基于即时学习的MIMO系统滑模预测控制方法

针对MIMO非线性系统的控制问题，采用数据驱动的控制策略，将具有本质自适应能力的即时学习算法与具有强鲁棒性的滑模预测控制相结合，设计了一种基于即时学习的滑模预测（LL-SMPC）控制方法．该方法在在线局部建模的基础上，采用滑模预测控制律求取最优控制量，具有较强的自适应和抗干扰能力，并避免TDiophantine方程的求解，有效减少了计算量．通过仿真研究，验证了算法的有效性．

Sliding mode predictive control for MIMO systems via lazy learning

To solve the control problem of MIMO nonlinear system, we propose a sliding mode predictive control based on lazy learning(LL--SMPC) method. The LL--SMPC builds the local model online based on the lazy learning algorithm and obtains the optimal control law by solving the quadratic optimization problem formulated in sliding mode predictive control framework; therefore it has strong adaptive ability and anti-jamming ability. Furthermore, the computation complexity is reduced by avoiding solving of Diophantine equation. Simulation results show that the proposed method is effective.