具有执行器饱和与随机非线性扰动的离散系统模型预测控制

Model predictive control for discrete-time systems in presence of actuator saturation and stochastic nonlinear perturbation

针对具有执行器饱和与随机非线性扰动的离散系统,提出一种模型预测控制器设计方法。通过引入一个服从Bernoulli分布且已知条件概率的随机变量描述系统的随机非线性扰动,根据预测控制的滚动优化原理,在每一采样时刻,求解保证无穷区域二次性能指标期望值的上界达到最小的优化问题。运用Lyapunov稳定性理论,给出了保证控制算法可行性和闭环系统随机稳定性的充分条件。最后通过仿真验证了所提控制方法的有效性。

The design method of model predictive controller was proposed for discrete-time systems in the presence of actuator saturation and stochastic nonlinear perturbation.The stochastic nonlinear perturbation of the systems was described according to a Bernoulli distributed white sequence with a known conditional probability.Based on the predictive control principle of receding optimization,the optimization problem was solved to ensure the upper bound on the expected value of infinite horizon performance objective minimizing,at each time.Using Lyapunov stable theory,the sufficient conditions were established to guarantee the feasibility of control algorithm and the stochastic stability of the closed-loop system.Finally,simulation results were given to illustrate the effectiveness of the proposed control method.