Research And Application Of Adaptive Inverse Control

The traditional control theory is combined with the signal processing theory to form a novel method for the control problems--Adaptive inverse control. Its realization is based on adaptive filtering technology and design of control configuration. In this paper, based on the elementary adaptive inverse control methods, different methods are designed according to the various objects. Improvements on the methods of adaptive inverse control for linear systems and nonlinear systems, adaptive modeling method and configuration design of adaptive inverse control for multivariable objects are deeply researched and some new ideas are proposed, which enriches and extends the original methods of adaptive inverse control.1. Based on the methods of adaptive inverse control, double loops of disturbance canceling system are designed for the main steam temperature system. Adaptive predictor is based on neural network to solve the delay for the loops of disturbance canceling, and then the output disturbance is instantaneously cancelled of double loops for the main steam temperature system.2. Givens transform is proposed to solve the dimentions problem of Volterra series modeling, which greatly reduces the calculation of the network composed of volterra basic polynomial functions. This improved network is applied to adaptive feedforward control system and adaptive inverse control system with some uncertainties. Simulation results showed that this improved adaptive inverse control system still has satisfying control precision and strong robustness.3. A deadzone inverse compensation of second-order neural network method based on adaptive inverse control is proposed for implemented organization, which realized the dynamic compensation for unknown nonlinear deadzone. This method doesn't require any hypothesis of nonlinear and constraint for deadzone, and realized the compesation strategy composed of estimator and compensator using neural network, which supplies a new solution for nonlinear compensation. Moreover, BP and RBF neural network are used to research on the adaptive inverse control for nonlinear systems.4. The support vector regression is introduced to adaptive inverse control systems. Online identification algorithm of support vector regression is used to build the inverse model for the plant. Due to its favorable generalization, the total system has fine control performance.5. Adaptive inverse control method for Multi-input and multi-output (MIMO) plant based on multi-variable internal model control is proposed in this paper. The configuration design of controller in multi-variable internal model control is combined with multi-variable modeling in MIMO Adaptive inverse control method to be applied into units load system and ball mill system. For the delay multi-variable plant, diagonal matrix compensator and time predictor are used to ball miss system, and the result shows that this method is effective.