Adaptive Inverse Control Based On Neural Network Research

The goal of control theory is to make a given dynamic system (the "plant") behave in a user-specified as accurately as possible. This objective may be broken down into three separate tasks: stabilization of the plant dynamics; control of plant dynamics; and control of plant disturbance. Conventionally, one uses feedback to treatall three problems simultaneously. Compromises are necessary to achieve good solutions.Adaptive inverse control is a method to treat the three control tasks separately. First, the plant is stable or the plant is stabilized; secondly, the plant is controlled using a feedforward controller, thirdly, a disturbance canceller is used to reject plant disturbances. Adaptive filters are used as controller and disturbance canceller, and algorithms adapt the transfer functions of the filters to achieve excellent control respectively.Concretely, adaptive inverse control is an open loop control toward dynamic characteristic of system by acting inverse of plant transfer function as series controller, so it avoids instability as a result of feedback. At the same time, because the control of dynamic characteristic of system and plant disturbance is to be treated separately and that has not influence mutually. It is an important trait. Moreover this controller is adaptive and regulates collective dynamic response of plant and controller to achieve excellent control. Feedback is used in control, but it is just limited to adaptive process. Adaptive inverse control dominates alterable parameters rather than signal flow in system not so as conventional control.The research of adaptive inverse control mainly concentrated on modeling and control by utilizing the way of signal process especially Wiener filter theory ago. This method is fairly successful to research linear system. But it is not an ideal method for nonlinear system. Especially toward the plant without exactly mathematic model, it is difficult to exact inverse model for conventional method. However neural network with natural parallel structure shows huge advantage in the field of automatic control with high real-time. Consequently it promotes the more development of inverse control in the design of control system and regulator.Author introduces the advantage of neural network in modeling and control ofnonlinear system into adaptive inverse control. Consequently the paper puts forward a structure of neural network adaptive inverse control based on plant-positive model-inverse model modeling. Feedforward neural network based on BP algorithm constructs identification of plant and inverse controller. For different plants with the variety of circumstance and parameter, a mass of simulations testify the project structure of control to be reasonable and effective, besides strong robustness. At the same time the stabilization of adaptive inverse control system based on neural network is discussed and analyzed simply. Ultimately several problems deserved attention are analyzed and summarized.Adaptive inverse control based on neural network is a method of non-model direct adaptive control system constituted by neural network. It has an advantages of low modek good robustness , strong adaptation and is applicable for the control of digital computer. It may acquire the better control quality if adaptive inverse control based on neural network applies to industrial process control.