Command Filter Technique Research Articles

Adaptive Predefined‐Time Fuzzy Control for Nonlinear Systems Subject to Unknown Control Direction and Mismatched Disturbances Based on Command Filter Technique

ABSTRACTThis article proposes an adaptive predefined‐time event triggered fuzzy tracking control strategy for strict‐feedback nonlinear systems with unknown control direction and mismatched disturbances by applying command filters. The Nussbaum‐type function is employed to tackle the unknown control direction problem, and the fuzzy logic systems (FLSs) are applied to model the nonlinear functions. And then, command filters are adopted to deal with the problems of complexity explosion, and the event‐triggered technique is exploited to conserve communication resources. With the help of backstepping mechanism and predefined‐time stability theory, the constructed adaptive control scheme is proposed to ensure all signals in the closed‐loop system are bounded and that the tracking error converges to a residual set. Finally, a simulation example proves the validity of presented control mechanism.

Event-Triggered Adaptive Neural Control for MIMO Nonlinear Systems With Rate-Dependent Hysteresis and Full-State Constraints via Command Filter.

This article presents an event-triggered adaptive acrlong NN command-filtered control for a class of multi-input and multi-output (MIMO) nonlinear systems with unknown rate-dependent hysteresis in the actuator and the constraints on full states. The acrlong ETM is used to reduce the communication frequency between controller and actuator. The command filter technique is first employed to solve the dilemma between the nondifferentiable control signal at triggering instants and rate-dependent hysteresis input premise while avoiding the "explosion of complexity" problem. During the backstepping design, the barrier Lyapunov functions are utilized to guarantee that system states will stay in certain regions and the unknown nonlinear items are approximated by adaptive neural networks. The compensating signals are constructed to eliminate filtering errors. The estimates of unknown hysteresis parameters are updated by adaptive laws. The stability analysis is given and the effectiveness of the proposed method is verified by simulation.

Adaptive command filtered control for a class of nonlinear time delay systems with extreme learning machine

AbstractIn this paper, an extreme learning machine (ELM)‐based adaptive command filtered control method is investigated for a class of strict feedback nonlinear systems with unknown external disturbances and time delay. To begin with, the original system state equation is transformed into a new form on account of coordinate transformation. Subsequently, an ELM is adopted to approximate unknown functions which exist in the whole system states, without any prior knowledges of the ideal weight vectors and approximation errors. Secondly, a command filter is developed for the system under consideration, which can avoid the probem of “explosion of complexity” caused by repeated derivation of virtual control signals in traditional backstepping control. Meanwhile, error compensation signals are designed to conquer the shortcoming of a dynamic surface control (DSC) method. The combination of ELM and command filter technique has been used to construct corresponding controllers and adaptive laws. Effective handling of the impact of time delay terms is ensured through designing a novel Lyapunov–Krasovskii functional. The proposed strategy guarantees boundedness of all signals in the closed‐loop system and the tracking error asymptotically converges to a compact set around the origin. In the end, two continuous stirred tank reactors (CSTRs) are taken as example to further verify the efficiency of the put forward control method.

Command filter-based adaptive neural tracking control of nonlinear systems with multiple actuator constraints and disturbances

In this paper, the adaptive practical finite-time tracking control problem for a class of strictly feedback nonlinear systems with multiple actuator constraints is investigated using backstepping techniques and practical finite-time stability theory. The effects of deadband and saturated nonlinear constraints on the controller design of nonlinear systems are addressed by the equivalent transformation method. The problem of complexity explosion due to the derivatives of virtual control signals is solved by using the virtual control signals as inputs to the command filters and using the outputs of the command filters to perform the corresponding control tasks. An adaptive neural network tracking backstepping control strategy based on the command filter technique and the backstepping design algorithm is proposed by approximating an unknown nonlinear function using a neural network. The control strategy ensures the boundedness of all variables in the closed-loop system, and the output tracking error fluctuates in a small region near the origin. Finally, simulations verify the effectiveness of the control strategy designed in this paper.

