A Runge-Kutta neural network-based control method for nonlinear MIMO systems

In this paper, a novel Runge-Kutta neural network (RK-NN)-based control mechanism is introduced for multi-input multi-output ( MIMO) nonlinear systems. The overall architecture embodies an online Runge-Kutta model which computes a forward model of the system, an adaptive controller with tunable parameters and an adjustment mechanism realized by separate online Runge-Kutta neural networks to identify the dynamics of each tunable controller parameter. Runge-Kutta identification block has the competency to approximate the time-varying parameters of the model and unmeasurable states of the controlled system. Thus, the strengths of radial basis function (RBF) neural network structure and Runge-Kutta integration method are combined in this structure. Adaptive MIMO proportional-integral-derivative (PID) controller is deployed in the controller block. The control performance of the proposed adaptive control method has been evaluated via simulations performed on a nonlinear three-tank system and Van de Vusse benchmark system for different cases, and the obtained results reveal that the RK-NN-based control mechanism and Runge-Kutta model attain good control and modelling performances.