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利用Kautz函数逼近得到表征过程对象的状态空间方程,基于Kautz模型设计一种稳定的自适应预测函数控制器。通过对闭环系统广义状态方程的稳定性分析,依据Lyapunov稳定性定理得到控制系统稳定的条件,并对算法进行改进,提出一种衰减因子优化补偿方法,设计局部搜索和混沌优化2种算法实现在线寻优调节补偿因子,抑制系统突变,减少调节时间,提高控制品质。仿真研究证明了该控制算法的有效性。
The state space equation characterizing the process object is approximated by Kautz function, and a stable adaptive predictive function controller is designed based on Kautz model. By analyzing the stability of the generalized state equation of the closed-loop system and the Lyapunov stability theorem, the condition of the stability of the control system is obtained and the algorithm is improved. An optimization method of attenuation factor optimization is proposed. Two algorithms are designed: local search and chaos optimization Optimum adjustment compensation factor, inhibit system mutations, reduce adjustment time, improve control quality. Simulation studies prove the effectiveness of this control algorithm.