连续搅拌反应釜的自适应输出反馈控制及参数整定

Adaptive output feedback control and parameter tuning for continuous stirred tank reactor

针对一类连续搅拌反应釜(CSTR),提出了基于扩张状态观测器和反步法的自适应控制方法,并结合连续动作强化学习器(CARLA)进行控制器参数整定.将CSTR视为包含不确定函数的非严格反馈非线性系统,利用扩张状态观测器对系统中的状态变量实时估计,并对不确定函数在线逼近,将系统补偿为线性二阶积分串联系统,为其设计反步法控制器.通过李雅普诺夫稳定性定理对系统稳定性进行分析,证明了闭环系统中所有信号均有界.最后,针对大量控制器参数难以人工整定的问题,设计CARLA算法快速搜索控制器参数最优值,提升了控制品质.仿真实验进一步验证了该方法的有效性.

For a class of continuous stirred tank reactor(CSTR),an adaptive control method based on extended state observer(ESO)and backstepping method is proposed.Meanwhile,continuous action reinforcement learning automata(CARLA)is used to tune the controller parameters.The CSTR is regarded as a non-strict-feedback nonlinear system with uncertain functions.The extended state observer is used to estimate the state variables of the system in real time,and the uncertain function of the system is approximated online by that.In this way,the system is compensated as a linear secondorder integral series system,and the backstepping controller is designed for it.The stability of the system is analyzed by Lyapunov stability theorem,which is proved that all signals in the closed-loop system are bounded.Furthermore,CARLA algorithm is designed to search the optimal value of controller parameters rapidly,which solves the problem that a large number of controller parameters are difficult to be tuned manually,and the control quality is improved.Finally,the simulation results verify the effectiveness of this method,and the superiority of that is further proved by comparing with other methods.