Research On Adaptive Model Predictive Control Method For Driverless Mining Dumpers

Driverless operation is one of the technological development trends of mining dumpers in the future.Model Predictive Control(MPC)algorithm has many advantages,such as low requirement for mathematical model accuracy and flexible handling of control constraints,and it is currently an ideal method for motion control of autonomous vehicles.In the existing research results,the uncertainty of the road condition is often less incorporated into the control process.And for the mining dumpers,the roads are in various condition,and the uncertainties such as the curvature and the friction coefficient are difficult to be simplified as general disturbances.Therefore,it is of great significance to study the adaptive model predictive control method for the realization of driverless mining dumpers.This paper presents a Model Predictive Control algorithm based on adaptive objective function strategy and its application in lateral motion control of driverless mining dumpers under changing road conditions.First of all,under certain assumptions,a three–degree–of-freedom dynamic model of driverless mining dumpers is established based on the automobile dynamics modeling method,then it is linearized and discretized.After that,it is converted to the prediction model required by MPC.In the second place,an optimization objective function including tracking error and acceleration increment is established,the principle that the penalty weight varies with the road curvature and friction coefficient is determined,and the adaptive weight parameter is given.On this basis,a lateral motion controller based on adaptive MPC algorithm is designed.The objective function and system constraints are transformed into a standard quadratic programming problem and the rolling solution process is realized.Finally,the common MPC controller and the adaptive MPC controller designed in this paper are simulated through the Simulink and Carsim cosimulation model.Simulation results show that the proposed adaptive algorithm can adjust the weight of penalty weights in the objective function according to the changing road conditions,and the improved comprehensive performance of the controller proposed in this paper is verified.