Decentralised adaptive neural finite-time prescribed performance control for nonlinear large-scale systems based on command filtering

In this research, the issue of decentralised adaptive neural finite-time prescribed performance control is discussed for nonstrict-feedback large-scale nonlinear interconnected systems subject to dead zones input and unknown control direction. The obstacle of ‘explosion of complex’ occurred in conventional backstepping design can be surmounted by adopting the command filter technique and nonlinearities are approximated by introducing an adaptive neural control approach. To handle the obstacles due to unknown directions and unknown interconnections, Nussbaum-type functions and two smooth functions are used and designed. Meanwhile, error compensation signals are introduced to deal with the problem associated with the dynamic surface method. To constraint the output tracking error within a predefined boundary in finite time, an improved performance function, i.e. finite-time performance function is introduced. Different from existing control results, the developed control methodology does not require any information on the boundedness of dead-zone parameters. It is further proved that the constructed controller not only assures the semi-global boundedness of all the controlled system signals, but also makes the output tracking errors reach within a predefined small set. Finally, both numerical and practical examples are supplied to further validate the effectiveness of the presented theoretic result.

Event-triggered finite-time neural control for uncertain nonlinear systems with unknown disturbances and its application in SVC

In this article, an event-triggered finite-time neural control strategy is proposed for nonlinear power systems with unknown disturbances and static var compensator (SVC). We first transform the power system with SVC into a three-dimensional uncertain nonlinear system and then extend it to an [Formula: see text]-dimensional uncertain nonlinear system. The disturbance observer is established to estimate external disturbances and the unknown nonlinear terms are approximated by the radial basis function neural networks. Moreover, to avoid the complexity explosion problem in the traditional backstepping method, the command filtering technique is adopted, and the error caused by the command filters is compensated. The adaptive event-triggered finite-time controller ensures that all signals are bounded in finite time and excludes Zeno phenomena. In the end, the simulation for the two-area interconnected power system with SVC is presented to verify the availability and feasibility of the proposed approach.

Neural networks-based adaptive command filter control for nonlinear systems with unknown backlash-like hysteresis and its application to single link robot manipulator

<abstract><p>In this paper, an adaptive neural network control problem for nonstrict-feedback nonlinear systems with an unknown backlash-like hysteresis and bounded disturbance was presented. Radial basis function neural networks (RBFNN) were used to approximate the unknown functions and the problem of the explosion of complexity problem was handled by utilizing the command filter method. Furthermore, the influence of an unknown backlash-like hysteresis input was addressed by approximating an intermediate variable. Based on the backstepping method and the command filter technique, an adaptive neural network controller was designed via the approximation abilities of RBFNN. With the help of the Lyapunov stability theory, the proposed controller ensures that all of the signals in closed-loop systems are bounded and that the tracking error fluctuates close to the origin within a bounded area. Finally, a real-world example based on the single-link manipulator was shown to demonstrate the viability of the presented approach.</p></abstract>

Distributed Adaptive-Neural Finite-Time Consensus Control for Stochastic Nonlinear Multiagent Systems Subject to Saturated Inputs.

In this article, the problem of distributed finite-time consensus control for a class of stochastic nonlinear multiagent systems (MASs) (with directed graph communication) in the presence of unknown dynamics of agents, stochastic perturbations, external disturbances (mismatched and matched), and input saturation nonlinearities is addressed and studied. By combining the backstepping control method, the command filter technique, a finite-time auxiliary system, and artificial neural networks, innovative control inputs are designed and proposed such that outputs of follower agents converge to the output of the leader agent within a finite time. Radial-basis function neural networks (RBFNNs) are employed to approximate unknown dynamics, stochastic perturbations, and external disturbances. To overcome the complexity explosion problem of the conventional backstepping method, a novel finite-time command filter approach is proposed. Then, to deal with the destructive effects of input saturation nonlinearities, the finite-time auxiliary system is designed and developed. By mathematical analysis, it is proven that the mentioned MAS (injected by the proposed control inputs) is semiglobally finite-time stable in probability (SGFSP) and all consensus tracking errors converge to a small neighborhood of the zero during a finite time. Finally, a numerical simulation onto a group of four single-link robot manipulators is carried out to illustrate the effectiveness of the suggested control scheme.

