Event-triggered Adaptive Tracking Control Research Articles

Event-triggered adaptive tracking control for uncertain nonlinear systems with preassigned prescribed performance constraints under three cases

Event-triggered adaptive tracking control for uncertain nonlinear systems with preassigned prescribed performance constraints under three cases

Event-Triggered Adaptive Tracking Control for Disturbed Nonholonomic Systems With Asymmetric State Constraints

Event-Triggered Adaptive Tracking Control for Disturbed Nonholonomic Systems With Asymmetric State Constraints

Event-triggered adaptive tracking control of a class of nonlinear systems with asymmetric time-varying output constraints

Event-triggered adaptive tracking control of a class of nonlinear systems with asymmetric time-varying output constraints

Dynamic event-triggered adaptive tracking control for stochastic nonlinear systems with deferred time-varying constraints

This article investigates the problem of adaptive tracking control for stochastic nonlinear systems with deferred state constraints. The novel unified universal barrier function (UUBF) and deferred funnel error transformation are developed to handle various state constraints and adjust the tracking error more precisely, respectively. Multi-dimensional Taylor network (MTN) and dynamic event-triggered mechanism (DETM) are employed to design controller in the backstepping process, which can achieve full-state constraints and the tracking control after the preassigned time while conserving more communication resources. Finally, two simulation examples are presented to verify the effectiveness of the proposed control scheme.

Event-triggered Adaptive Tracking Control for Stochastic Nonlinear Systems With State Constraints

Event-triggered Adaptive Tracking Control for Stochastic Nonlinear Systems With State Constraints

Predefined-time event-triggered adaptive tracking control for strict-feedback nonlinear systems with full-state constraints

This paper investigates the problem of predefined-time event-triggered adaptive tracking control for strict-feedback nonlinear systems (SFNSs) with full-state constraints. To handle asymmetric full-state constraints, a nonlinear state-dependent function (NSDF) that purely depends on the constraint state is introduced. Then, a switching threshold event-triggered mechanism (ETM) is designed to enhance usage efficiency of communication resources. Moreover, a predefined-time event-triggered adaptive tracking controller is constructed by incorporating a smooth tuning function into each step of the backstepping design based on dynamic surface technology. Under such a control scheme, the output tracking error can be steered to the small neighborhood of the origin within the user setting time. Compared to various previous control schemes developed for full-state constrained systems, our proposed method can circumvent the demanding feasibility conditions in controller designs. Finally, a simulation result is given to illustrate the effectiveness of the control scheme.

Dynamic Event-Triggered Adaptive Tracking Control for Multiagent Systems with Power Exponential Function Using Fuzzy Disturbance Observer

Dynamic Event-Triggered Adaptive Tracking Control for Multiagent Systems with Power Exponential Function Using Fuzzy Disturbance Observer

Dynamic event-triggered adaptive control for uncertain stochastic nonlinear systems

In this paper, the dynamic event-triggered adaptive tracking control problem is investigated via backstepping technology for uncertain stochastic nonlinear systems. First, the stochastic nonlinear system with unknown parameter is considered. By introducing an additional dynamic variable, a dynamic event-triggered adaptive controller is designed such that the closed-loop signals are uniformly ultimately bounded in the sense of the fourth moment. Then, a more general partially unknown stochastic nonlinear system is further considered, and the designed adaptive neural network control scheme ensures that the closed-loop signals are fourth moment semi-globally uniformly ultimately bounded (SGUUB). The proposed dynamic event-triggering mechanism (DETM) guarantees that the lengths of time intervals between each two consecutive events are lower-bounded by a positive constant. It is necessary to point out that the DETM is better at saving resources than the static event-triggering mechanism (SETM). Finally, two simulations are conducted to show the validity of the control strategies for these two systems, respectively.

Event-triggered adaptive tracking control of uncertain nonlinear systems with prescribed performance and actuator failures

This article studies adaptive prescribed performance tracking control problem for a class of strict-feedback nonlinear systems with parametric uncertainties and actuator failures. Firstly, in order to compensate the multiple uncertainties and eliminate the influence of actuator failure, a new adaptive tracking controller based on first-order filter technology will be proposed, which simplifies the algorithm design process. Then, by introducing an asymmetric state transition function, the transient and steady performances of the output tracking error are both constrained such that the predetermined performance control goal is achieved. Moreover, to reduce the communication burden from the controller to the actuator, the event-triggered mechanism is designed, and there will be no Zeno phenomenon. Based on Lyapunov stability theory, it is strictly proved that output signal can track the reference signal and all the signals of the closed-loop system are bounded. Finally, a simulation example is performed and the results demonstrate effectiveness of the proposed strategy.

