scholarly journals Event-driven NN adaptive fixed-time control for nonlinear systems with guaranteed performance

2021 ◽  
Author(s):  
Xiaona Song ◽  
Peng Sun ◽  
Shuai Song
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Tracking Error ◽  
Fixed Time ◽  
Dynamic Surface Control ◽  
Dynamic Surface ◽  
Filter Performance ◽  
Time Control ◽  
The Impact ◽  
Compensation Signal

Abstract This article investigates the adaptive neural network fixed-time tracking control for a class of strict-feedback nonlinear systems with prescribed performance demands, in which radial basis function neural network (RBFNN) is utilized to approximate the unknown items. First, an improved fractionalorder dynamic surface control (FODSC) technique is incorporated to address the issue of the iterative derivation, where a fractional-order filter is adopted to improve the filter performance. What's more, the error compensation signal is established to remove the impact of filter error. Furthermore, a fixed-time adaptive event-triggered controller is constructed to reduce the communication burden, where the Zeno-behavior can also be excluded. Stability results prove that the designed controller not only guarantees all the signals of the closedloop systems (CLS) are practically fixed-time bounded, but also the tracking error can be regulated to a predefined boundary. Finally, the feasibility and superiority of the designed control algorithm are verified by two simulation examples.

scholarly journals Event-Triggered Fixed-time Adaptive Fuzzy Control for State-constrained Stochastic Nonlinear Systems Without Feasibility Conditions

2021 ◽  
Author(s):  
Yangang Yao ◽  
Jieqing Tan ◽  
Jian Wu ◽  
Xu Zhang
Keyword(s):  
Nonlinear Systems ◽  
Adaptive Fuzzy Control ◽  
Fixed Time ◽  
Dynamic Surface Control ◽  
Control Technique ◽  
Stochastic Nonlinear Systems ◽  
Stable Theory ◽  
Dynamic Surface ◽  
Time Control ◽  
Event Triggered

Abstract The problem of event-triggered fixed-time control for state-constrained stochastic nonlinear systems is discussed in this article. Different from the Barrier Lyapunov Function (BLF)-based and Integral BLF-based schemes which rely on feasibility conditions (FCs), by introducing nonlinear state-dependent functions , the asymmetric time-varying state constraints are handled without FCs .Combining with the fixed-time stable theory and dynamic surface control technique with fixed-time filter, the fixed-time stability in probability of the closed-loop system is ensured and the problems of “explosion of complexity” and “singularity” are overcome. Furthermore, the novel fixed-time error compensation signals are designed to compensate filtering errors, and event-triggered control technique is used to save network resources. Simulations also illustrate the effectiveness of the proposed method.

scholarly journals Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

2021 ◽  
pp. 002029402110211
Author(s):  
Tao Chen ◽  
Damin Cao ◽  
Jiaxin Yuan ◽  
Hui Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Mode Control ◽  
The Stability

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

scholarly journals Adaptive Dynamic Surface Control for Generator Excitation Control System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhang Xiu-yu ◽  
Liu Cui-ping ◽  
Wang Jian-guo ◽  
Lin Yan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Tracking Error ◽  
Dynamic Surface Control ◽  
Excitation Control ◽  
Dynamic Surface ◽  
Adaptive Dynamic ◽  
Surface Control ◽  
Control Scheme ◽  
Generator Excitation Control

For the generator excitation control system which is equipped with static var compensator (SVC) and unknown parameters, a novel adaptive dynamic surface control scheme is proposed based on neural network and tracking error transformed function with the following features: (1) the transformation of the excitation generator model to the linear systems is omitted; (2) the prespecified performance of the tracking error can be guaranteed by combining with the tracking error transformed function; (3) the computational burden is greatly reduced by estimating the norm of the weighted vector of neural network instead of the weighted vector itself; therefore, it is more suitable for the real time control; and (4) the explosion of complicity problem inherent in the backstepping control can be eliminated. It is proved that the new scheme can make the system semiglobally uniformly ultimately bounded. Simulation results show the effectiveness of this control scheme.

Multi-dimensional Taylor network modelling and optimal control of SISO nonlinear systems for tracking by output feedback

2019 ◽  
Vol 37 (3) ◽  
pp. 699-717 ◽  
Author(s):  
Qi-Ming Sun ◽  
Hong-Sen Yan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Systems ◽  
Real Time ◽  
Output Feedback ◽  
Tracking Error ◽  
Real Time Control ◽  
Network Modelling ◽  
Single Input Single Output ◽  
Time Control

Abstract In this paper, a multi-dimensional Taylor network (MTN) output feedback tracking control of nonlinear single-input single-output (SISO) systems in discrete-time form is studied. To date, neural networks are generally used to identify unknown nonlinear systems. However, the neuron of neural networks includes the exponential function, which contributes to the complexity of calculation, making the neural network control unable to meet the real-time requirements. In order to identify the controlled object whose model is unknown, the MTN, which requires only addition and multiplication, is utilized for successful real-time control of the SISO nonlinear system based on only its output feedback. Lyapunov analysis proves that output signals in the closed-loop system remain bounded and the tracking error converges to an arbitrarily small neighbourhood around the origin. In contrast to the back propagation (BP) neural network self-adaption reconstitution controller, the edge of the scheme is that the MTN optimal controller promises desirable response speed, robustness and real-time control.

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