explosion of complexity" is successfully solved by incorporating the command filtering technology and the backstepping method. The unknown nonlinearities are identified with the help of the fuzzy logic system. An event-triggered mechanism with the relative threshold strategy is exploited to save communication resources. Furthermore, the proposed control design can guarantee that the tracking error converges to a small neighborhood of origin within a finite time by taking full advantage of the finite-time stability theory. Finally, the simulation example is presented to further verify the validity of the proposed control method.

This article focuses on global finite-time output feedback stabilization for uncertain nonlinear systems with unknown measurement sensitivity. The existence of the continuous measurement error resulting from limited accuracy of sensors invalidates the existing design strategies depending on the use of the precise output in the construction of an observer, which highlights the contribution of this article. Essentially, different from related works, we propose a new finite-time convergent observer by avoiding the use of the information on nonlinearities. By combining the homogeneous domination with the addition of a power integrator method, an output feedback controller composed of multiple nested sign functions is successfully developed. Finally, the effectiveness of the presented scheme is exhibited by a numerical example.

This article focuses on the design of a novel adaptive fuzzy event-triggered tracking control approach for a category of high-order uncertain nonlinear systems with prescribed performance requirements, in which a high-order tan-type barrier Lyapunov function (BLF) is employed to handle and analyze the output tracking error, fuzzy systems are adopted to identify the totally unknown nonlinear functions, and only one gain function rather than parameter estimation functions is designed to cancel out all unknowns appearing in fuzzy systems. As a result, complicated calculations are avoided and a structured simple control is achieved. The proposed controller not only ensures that the tracking error is always within a predefined region but also reduces the communication burden from the controller to the actuator. Finally, comparison simulations are presented to verify the effectiveness of the proposed control schemes.

This article addresses an event-triggered adaptive fuzzy tracking control problem for nonlinear systems with unknown control directions. To achieve the practical tracking, fuzzy logic systems are employed to approximate unknown nonlinear function. Nussbaum functions are adopted to address the problem of the unknown control directions. The newly designed controller not only guarantees the tracking error that converges to an arbitrary small neighborhood near the zero but also reduces the communication burden from the controller to the actuator. Moreover, the feasibility of the proposed event-triggered mechanism is verified by excluding Zeno behavior. Finally, the simulation result proves the effectiveness of the designed control scheme.

This article reports our study on a reduced adaptive fuzzy decoupling control for our lower limb exoskeleton system which typically is a multi-input-multi-output (MIMO) uncertain nonlinear system. To show the applicability and generality of the proposed control methods, a more general MIMO uncertain nonlinear system model is considered. By decoupling control, the entire MIMO system is separated into several MISO subsystems. In our experiments, such a system may have problems (even unstable) if a traditional fuzzy approximator is used to estimate the complicated coupling terms. In this article, to overcome this problem, a reduced adaptive fuzzy system together with a compensation term is proposed. Compared to traditional approaches, the proposed fuzzy control approach can reduce possible chattering phenomena and achieve better control performance. By employing the proposed control scheme to an actual 2-DOF lower limb exoskeleton rehabilitation robot system, it can be seen from the experimental results that, as expected, it has good performance to track the model trajectory of a human walking gait. Therefore, it can be concluded that the developed approach is effective for the control of a lower limb exoskeleton system.

This article reports our study on asynchronous H∞filtering for fuzzy singular Markovian switching systems with retarded time-varying delays via the Takagi-Sugeno fuzzy control technique. The devised parallel distributed compensation fuzzy filter modes are described by a hidden Markovian model, which runs asynchronously with that of the original fuzzy singular Markovian switching delayed system. The fuzzy asynchronous filtering dealt with in this article contains synchronous and mode-independent filtering as special cases. Novel admissibility and filtering conditions are derived in terms of linear matrix inequalities so as to ensure the stochastic admissibility and the H∞performance level. Simulation examples including a singlelink robot arm are employed to demonstrate the correctness and effectiveness of the proposed fuzzy asynchronous filtering technique.

A novel adaptive fuzzy control scheme is designed in this article for a class of stochastic nonstrict feedback nonlinear systems with output constraint and unknown control coefficients. A reduced adaptive fuzzy system is proposed to approximate the unknown function which contains all state variables of the whole system and ensures that the backstepping design method works normally for nonstrict feedback nonlinear systems. With the use of this reduced adaptive fuzzy control method and a combination of Barrier Lyapunov Function control design and Nussbaum gain technique, a novel adaptive fuzzy controller is proposed to guarantee that the output tracking error always meets the given constraint requirement in a sense of probability and the resulting closed-loop states are bounded in probability. Finally, an example is presented to confirm the effectiveness of the designed method.

