Neural Network Based Adaptive Control Theory And Its Application

In many practical engineering problems, the model of the controlled system vary with the change of time and working environment. If the parameters of the controlled system vary in large range, the controlling performance will be badly destroyed. Moreover, it leads to instability. So, it is important to make the system be adaptive, that is, the system can change the control parameter or control action according to the change of parameter or control index such that the system work at the best state. Proper speaking, many systems in control engineering are nonlinear and linear system is only a special case of it. Because of the complexity of nonlinear and the uncertain in the system, the study of nonlinear system is very difficult It is important to seek new tools and new approach to study nonlinear system." In mis thesis, the problem of adaptive control for affine nonlinear system is studied based on the global approximation of neural network. Based on Lyapunov stability theory, the on-line algorithm of network weights is derived and guarantees the global stability of system. The proposed approach overcome the disadvantages which the neural network need learning off-line before using as a controller and lack of real-time and can not guarantee stability of the system, hi order to eliminate the extern disturbance and approximation error of network, the controller design combine with robust control approach and the system possess robustness. At the same time, the application of the proposed approach is considered in this paper, that is, in robotic control and synchronization of chaotic system, the main results contain:Deep investigations on adaptive tracking problem for a class of single input single output affien nonlinear system. The main results contain: (1) An adaptive sliding mode control approach is proposed for a class of unknown SISO affine nonlinear system based on neural network. In this approach, the neural network is used to learning the nonlinear function of the system. The network weights are derived using Lyapunov-based design and are adapted on-line. Due to the existence of neural network approximation error and external disturbance, thesliding mode control which is insensitive to disturbance and parameter pertabation is used to achieve robust tracking for the system. (2) An adaptive Hx control approach is proposed for a class of unknown SISO affine nonlinear system based on neural network. This approach overcomes the disadvantages of chattering of the sliding mode control approach. The control law is continue and the system possess Hx tracking performance. (3) An adaptive Ha control approach is proposed for a class of uncertain nonlinear system. The neural network is used to learn the error between the real model and the nominal model. This approach relax the restriction of matching condition that geometric control approach need. Meanwhile, the learning burden is cut down and speed the learning process, so, the control law is simple is easy to apply to engineering.The adaptive tracking problem is studied for a class of MIMO affine nonlinear system. In this section, the main work is that some main results of SISO is extended.The application problem of neural network based adaptive control theory is studied for real model. First, n-degree of freedom robotic manipulator control problem is studied. On the basis of computed torque (CT), the uncertainty is learned by the neural network, and the output of the neural network is used as compensator. This approach overcome the deterioration of control performance due to the uncertainties including working circumstance and load change. Second, the synchronization of two chaotic system that exposure to disturbances is considered. Robust adaptive synchronization is achieved using neural network for two chaotic systems. The results show that the approach proposed in this section can overcome the effectively the disruption of perturbation.