Stable direct adaptive neural controller of nonlinear systems based on single auto-tuning neuron

In this study, using a model reference adaptation law, a stable fuzzy neural control system is developed. Despite the advantages of Model reference control design technique, which is mainly its power to exactly set trajectories of the system under control, this method is designed for linear system. In this study using fuzzy neural systems, a stable model reference controller for nonlinear systems is developed, Lyapunov method is used to guarantee the stability of fuzzy neural training algorithm and model following of the system under control.

In this paper, output feedback direct adaptive robust NN control is investigated for a class of nonlinear discrete-time systems in strict-feedback form. To construct output feedback control, the original strict-feedback system is transformed into a cascade form, which the output feedback of the nonlinear discrete-time system can be carried out. Then with employment of the inputs and outputs, the output feedback direct adaptive robust NN control is developed. The HONNs is exploited to approximate unknown function, and a stable adaptive NN controller is synthesized. The proposed algorithm improves the rubostness of the discrete-time nonlinear systems. It is proven that all the signals in closed-loop system are uniformly ultimately bounded (UUB). A simulation example is presented to illustrate the effectiveness of the proposed algorithm.Keywordsoutput feedbackrubostnessdirect adaptive NN controldiscrete-time nonlinear system

Sector-condition-based results for adaptive control and synchronization of chaotic systems under input saturation

Globally stable direct adaptive backstepping NN control for uncertain nonlinear strict-feedback systems

The design of stable adaptive neural controllers for uncertain nonlinear dynamical systems with unknown nonlinearities is considered. The Lyapunov synthesis approach is used to develop state-feedback adaptive control schemes based on a general class of nonlinearly parametrized on-line approximation models. The key assumptions are that the system uncertainty satisfies a strict feedback condition and that the network reconstruction error and higher-order terms of the on-line approximator (with respect to the network weights) satisfy certain bounding conditions. An adaptive bounding design is used to show that the overall neural control system guarantees semi-global uniform ultimate boundedness within a neighbourhood of zero tracking error. The theoretical results are illustrated through a simulation example.

This paper presents an adaptive neural flight control design for helicopters performing nonlinear maneuver. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller uses a real-time learning dynamic radial basis function network, which uses Lyapunov based on-line update rule integrated with the neuron growth criterion. The real-time learning dynamic radial basis function network does not require a priori training and also find a compact network for implementation. The proposed adaptive law provide necessary global stability and better tracking performance. The simulation studies are carried-out using a nonlinear desktop simulation model. The performances of the proposed adaptive control mechanism clearly show that it is very effective when the helicopter is performing nonlinear maneuver.

The force tracking error is dependent on both the position tracking error and the estimated parameter error so that, in constrained adaptive robot control, the convergence of the estimated parameter error becomes more important than in unconstrained adaptive robot control. However, in the direct adaptive controller, the parameter adaptation is driven only by the tracking error in the joint motion, while in the indirect adaptive controller, the parameter estimation is driven only by the prediction error in the joint torque. Based on this observation a new combined direct and indirect adaptive controller driven by both the tracking error and the prediction error is developed for constrained robots with inertia parameter uncertainty. Stability of the combined adaptive controllers is established in the Lyapunov sense. The combined control improves both position and force tracking performance compared with direct adaptive control. The computer simulations are presented to demonstrate the proposed schemes.

This paper shows that stable direct adaptive control of an LTI plant is possible using knowledge of only the plant order. The approach is based on periodic control, which relocates system zeros. No prior knowledge of the plant high-frequency gain or relative degree is used, and persistent excitation is not required. More importantly, no assumption or knowledge is required as to whether the plant is minimum or nonminimum phase. >

An adaptive integral plus states neural control of aerobic continuous stirred tank reactor

In the conventional CMAC-based adaptive controller design, a switching compensator is designed to guarantee system stability in the Lyapunov stability sense but the undesirable chattering phenomenon occurs. This paper proposes a CMAC-based smooth adaptive neural control (CSANC) system that is composed of a neural controller and a saturation compensator. The neural controller uses a CMAC neural network to online mimic an ideal controller and the saturation compensator is designed to dispel the approximation error between the ideal controller and neural controller without any chattering phenomena. The parameter adaptive algorithms of the CSANC system are derived in the sense of Lyapunov stability, so the system stability can be guaranteed. Finally, the proposed CSANC system is applied to a Chua’s chaotic circuit and a DC motor driver. Simulation and experimental results show the CSANC system can achieve a favorable tracking performance. It should be emphasized that the development of the proposed CSANC system doesn’t need the knowledge of the system dynamics.

A controller to rotate a rigid body between two successive orientations is designed. Particular features are the fact that it is based on the quaternion approach, known to provide singularity-free attitude description, and it is adaptive in the sense that it does not need specific knowledge of the inertia matrix. Global stability of the overall controller is proved analytically and tested in computer simulations. © 1994 American Institute of Aeronautics and Astronautics, Inc., All rights reserved.

Data-based adaptive neural network optimal output feedback control for nonlinear systems with actuator saturation

Intelligent switching adaptive control for uncertain nonlinear dynamical systems

This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the convergence of the identification error is investigated by Lyapunov's second method. The performance of the proposed scheme is evaluated via simulations and a real time application.

This paper aims to investigate the formation control of multi-robot systems, where the first-order kinematic model of a differential wheeled robot is considered. Based on the graph theory and consensus algorithm, an adaptive neural fuzzy formation controller is designed with the capability of on-line learning. The learning rules of controller parameters can be derived from the analyzing of Lyapunov stability. Simulations are adopted to verify the feasibility of proposed techniques. From simulation results, the proposed adaptive neural fuzzy controller can provide better formation responses compared to conventional consensus algorithm.