In this article, the issue of finite-time command filter-based adaptive fuzzy tracking control based on an event-triggered scheme for stochastic strict-feedback nonlinear systems is studied. By using a fuzzy logic system, finite-time command filter with compensation signals, an event-triggered adaptive controller is designed. The newly designed controller not only guarantees the property of finite-time convergence, but also reduces the communication burden from the controller to the actuator. Meanwhile, the problem of complexity explosion caused by the backstepping method is avoided by using command filter technology. The proposed controller can ensure that the output signal tracks the given reference signal under the bounded error. Finally, the simulation result proves the effectiveness of the proposed control method.

This article is concerned with the issue of asynchronous feedback control for fuzzy degenerate jump systems with mode-dependent time-varying delays via Takagi–Sugeno fuzzy control technique under observer-based event-driven characteristic. The improved observer and event trigger transmit not only state estimate signals but also the information of Markovian jump modes to the controller under the networked nonperiodic sampling scheme. Applying parallel distributed compensation technique, the asynchronous fuzzy feedback controller is devised, and the asynchronous fuzzy controller modes are depicted by a hidden Markovian model, where the modes of fuzzy controller run asynchronously with that of the original degenerate fuzzy jump systems. Exponentially decreasing function is employed to devise the triggering threshold, which can ensure not only the admissibility of the delayed fuzzy degenerate jump systems but also the effectiveness of event-driven strategy. Original stochastic admissibility and asynchronous feedback control conditions are characterized by linear matrix inequalities. A numerical example and a single-link robot arm model are applied to illustrate the correctness and validity of the proposed observer-based event-driven asynchronous fuzzy feedback control technique.

In this article, a global finite-time active disturbance rejection control (ADRC) scheme is proposed for tracking control of redundant parallel manipulators with unknown bounded uncertainties. This approach combines an ADRC and a global finite-time control for high accuracy trajectory tracking control. Based on the nonsingular fast terminal sliding mode control, the proposed approach can remove the condition in the original ADRC that the derivative of the uncertainties is required to be bounded. The extended state observer is employed to handle the real-time estimation of the total uncertainty. It can be found that the proposed scheme not only can converge fast to the semi-global finite-time stable equilibrium but also can have superior tracking control performance. In summary, compared to existing approaches, the proposed scheme can have several advantages, such as uncertainty rejection, easy implementation, robustness, chattering-free, high precision, and no need for prior knowledge of bounded uncertainties. The simulation results validate the effectiveness of the proposed method.

This article is concerned with the improvement of robust control methodology and its application in stabilizing a class of high-order nonlinear systems with multiple unknown time-varying sensitivities, and discusses how to use the proposed control strategy on humanoid robot manipulation. The novel design approach successfully breaks through the limitation of the neural network technique; that is, state variables must be located in some compact sets. The remarkable feature of the systems under investigation lies in the presence of measurement sensitivities and higher powers, which makes the nonlinear systems essentially different from the related works. By reductio and the introduction of a modified tuning function, an appropriate controller is constructed to render that all the state variables belong to a predetermined bounded set. Finally, an example is provided to illustrate the effectiveness of the proposed control strategy.

This paper focuses on the design of a reduced adaptive fuzzy tracking controller for a class of high-order stochastic nonstrict feedback nonlinear systems with full-state constraints. In the proposed approach, reduced fuzzy systems are used to approximate uncertain functions which involve all state variables and a high-order tan-type barrier Lyapunov function (BLF) is considered to deal with full-state constraints of the controlled system. With this BLF and a combination of the reduced fuzzy control and adding a power integrator, a novel control scheme is constructed to ensure that tracking error is within a very small range of the origin almost surely, meanwhile, the constraints on the system states are not breached almost surely during the operation. Two examples are proposed to show the effectiveness of the design scheme.

This article reports this article on the problem of adaptive fuzzy output tracking control for a category of high-order nonlinear systems with input saturation, output constraint, and serious uncertainties. A high-order barrier Lyapunov function and an auxiliary Hyperbolic Tangent function is employed to deal with output constraint and input saturation, respectively. By incorporating a backstepping design technique, adaptive fuzzy control, and adding a power integrator, a novel control scheme is designed to ensure that all states in the resulting closed-loop system are bounded and the tracking error converges to a bounded compact set. Moreover, two simulations are conducted to verify the effectiveness of the design method.

The issue of adaptive fuzzy prescribed performance tracking control is considered in this article for strict-feedback stochastic uncertain nonlinear systems. A novel adaptive tracking control design approach, in which the fuzzy systems are employed to approximate the totally unknown nonlinear terms, is proposed by incorporating the technology of prescribed performance control with the method of command filtered backstepping design. The proposed adaptive state feedback controller can ensure that the output tracking error converges to a predefined arbitrarily small residual set in probability and all the signals of the closed-loop system can be bounded in probability, meanwhile, the problem of “explosion of complexity” is solved. The effectiveness of the presented method is verified by two simulation examples.