Adaptive Fuzzy Command Filtered Tracking Control for Flexible Robotic Arm with Input Dead-Zone

In this paper, an adaptive fuzzy tracking control method is proposed to address the issues of dead-zone and unobservable states in a flexible robotic arm system. The control design process begins with the utilization of a fuzzy logic system to approximate the nonlinear functions present in the flexible robotic arm system. To estimate the unobservable states of the system, a state observer is then designed. To alleviate the computational complexity during controller design, the command filtering technique is introduced. Additionally, the Nussbaum function is incorporated to address the unknown control gain problem. The stability of the system can be verified through the design of a Lyapunov function. This study’s simulation results demonstrate that the designed control system can closely track the specified reference signals. The closed-loop system effectively controls the flexible robotic arm, as verified through experimentation.

Adaptive neural practical fixed‐time command filtered control for multi‐input and multi‐output nonlinear systems with dead zones input and unknown control direction

AbstractIn this article, under the circumstance of dead zones input and unknown control direction, the adaptive practical fixed‐time control strategy is presented for a general class of multi‐input and multi‐output (MIMO) nonlinear systems. The inherent explosion of computational complexity difficulty is eliminated by adopting a command filter technique and the universal approximation properties of radial basis function neural networks (RBFNNs) are applied to model the unknown nonlinear functions. The difficulties of the dynamic surface method and unknown directions can be handled by invoking error compensation mechanism and Nussbaum‐type functions, respectively. The uniqueness of the presented control scheme is that the tracking system can achieve the fixed‐time stability without relying on the boundedness of dead‐zone parameters. The fixed‐time convergence of the output tracking error and the semiglobally fixed‐time stable of closed‐loop system are assured via the developed adaptive fixed‐time command filtered controller. Finally, a practical example is supplied to further validate the availability of the presented theoretic result.

Event-trigger-based composite adaptive fuzzy control for nonlinear time-varying state constraint systems with asymmetric input saturation

In this paper, a novel composite adaptive fuzzy control (AFC) based on the command filtered backstepping and event-triggered mechanism is proposed for nonstrict-feedback nonlinear systems with input saturation and time-varying full-state constraints. A fuzzy observer is designed to estimate the unmeasured states. Simultaneously, by introducing a serial-parallel estimation model, a fuzzy control tactic with the composite adaptive law of parameters is established which greatly increases the precision of unknown term estimation. In addition, the command filtering technique is employed to solve the issue of “explosion of complexity” and compensate for the errors caused by the command filters. The event-triggered mechanism is utilized to effectively decrease the data redundancy. By using the time-varying barrier Lyapunov functions (BLFs), all the states can remain within a prescribed time-varying interval. The designed controller can guarantee the convergence of the tracking error at the origin and the boundedness of all closed-loop signals. Finally, two simulation examples are carried out to validate the effectiveness of the proposed control scheme.

Fuzzy-Model-Based Dynamic Event-Triggered Control in Sensor-to-Controller Channel for Nonlinear Strict-Feedback System via Command Filter

This paper considers the situation of sensors transmit plant states to controller in a dynamic event-triggered manner and develops a fuzzy-model-based adaptive command filtered tracking control method for nonlinear strict-feedback systems with uncertain dynamics. Firstly, the dynamic event-trigger rules are designed and implemented in sensor-to-controller channel to reduce communication frequency in network controlled system. Then, the adaptive fuzzy-model is designed to generate approximated states for controller during the event-trigger intervals to avoid an open-loop like system operation which can be caused by the traditional zero-order-hold policy. Moreover, command filter technique is incorporated to solve the issue of “jumping of virtual controller” and circumvent “explosion of complexity” problem during the fuzzy-model-based event-triggered controller design process. Meanwhile, the filtering errors are eliminated by compensate signals. Finally, two simulation examples are conducted and the results show the effectiveness and superiority of the proposed method.

