Model predictive trajectory tracking control of unmanned vehicles based on radial basis function neural network optimisation

Abstract

To improve the accuracy of tracking unmanned vehicles on known trajectories, two optimised model predictive control (MPC) trajectory tracking control systems are designed based on the adaptive compensation and robust control of a radial basis function (RBF) neural network. Based on the traditional MPC trajectory tracking controller and the local approximation characteristics of the RBF neural network, the proposed RBF compensation–MPC control system is designed to compensate for the inaccuracy in the MPC prediction model arising from modelling errors. The results show that this method can achieve a root mean square error of less than 0.3703 m for the lateral position. Subsequently, to suppress the error generated by the RBF neural network and reduce the degree of vehicle sideslip, the error is considered to be external interference, and the anti-interference characteristic of the RBF robust control is incorporated into the RBF robust-MPC control system. Following the re-optimisation of the RBF robust control, the root mean square error of the lateral position is set within 0.2352 m. The results of a MATLAB/Carsim joint simulation show that using the RBF robust control can improve the tracking accuracy of the traditional MPC controller compared with RBF compensation control, while simultaneously improving the driving stability of the vehicle.

A new fast nonlinear model predictive control of parallel manipulators: Design and experiments

“…System data (history of states and controls) were used to construct a neural network model that was integrated as the system model in the MPC scheme to predict its future behavior. Four examples of recent studies within this class include [31] [32] [33] and [34]. The second class concerns ANN-based learned MPC controllers wherein the concept involves creating and training an ANN based on data collection from an MPC algorithm to emulate this last one.…”