Event-Triggered Adaptive Neural Network Tracking Control with Dynamic Gain and Prespecified Tracking Accuracy for a Class of Pure-Feedback Systems

This paper studies the event-triggered adaptive tracking control problem of a class of pure-feedback systems. Via the backstepping method and the neural network approximation with the central symmetric distribution, an event-triggered adaptive neural network controller is designed. In particular, a dynamic gain driven by the tracking error is introduced into the event-triggering mechanism. Then, by using the Lyapunov stability theory, the boundedness of all the closed-loop signals is proved, and the tracking error falls into a prespecified ϵ-neighbourhood of zero. Meanwhile, the Zeno behaviour is avoided. Finally, two simulations verify the effectiveness of the proposed control scheme.

Double-channel event-triggered adaptive tracking control of nonstrict-feedback nonlinear systems

This paper develops a novel way to control nonstrict-feedback nonlinear systems by using only one approximator, namely a neural network (NN) or a fuzzy logic system (FLS), in the first step of backstepping, such that the computational simplicity is ensured. The bounded property of basis functions solves the “algebraic loop” problem. The double-channel event-triggered control (ETC) is completed both in sensor-to-controller (SC) and controller-to-actuator (CA) channels, where the separate triggering conditions are fabricated. To solve the “jumps of virtual control laws (JVCL)” problem bedeviling the backstepping combined with ETC, this paper opens an avenue while guaranteeing the closed-loop stability with the aid of undetermined virtual control laws.

Periodic event-triggered adaptive tracking control design for nonlinear discrete-time systems via reinforcement learning

In this paper, an event-triggered control scheme with periodic characteristic is developed for nonlinear discrete-time systems under an actor–critic architecture of reinforcement learning (RL). The periodic event-triggered mechanism (ETM) is constructed to decide whether the sampling data are delivered to controllers or not. Meanwhile, the controller is updated only when the event-triggered condition deviates from a prescribed threshold. Compared with traditional continuous ETMs, the proposed periodic ETM can guarantee a minimal lower bound of the inter-event intervals and avoid sampling calculation point-to-point, which means that the partial communication resources can be efficiently economized. The critic and actor neural networks (NNs), consisting of radial basis function neural networks (RBFNNs), aim to approximate the unknown long-term performance index function and the ideal event-triggered controller, respectively. A rigorous stability analysis based on the Lyapunov difference method is provided to substantiate that the closed-loop system can be stabilized. All error signals of the closed-loop system are uniformly ultimately bounded (UUB) under the guidance of the proposed control scheme. Finally, two simulation examples are given to validate the effectiveness of the control design.

A novel event-triggered adaptive tracking control framework for a manipulator with aperiodic neural network estimation

PurposeThe purpose of this study is developing the minimum parameter learning law for the weight updating, which reduces the updating of neural network (NN) weight only at triggering instants and makes a trade-off between the estimation accuracy and triggering frequency such that the computing complexity can be decreased. Besides that, a novel “soft” method is first constructed for the control updating at the triggered instants, to reduce the chattering effect of discontinued renewal of control. Addressing to the proposed control and updating method, a novel dead-zone condition with variable boundary about the triggered control signal is derived to ensure the positivity of adjacent execution intervals.Design/methodology/approachIn this paper, to achieve the motion tracking of manipulator with uncertainty of system dynamics and the communication constraints in the control-execution channel, an adaptive event-triggered controller with NN identification is constructed to improve the transmission efficiency of control on the premise of the guaranteed performance. In the proposed method, the NN with intermittent updating is proposed to perform the uncertain approximation with the saved computation, and the triggered mechanism is constructed to regulate the transportation of the signal in the channel of controller-to-actuator.FindingsAccording to the impulsive Lyapunov function, it can be proved that all the signals are semi-global uniformly ultimately bounded, and the positivity of adjacent execution intervals is also guaranteed by the proposed method. In addition, the chattering effect of control updating at the jumping instants can be relieved by the proposed “soft” mechanism, such that the control accuracy and stability can be guaranteed. Experiments on the JACO2 real manipulator are carried out to verify the effectiveness of the proposed scheme.Originality/valueTo the best of the author’s knowledge, this study is firstly to propose a “soft” method to reduce the chattering effect caused by discontinuous updating. Addressing to the updating method designed above, a novel dead-zone condition with variable threshold and boundary is first constructed to ensure the positivity of execution intervals.

Event-Triggered Adaptive Tracking Control for Random Systems With Coexisting Parametric Uncertainties and Severe Nonlinearities

Comparing with traditional stochastic differential equations involving white noise, random differential equations (RDEs) with colored noise are claimed to have more practical meaning. This article considers the event-triggered adaptive tracking control for RDE systems with coexisting parametric uncertainties and severe nonlinearities. Combining a tracking error-based dynamic gain with a relative threshold event triggered control mechanism, the tracking control problem for the random systems is solved without Zeno behavior. The tracking error can be rendered small enough by tuning design parameters. First, a series of adaptive control laws are designed by using backstepping technique. Then, two special cases are considered and the main results are extended to MIMO systems. Finally, a simulation example confirms the validity of the results. To the best of the authors’ knowledge, this article serves as the first attempt of event-based control for RDE systems.

