SYNPOL D CMA127 3DDE300407 具有鲁棒性保证的数据驱动模型预测控制

We propose a robust data-driven model predictive control scheme to control linear time invariant systems. CMA127 3DDE300407 This scheme is described using an implicit model based on behavioral system theory and past measurement trajectories. Specifically, it does not require any prior identification steps, but only the input-output trajectory of the initial measurement and the upper bound of the order of the unknown system. Firstly, we demonstrate the exponential stability of a nominal data-driven MPC scheme with terminal equality constraints in the absence of measurement noise. We propose a robust modification scheme for bounded additive output measurement noise, CMA127 3DDE300407 including a relaxation variable and regularization cost. We have demonstrated that applying this robust MPC scheme in a multi-step manner leads to actual exponential stability of the closed-loop system relative to noise levels. The presented results provide the first (theoretical) analysis of the closed-loop characteristics, which is generated by a simple pure data-driven MPC scheme.