Command filtered backstepping control of constrained flexible joint robotic manipulator

AbstractHere, an adaptive radial basis function (RBF) neural network (NN) backstepping controller is proposed for a class of input‐constrained flexible joint robotic manipulators represented by strict‐feedback form with unknown terms, external stochastic disturbance, and output disturbance. The proposed approach is robust against both deterministic and stochastic uncertainties and disturbances and copes with the control input amplitude saturation. Moreover, by deploying the minimal learning parameter method and command filter technique, the computational burden of derivative terms and adaptive terms greatly decreases. Considering the mean‐value theorem assists us to avoid the need for having the input saturation bounds in prior. The suggested tracking control scheme mandates the closed‐loop system states to be semi‐globally bounded‐in‐probability. Also, a quartic Barrier Lyapunov function is utilized to force the tracking error to be confined within a pre‐chosen small region around the origin. Eventually, a numerical simulation of a flexible joint robot manipulator with a single link is performed to show the effectiveness and performance of the developed control method.

Event-Based Finite-Time Control for Nonlinear Multiagent Systems With Asymptotic Tracking

In this paper, an adaptive neural finite-time event-triggered consensus tracking problem is studied for nonlinear multi-agent systems (MASs) under directed graphs. Firstly, the unknown nonlinear functions of MASs can be approximated by neural networks. Then, a distributed adaptive event-triggered control (ETC) scheme is proposed via command filter and backstepping technique. The newly designed control scheme can not only circumvent the problem of the explosion of complexity, but also remove the singularity issue typical of conventional backstepping technique. In the meanwhile, an event-triggered mechanism with a dynamic threshold is devised to reduce the waste of network resources. Moreover, by using a novel finite-time stability criterion, it can be proved that the closed-loop system is finite-time stable and the consensus tracking errors can reach zero as time approaches to infinity. Finally, a numerical example is given to validate the feasibility of the proposed scheme.

Fixed-Time Disturbance Observer-Based Distributed Formation Control for Multiple QUAVs

In this article, the problem of fixed-time distributed formation control for multiple quadrotor unmanned aerial vehicles (QUAVs) suffering from external disturbances is investigated. Resorting to the fixed-time disturbance observer (FTDO), the unknown disturbances are accurately estimated without requiring the information of initial observer errors. Based on the fusion of command filter technique, nonsmooth error compensation mechanism and backstepping design method, the proposed singularity-free distributed formation controllers ensure that the whole closed-loop system is practical fixed-time stable, and the formation tracking errors converge to a small neighbourhood of the zero in a fixed time. Finally, a numerical simulation reveals the validity of the devised distributed formation control scheme.

Command Filter-Based Adaptive Practical Prescribed-Time Asymptotic Tracking Control of Autonomous Underwater Vehicles With Limited Communication Angles

This work proposes a command filter-based adaptive practical prescribed-time (PPT) asymptotic tracking control scheme for autonomous underwater vehicles (AUVs) under dynamic uncertainties. Considering that the followers have limited communication angles in the leader–follower formation structure, where a novel controller is proposed to ensure communication angles to achieve preassignable precision within a predefined time. First, we present a PPT control for handling the limited communication angles and, thus, they are allowed to be limited, turnable, and controllable. Then, to achieve the asymptotic convergence of output tracking errors, the proposed adaptive controllers can effectively insure the trajectory tracking errors of AUVs asymptotically converge to zero under the influence of dynamic uncertainties. Furthermore, the derived asymptotically tracking command filter technique for AUVs not only deduce an acceleration-free strategy for followers but also achieve the asymptotic convergence of output tracking errors for the command filters themselves. In the end, the effectiveness of the proposed control strategy is demonstrated through stability analysis and simulation test.

