The Research On System Identification And Predictive Control Of The Non-linear System Based On AIS And Its Applications

Based on the immune principium, two significant fields concerning about nonlinear system control: the nonlinear system model identification and the predictive control are discussed in this dissertation. The immune system is emerging as a distributed dynamic robust control system with powerful self-study ability and more and more researchers attach importance to the artificial immune algorithm based on the immune principium, which is widely employed in numerous fields. Although with finite categories, the antibody in immune system is able to identify the antigen which is of multifarious categories and continuous evolution. The mechanism, which the antibody identifies the antigen, verifies that the immune system is of powerful self-study, self-organizing and favorable self-adaptive performances which are needed by normal system identification. Diversified optimization algorithms based on the immune principium are global optimization algorithms with excellent performances which are fit for diversified optimization problems such as rolling optimization in predictive control. The main research content of this thesis and contribution are below:(1) A coding method for antibody structure and an immune optimization algorithm based on the structure coding are proposed here, which are applied to nonlinear system structure identification. This algorithm is based on the principium of immune system and encodes the nonlinear response model of the antibody as a dynamic structure tree, which can be utilized in the immune optimization for nonlinear problems through the immune operations such as clone, selection, mutation and crossover on the structure tree. The structure identification for nonlinear system can be achieved with the algorithm.(2) A hybrid coding method for antibody and an immune optimization algorithm based on the hybrid coding method are proposed here which are employed in the incorporate identification for the structure and parameters of nonlinear system model. The structure of the nonlinear expression is encoded as a dynamic structure tree though hybrid coding method, in which the parameters of the expression are described by dynamic floating point array. The operations of immune optimization for the nonlinear expression structure and parameters are achieved through the encoding of structure and parameters, which are used for the realization of incorporate identification for the structure and parameters of nonlinear system model. There are excellent performances in immune optimization algorithm based on structure coding and hybrid coding, such as predominant global searching ability, not so reliant in getting too much transcendent knowledge and easy to get an intelligible nonlinear expression model with simple structure.(3) A predictive immune clone algorithm based on clone selection is proposed. A predictive model based on universal form of NARX is employed and the rolling optimization problem is solved by the clone selection algorithm, in which the optimum control sequence in time domain is directly achieved by using predictive model and the searching for the optimum of the target function in solution space so that it can avoid the complex process of solving Diophantine function, inverse matrix and so on. It is no need for this algorithm to linearize the nonlinear system and to decouple the strong-coupling MIMO system. It is also convenient to use the penalty function to solve the restriction. The simulation results also verify that the algorithm exhibits corking robustness against the external interference and modeling errors. Ideal control effect can be achieved without changes of the algorithm and its parameters in time-delay system, non-minimum phase system, unstable object, non-linear system and MIMO system. It is so that why this algorithm has generality and is easy to use, which is favorable for the application and generalization of predictive control.(4) A novel intelligent watercraft load meter is developed against the status quo that the watercraft load is hard to measure. It is the first time for the hybrid coding immune identification algorithm to identify the watercraft load model and actualize the measuring of loading and unloading cargo. The practical engineering application verifies that the error of the proposed watercraft load meter is less than 0.5% which satisfies the requirement of measuring low price cargo in watercraft transportation completely and a novel method for watercraft load online testing is proposed at the same time.(5) An intelligent control meter based on adaptive immune predictive control is designed. This intelligent meter incorporates the system identification algorithm based on hybrid immune optimization and predictive control algorithm based on clone selection, whose hardware and software structure are actualized by multiple parallel DSP. It includes performances such as parallelism, adaptability, robustness, generality and easy for using and maintenance. So it will be a new generation of intelligent control meter which is more excellent than traditional PID meters.