Observer-Based Adaptive Neural Network Control for Nonlinear Stochastic Systems With Time Delay

This paper investigates a class of nonlinear time-delayed systems with output prescribed performance constraint. The neural network and DOB (disturbance observer) are designed to tackle the uncertainties and external disturbance, and prescribed performance function is constructed for the output prescribed performance constrained problem. Then the robust controller is designed by using adaptive backstepping method, and the stability analysis is considered by using Lyapunov-Krasovskii. Furthermore, the proposed method is employed into the unmanned helicopter system with time-delay aerodynamic uncertainty. Finally, the simulation results illustrate that the proposed robust prescribed performance control system achieved a good control performance.

show abstract

“…Assumption 1 (see [5]). The ideal tracking signals ( ) and their derivativeṡ( ) are known and continuous.…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

“…The dynamic surface method was presented for the nonlinear time-delay system in [4]. In [5], the nonlinear stochastic system with time delay was studied. The finitetime control method was proposed for a class of time-delay systems in [6,7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Constrained Control for Uncertain Nonlinear Time-Delay System with Application to Unmanned Helicopter

Chen

Yang

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In recent years, some new techniques of construction of a suitable LKF and estimation of its derivative for delayed neural networks (DNNS) and time delay systems have been presented [44][45][46][47][48]. Methods for constructing a dedicated LKF include delaypartitioning idea [12][13][14][15][16][17][18][19][20], triple integral terms [16][17][18][19][20][21][22][23][24][25], more information on the activation functions [26], augmented vector [27,28], etc.…”

Section: Introductionmentioning

confidence: 99%

New delay-dependent stability criteria for recurrent neural networks with time-varying delays

et al. 2015

View full text Add to dashboard Cite

a b s t r a c tThis work is concerned with the delay-dependentstability problem for recurrent neural networks with time-varying delays. A new improved delay-dependent stability criterion expressed in terms of linear matrix inequalities is derived by constructing a dedicated Lyapunov-Krasovskii functional via utilizing Wirtinger inequality and convex combination approach. Moreover, a further improved delay-dependent stability criterion is established by means of a new partitioning method for bounding conditions on the activation function and certain new activation function conditions presented. Finally, the application of these novel results to an illustrative example from the literature has been investigated and their effectiveness is shown via comparison with the existing recent ones.

show abstract

“…neural network have been intensively researched For nonlinear systems in the last two decades [4][5][6][7][8]. There are still no assurance of high convergence speed, the over-heating phenomenon, avoidance of local minima and so on; meanwhile, there are not general methods to choose the number of the fuzzy rule base and hidden units of common neural network.…”

mentioning

confidence: 99%

“…Inspired by the work of dynamic linearization technique of Hou [5][6], we present two adaptive observer-based control strategies for nonlinear processes systems in which the pseudo-partial derivative (PPD) theory is used to dynamically linearize the nonlinear system. In order to achieve the time-varying PPD parameter estimation, based on discrete-time adaptive observer technique, a novel adaptive strategy for computing the PPD term is designed by using the Lyapunov method.…”

mentioning

confidence: 99%

Adaptive Observer Based Data-Driven Control for Nonlinear Discrete-Time Processes

Jiang

Shi

2014

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

Abstract-In this paper, two adaptive observer-based strategies are proposed for control of nonlinear processes using input/output (I/O) data. In the two strategies, pseudo-partial derivative (PPD) parameter of compact form dynamic linearization and PPD vector of partial form dynamic linearization are all estimated by the adaptive observer, which are used to dynamically linearize a nonlinear system. The two proposed control algorithms are only based on the PPD parameter estimation derived online from the I/O data of the controlled system, and Lyapunov-based stability analysis is used to prove all signals of close-loop control system are bounded. A numerical example, a steam-water heat exchanger example and an experimental test show that the proposed control algorithm has a very reliable tracking ability and a satisfactory robustness to disturbances and process dynamics variations.Note to Practitioners-In actual industrial process, the dynamic behaviors is complex and nonlinear, and their mathematical models are often difficult to obtain. How to design the controller for unknown nonlinear systems using input/output (I/O) data has become one main focus of control researches. Therefore, in this paper, two adaptive observer-based data-driven control algorithms are proposed for a class of unknown nonlinear systems. Finally, the effectiveness of two control strategies are illustrated via simulation study and experimental test.Index Terms-Data-driven control, adaptive observer, pseudopartial derivative, Lyapunov-based stability analysis, nonlinear discrete-time systems.

show abstract

Observer-Based Adaptive Neural Network Control for Nonlinear Stochastic Systems With Time Delay

Cited by 328 publications

References 45 publications

Adaptive Constrained Control for Uncertain Nonlinear Time-Delay System with Application to Unmanned Helicopter

Adaptive Constrained Control for Uncertain Nonlinear Time-Delay System with Application to Unmanned Helicopter

New delay-dependent stability criteria for recurrent neural networks with time-varying delays

Adaptive Observer Based Data-Driven Control for Nonlinear Discrete-Time Processes

Contact Info

Product

Resources

About