This article proposes a novel approach to replace the fixed projection planes existed in the previous researches to reduce motion artifacts obtained from the human face by a normal webcam for monitoring heart rate in a real-time fashion. The novel projection plane is adaptively changed with the light intensity change to eliminate the color distortion induced by motion. In this article, the state-of-the-art semantic segmentation Deeplabv3+ is implemented to segment the skin pixels from the facial region that is detected by several trackers (Boosting, MIL, TLD, Median Flow, Mosse, and CSRT) to boost the computational time compared to the conventional face detection by Haar-Like features. Image and digital signal processing techniques are also applied to eliminate possible noise for obtaining a clean pulse signal. The proposed approach is compared with other existing approaches (Green, PCA, Chrom, and POS) in multiple challenges. From the experiments conducted, the Deeplabv3+ outperforms the conventional K-means for different kinds of skin segmentation. Moreover, the proposed approach is quite robust and stable in the stationary case (with the accuracy 96%), dim-lighting environment and the long-distance up to 4-m away without zooming in camera. Besides, multiple head-movement simulations and motions of fitness are conquered by the APP approach as shown in the experiments. Thus, it can be concluded that the proposed approach is applicable to surveillance or healthcare applications.

This article considers admissibility analysis and stabilization for implicit Markovian jump systems (IMJSs) with retarded discrete-distributed delays. Admissibility analysis is investigated for the unforced delay IMJSs by virtue of reciprocally convex integral inequality technique and Barbalat’s lemma. State feedback controller is designed via the matrix transformation technique to realize the stabilization of the delayed closed-loop IMJSs. By selecting comprehensive L-K functional with modes and delays information, admissibilization conditions are presented in terms of LMIs. Two illustrative examples including an inverted pendulum controlled by a direct current motor (DCMCIP) system are utilized to certify the effectiveness and practicality of the admissibilization technique.

This paper focuses on the practical output tracking control for a category of high-order uncertain nonlinear systems with full-state constraints. A high-order tan-type barrier Lyapunov function (BLF) is constructed to handle the full-state constraints of the control systems. By the BLF and combining a backstepping design technique, an adding a power integrator, and a fuzzy control, the proposed approach can control high-order uncertain nonlinear system with full-state constraints. A novel controller is designed to ensure that the tracking errors approach to an arbitrarily small neighborhood of zero, and the constraints on system states are not violated. The numerical example demonstrates effectiveness of the proposed control method.

This paper reports our study on adaptive tracking control for a mobile-wheeled inverted pendulum with periodic disturbances and parametric uncertainties. With an appropriate reduced dynamic model, incorporating repetitive learning strategies with dynamic decoupling and related adaptive control techniques, a novel controller is successfully constructed to ensure that the output tracking errors of the system will stay within a small neighborhood around zero and all of the other signals are semiglobal uniform bounded. Meanwhile, only one parameter estimation is used for adaptive controller design, which overcomes the problem of over-parametrization. Furthermore, a required condition of period identifier mechanisms is proposed. Finally, detailed simulation results are presented to demonstrate the effectiveness of the proposed control schemes.

This article reports our study on the issue of finite-time adaptive fuzzy tracking control for a class of uncertain nonlinear systems with the prescribed performance. A novel finite-time prescribed performance control approach, in which fuzzy systems are employed to approximate completely unknown nonlinear functions, is proposed by incorporating the technique of prescribed performance control with the method of command filtered design. Based on the finite-time stability theory, all the signals of the closed-loop system can be bounded and the output tracking error can converge to a prescribed small region within a finite-time by the proposed scheme; meanwhile, the problem of complexity explosion can be avoided. The effectiveness of the presented method is verified by two examples.

This paper discusses the problem of adaptive tracking control for full-state constrained nonlinear system with unknown control directions. In view of Nussbaum technology and Barrier Lyapunov function design, an adaptive control scheme is designed to ensure that the tracking error asymptotically converges to zero and the states of the system are always within the given bounds. A numerical simulation example is given to confirm the effectiveness of the designed method.

This paper reports our study on adaptive fuzzy tracking control for flexible-joint robots with full state constraints. In the control design, fuzzy systems are adopted to identify the totally unknown nonlinear functions and can properly avoid burdensome computations. The tan-type barrier Lyapunov functions are used to deal with state constraints so that even without state constraints, the controller is still valid. By combining the method of backstepping design with adaptive fuzzy control approaches, a novel simpler controller is successfully constructed to ensure that the output tracking errors converge to a sufficiently small neighborhood of the origin, while the constraints on the system states will not be violated during operation. Finally, comparison simulations are presented to demonstrate the effectiveness of the proposed control schemes.

In this paper, an adaptive fuzzy controller is constructed to address the finite-time tracking control problem for a class of strict-feedback nonlinear systems, where the full state constraints are strictly required in the systems. Backstepping design with a tan-type barrier Lyapunov function is proposed. Meanwhile, fuzzy logic systems are used to approximate the unknown nonlinear functions. The addressed control scheme guarantees that the output is followed the reference signals within a bounded error, and all the signals in the closed-loop system are bounded. The simulation results demonstrate the validity of the proposed method.