Switching threshold‐based event‐triggered adaptive asymptotic tracking control for stochastic nonlinear systems with full‐state constraints

This study proposes the time-/event-triggered adaptive neural control strategies for the asymptotic tracking problem of a class of uncertain nonlinear systems with full-state constraints. First, we design a time-triggered strategy. The effect caused by the residuals of the estimation via radial basis function (RBF) neural networks (NNs), and the reasonable upper bounds on the first derivative of the reference signal and the derivative of each virtual control, can be eliminated by designing appropriate adaptive laws and utilizing the basic properties of RBF NNs. Moreover, the construction of the barrier Lyapunov functions (BLFs) in this work ensures the compliance of the full-state constraints and also holds the asymptotic output tracking performance. Then, based on the time-triggered strategy, we further design a relative threshold event-triggered strategy. The proposed event-triggered adaptive neural controller can solve the main control objective of this work, that is: 1) the full-state constraint requirements of the system are not violated and 2) the output signal asymptotically tracks the reference signal. Compared with the traditional method, the event-triggered strategy can improve the utilization of communication channels and resources and has greater practical significance. Finally, an example of single-link robot under the proposed two strategies illustrates the validity of the constructed controllers.

Adaptive Tracking Control for Stochastic Nonlinear Systems with Full-State Constraints and Unknown Covariance Noise

Event-triggered adaptive neural control for uncertain nonstrict-feedback nonlinear systems with full-state constraints and unknown actuator failures

In this work, the issue of event-triggered-based asymptotic tracking adaptive control of stochastic nonlinear systems in pure-feedback form with strong interconnections is considered. First, a new decentralized control scheme is developed by introducing the new types of Nussbaum functions, which enables the output of each subsystem to asymptotically track the desired reference signal. Second, the nonaffine structures and the unknown control gains existing in the nonlinear systems are a part of the considered system model, which makes it more complicated to design the decentralized controllers. Therefore, the complexity caused by the nonaffine structures is faciliated by mean value theorem and the unknown control gains are handled by a novel Nussbaum function in our proposed design scheme. Meanwhile, the unknown nonlinearities of the system are approximated by using intelligent control technology. Furthermore, an event-triggered method is introduced in the design process to save communication resources effectively. It is shown that all signals of the closed-loop systems are bounded in probability and the tracking errors asymptotically converge to zero in probability. Finally, the simulation results illustrate the effectivity of the presented scheme.

Event-triggered adaptive optimal tracking control for nonlinear stochastic systems with dynamic state constraints

Event-triggered adaptive asymptotic tracking control of uncertain nonlinear systems with unknown dead-zone constraints

Event-triggered adaptive control for stochastic nonlinear systems with time-varying full state constraints and output dead-zone

Adaptive fuzzy asymptotical tracking control of nonlinear systems with unmodeled dynamics and quantized actuator

This paper investigates the adaptive asymptotic tracking control for networked nonlinear stochastic systems. Different from having the necessity of prior knowledge of the unknown control coefficients in the conventional adaptive control of nonlinear stochastic systems, in this study, the limitation of control coefficients in the stability analysis is relaxed by constructing a new Lyapunov function that contains the lower bounds of the control gain function. By constructing a smooth function with a positive time-varying integral function and utilizing the boundary estimation method, asymptotic tracking control can be guaranteed. At the same time, for nonlinear stochastic systems with unknown control coefficients, a neural adaptive event-triggered strategy that greatly saves communication resources while ensuring system performance is proposed. Finally, simulation results show that the proposed control scheme can guarantee the realization of the control objectives.

This article focuses on the problem of adaptive finite-time tracking control for nonlinear stochastic systems under asymmetric constraints based on dynamic event-triggering control. Different from the existing works, a novel adaptive tracking control algorithm is proposed with asymmetric time-varying constraints and dynamic event-triggering mechanism. First, to constrain the state variable within given time-varying boundaries, a novel predefined-time performance function is constructed. Second, a novel barrier function related to state variable is constructed, by means of which the state variable is directly constrained within the asymmetric time-varying boundaries without the virtual controller. In addition, by establishing a novel dynamic function, we propose a dynamic event-triggering mechanism, and then design controller accordingly, which can reduce computation burdens and save the network resources. By the aid of the Lyapunov stability theory, it is proved that the system tracking error converges to an adjustable bounded set in probability in a finite time and all state variables are successfully constrained into the asymmetric time-varying boundaries. Finally, the effectiveness of the proposed control algorithm is verified by a simulation example.

