For the MIMO nonlinear systems, a multivariable ORVFL neural network adaptive predictive control algorithm based on Improved Sparrow Search Algorithm was proposed in this paper. The algorithm uses the ORVFL network to approximate the nonlinear system model, and applies to the multi?step prediction of the system process. In order to improve the performance of the Sparrow Search Algorithm, the algorithm is used to optimize the system performance index online and solve the optimal control law of each sampling period. The results show that the algorithm has good control performance and good anti?model mismatch ability.

A novel distributed model predictive control (DMPC) approach based on immune algorithm (IA) to find out the optimal system decomposition structure is proposed. The IA is used to solve decomposition problems for input clustering decomposition (ICD) and input?output pairing decomposition (IOPD), which can minimize the impact of input?output coupling between systems, and then DMPC algorithm is used to control the decomposed system. This approach effectively reduces the coupling between subsystems, and reduces the communication load of the system. Finally, a heavy oil fractionation chemical process is simulated and compared with the centralized MPC simulation results to verify the effectiveness of the algorithm.