Event-Triggered, Adaptive, Exponentially Asymptotic Tracking Control of Stochastic Nonlinear Systems

This paper investigates the problem of event-triggered, adaptive, asymptotic tracking control for a class of non-strict feedback stochastic nonlinear systems with symmetrical structures and sensor faults. Based on the negative exponential function, the event-triggered adaptive tracking control strategy deals with the problem of exponentially asymptotic convergence for the first time. The radial basis function neural network (RBFNN) mechanism addresses uncertain factors and unknown external disturbances in the system. The developed strategy ensures that all the signals of the closed-loop system are semi-globally uniformly bounded in probability, and that the tracking error can exponentially converge to zero. Finally, a simulation example demonstrates the effectiveness of the proposed method.

Dynamic Event-Triggered Adaptive Tracking Control for a Class of Unknown Stochastic Nonlinear Strict-Feedback Systems

In this paper, the dynamic event-triggered tracking control issue is studied for a class of unknown stochastic nonlinear systems with strict-feedback form. At first, neural networks (NNs) are used to approximate the unknown nonlinear functions. Then, a dynamic event-triggered controller (DETC) is designed through the adaptive backstepping method. Especially, the triggered threshold is dynamically adjusted. Compared with its corresponding static event-triggered mechanism (SETM), the dynamic event-triggered mechanism (DETM) can generate a larger execution interval and further save resources. Moreover, it is verified by two simulation examples that show that the closed-loop stochastic system signals are ultimately fourth moment semi-globally uniformly bounded (SGUUB).

Event-triggered adaptive control of a class of nonlinear systems with non-parametric uncertainty in the presence of actuator failures

This paper deals with event-triggered adaptive tracking control of a class of nonlinear systems with non-parametric uncertainty and unknown control input direction, in the presence of actuator faults. The proposed event-triggered control method takes advantage of the radial basis function neural networks to approximate the non-parametric uncertainties. Moreover, this control method benefits from the Nussbaum-type function-based adaptation laws for simultaneously dealing with unknown input direction and actuator faults. Numerical simulation results confirm the efficiency of the proposed control method to confront the above mentioned limitations.

Event-Triggered Adaptive Fuzzy Tracking Control With Guaranteed Transient Performance for MIMO Nonlinear Uncertain Systems

This paper investigates an event-triggered adaptive tracking control problem of multi-input and multi-output (MIMO) triangular structure nonlinear systems with nonparametric uncertainties. The implementation of this paper can be roughly classified into two steps: 1) solving the existing rate-limited communication constraints and 2) guaranteeing perfect tracking control performance. By using the relative threshold event-triggered strategy, the communication resource constraint is availably resolved, while the Zeno behavior can be avoided. In addition, by constructing a series of novel Lyapunov functions, an effective adaptive fuzzy control method is developed. The proposed fuzzy control scheme contains fewer calculations by the operation of addressing the square of the norm of the fuzzy weight vector for the entire MIMO system. It is proved that all of the closed-loop signals are global bounded, and the proposed method is not only capable of guaranteeing output tracking performance but is also available to ensure preserved transient performance. Simulation studies show the effectiveness of our approach and verify our established theory.

Novel adaptive neural networks control with event-triggered for uncertain nonlinear system

In this paper, the issue of developing an event-triggered adaptive tracking controller for a class of uncertain nonlinear system is investigated. The radial basis function neural networks (RBFNN) is employed to approximate the uncertain parts, where the time-varying approximation errors are combined. However, it causes the dimensionality of RBFNN’s weight vector larger, which means more network resources are needed. It is a tough task to develop an adaptive tracking controller for nonlinear systems suffered network resources constraint. To save network resources, an event-triggered scheme is developed. Then, with the aid of adaptive backstepping technique, an event-triggered adaptive tracking control approach is established. With the developed event-triggered adaptive tracking controller, the boundedness of all signals in the closed-loop system can be guaranteed. Moreover, it can achieve the balance of tracking performance and the utilization of network resources. Finally, two simulation examples are given to verify the effectiveness of the proposed control scheme.

Event-triggered adaptive consensus tracking control for nonlinear switching multi-agent systems

This paper focuses on a class of nonlinear switching multi-agent systems about event-triggered adaptive tracking control. Compared with existing results, the event-triggered mechanism is first employed in nonlinear switched multi-agent systems with unknown parameters, and it can reduce the amount of computation and the frequency of the controller updates. By utilizing the backstepping technology, adaptive laws are designed to analyze the unknown parameters. In the sequel, all signals of multi-agent systems are bounded, the tracking errors converge to a small neighborhood of the origin. Finally, simulation results verify the effectiveness of the proposed scheme.