Self-Organizing Model Predictive Control for Constrained Nonlinear Systems

Model predictive control (MPC) is a practical method for addressing control issues in constrained systems. System identification and constrained optimization are two key problems that affect MPC performance. In this work, a self-organizing MPC (SOMPC) strategy is proposed for constrained nonlinear systems with unknown dynamics to achieve constraint satisfaction and improve control performance. First, the generalized multiplier method is introduced into the MPC framework to redesign the objective function. In this way, the constrained optimal control problem is reconstructed into an easily solvable unconstrained optimal problem. Second, a self-organizing fuzzy neural network (SOFNN) is adopted to identify unknown nonlinear system. Then, the performance of SOFNN is optimized by parameter updating and structure self-organization to provide accurate prediction output. Third, the gradient descent algorithm is utilized to solve nonlinear optimization problem of MPC to obtain control input. To ensure practical application, the convergence of SOFNN, the feasibility and stability of SOMPC strategy are proved. Finally, the proposed SOMPC strategy is demonstrated by a numerical experiment and an industrial process control simulation experiment, and the results show that it exhibits outstanding control performance and constraint satisfaction ability.