A Novel Model-Free Adaptive Recursive Optimal Control for Time-Varying Nonlinear Systems

In this paper, A novel model-free adaptive recursive optimal control (MFAROC) is proposed for time-varying nonlinear systems. On the basis of the optimization index designed based on the recursive idea, a novel time-varying parameter called information weight (IW) is derived and proved to be derived, which effectively solves the problem of difficult establishment of the controlled model. Meanwhile, the control law is designed by using the symmetry principle, and the model-free adaptive recursive optimization control algorithm (MFAROC) is investigated, which effectively compensates for the time-varying nature of the system and solves the time-varying problem of the parameters, structure, and order of the nonlinear system. The algorithm is an important data-driven control method involving adaptive learning capability of nonlinear systems. The effectiveness of the MFAROC algorithm is verified by numerical simulation and simulation of the speed tracking control example of the transportation system. Compared with the existing MFAC and incremental PID, our MFAROC proves that the convergence speed is significantly improved and the tracking error is significantly reduced by some comparative analyses.