Neural adaptive optimal control for nonlinear multiagent systems with full-state constraints and immeasurable states

In this paper, a neural adaptive optimal control strategy is proposed for strict-feedback nonlinear multiagent systems (MASs) with full-state constraints and immeasurable states. In order to solve the Hamilton–Jacobi-Bellman (HJB) equation, the reinforcement learning (RL) is employed with the actor-critic architecture. Different from the existing results for the optimized backstepping technique, by introducing the command filter technique into the value function, the condition that the derivative of the virtual controller is bounded by a constant can be released. Moreover, in the case of considering full-state constraints and immeasurable states, the tracking control problem of MASs can be solved without violating constraints, and the resource consumption can be reduced. We estimate and limit the states of MASs by employing the state observer and the novel mapping function, respectively. By using the Lyapunov stability theorem, it verifies that all signals in the closed-loop system are uniformly ultimately bounded (UUB) and the tracking error converges to a small neighborhood of the origin. Finally, a simulation example is given to illustrate the validity of the proposed control strategy.

Command-Filtered Neuroadaptive Output-Feedback Control for Stochastic Nonlinear Systems With Input Constraint.

In this article, an adaptive neural-network (NN) command-filtered output-feedback control strategy is proposed for a class of stochastic nonlinear systems (SNSs) with the actuator constraint. The problem of "explosion of complexity" existing in the conventional backstepping design procedure for SNSs is successfully resolved based on the command filter technique, and the error compensation mechanism is introduced to remove effectively the influence of filtered error. By using the NNs to identify the unknown nonlinear functions, a neural-network-based state observer is designed to estimate the unmeasurable states of the SNSs. Based on the quartic Lyapunov function, the stability of stochastic closed-loop systems is analyzed. It is proved that all signals of the closed-loop systems are bounded in probability, and the tracking error approaches a small neighborhood of the origin in probability. Finally, the effectiveness of the developed control algorithm in this article is verified by a comparison example.

Observer-based event-triggered adaptive neural control for time-delay nonlinear systems with input saturation and external disturbances

In this article, the issue of adaptive event-triggered tracking control is investigated for time-delay nonlinear systems with input saturation and external disturbances. In the whole process of control design, the radial basis function neural networks are utilized to approximate uncertain nonlinearities. To estimate the unknown states, a neural network-based observer is constructed. Pade approximation method is adopted to eliminate the effect of input delay. A smooth non-affine function is introduced to replace input saturation, and an auxiliary variable is employed to obtain the actual control input. An event-triggered strategy is designed to reduce the utilization of communication and computation resources. Moreover, the command filtering technique is applied to handle the issue of “explosion of complexity” in the conventional backstepping method. The designed controller can assure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Therefore, the proposed event-triggered neural control scheme can not only save network resources, but also improve the robustness of the system by dealing with input constraints and external disturbances. Finally, two simulation examples are given to verify the availability and feasibility of the designed controller.

Event-Triggered Adaptive Output Feedback Control for Stochastic Nonlinear Systems With Time-Varying Full-State Constraints

This brief investigates the issue of event-triggered adaptive output feedback control for stochastic nonlinear systems with time-varying full-state constraints. Firstly, the unmeasurable states are estimated by the fuzzy observer. Secondly, the quartic time-varying barrier Lyapunov functions are constructed to avoid the violation of time-varying constraints. Thirdly, the command filter technique and error compensation mechanism are incorporated into the controller design to get over the issue of “explosion of complexity” and compensate the filtered errors. Then, the event-triggered mechanism is introduced to improve the efficiency in resource utilization. It is shown that the tracking error can converge to the desired neighborhood of the origin and all the signals in the closed-loop systems are bounded. Finally, the validity of the control strategy is demonstrated by a physical example.