For a class of stochastic nonlinear systems, this paper proposes a novel event-triggered adaptive control scheme by means of multi-dimensional Taylor network (MTN) approach for the first time, which has the advantages of alleviating computational burden and reducing communication frequency. In addition, the event-triggered control (ETC) strategy can effectively save network resource by alleviating the computational burden and reducing the communication frequency. Therefore, the proposed control approach can not only reduce communication frequency but also further alleviate computational burden, thereby saving network resource to a greater extent. The proposed control scheme ensures that all signals of the system are semi-global uniformly ultimately bounded (SGUUB) in probability and the tracking error can be made arbitrarily small by choosing appropriate design parameters. Meanwhile, Zeno behavior can be avoided. Finally, two simulation results are given to illustrate the effectiveness of the proposed scheme.

In this paper, an adaptive fuzzy output feedback control problem is investigated for a class of stochastic nonlinear systems in which the fuzzy logic systems are adopted to approximate the unknown nonlinear functions. A reduced-order observer and a general fault model are designed to observe the unavailable state variables and describe the actuator faults, respectively. An event-triggered control law is developed to reduce the communication burden from the controller to the actuator. Meanwhile, the barrier Lyapunov functions are constructed to guarantee that all the states of the stochastic nonlinear system are not to violate their constraints. Furthermore, an observer-based adaptive fuzzy event-triggered control strategy is proposed for the full-state-constrained nonlinear system with actuator faults based on backstepping technique, which can guarantee that all the signals in the closed-loop system are bounded and the tracking error converges to a small neighborhood of the origin in a finite time. Finally, simulation results are given to illustrate the effectiveness of the proposed control scheme.

Neural-networks-based adaptive asymptotic tracking control of MIMO stochastic non-strict-feedback nonlinear systems with full state constraints and unknown control gains

This article focuses on the command-filtered-based asymptotic tracking control problem of uncertain nonlinear systems with mismatching disturbances. Different from the previous command filtered control results, the control directions are assumed to be unknown, and a novel logic-based switching method is presented to handle them instead of the classical Nussbaum function method. Besides, some smooth functions are introduced into the virtual controllers to compensate the unknown disturbances. Then, an adaptive tracking controller with dynamic parameter is proposed for the considered nonlinear system. With the aid of the supervisory function, a logic-based switching algorithm is developed to regulate the dynamic controller parameter. Furthermore, it is shown that asymptotic tracking control can be achieved for the considered system with finite switchings. Finally, a simulation example is given to verify the effectiveness of the novel design scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper aims at presenting a novel asymptotic tracking control scheme for uncertain nonlinear systems with unknown control signs and external disturbances, which can model most practical systems such as spacecraft with actuation sign errors. Although considerable command filter control results have been obtained for nonlinear systems, it is still challenging and difficult to realize the command filter asymptotic tracking control of nonlinear system with multiple unknown control signs. To this end, switching functions with dynamic parameter are introduced to design the compensation signals and controller. Besides, an effective switching algorithm is proposed to regulate the dynamic parameters. Based on the proposed control scheme, the command filter control problem can be handled for nonlinear system with multiple unknown control signs. Besides, some smooth functions are also utilized to compensate the mismatching disturbances such that the asymptotic tracking control can be realized. It is worth pointing out that our proposed control method has potential application in practical systems including spacecraft and autonomous underwater vehicles.

In this paper, an adaptive event-triggered asymptotic tracking control problem is addressed for switched nonlinear systems with unknown control directions. In existing control schemes, the proposed controller is not directly aimed at the original system, which affects the control performance. Different from the existing control schemes, based on the original system, an event-triggered control law is constructed in this paper. The proposed event-triggered controller guarantees that the tracking error ς1 asymptotically converges to the origin. Finally, the effectiveness of the proposed controller design scheme is proved by simulation examples.