Application of adaptive control based on Kalman state estimation in MIMO systems

Industrial process is often MIMO system, and the system model parameters are time-varying, so the traditional control strategy can not play a good control performance. To solve these problems, an Online Adaptive Kalman Feedback Control (OAKFC) algorithm is proposed in this paper. It obtains the real-time state of the system through Kalman filter. On the one hand, the system parameters are identified and updated online to solve the problem of time-varying model parameters. On the other hand, the dynamic performance of the system is improved by pole assignment state feedback. At the same time, the design of adaptive controller makes the system have better tracking performance. Taking the two-in two-out two-capacity water tank model as an example, the model parameters change during the simulation process, and the results show that the algorithm can solve the time-varying control problem of MIMO system model parameters.