MPC policy learning using DNN for human following control without collision

Abstract Model predictive control has recently been applied to a wide variety of motion control systems. Model predictive control can be used to generate optimized control inputs with excellent performance considering inequality constraints to the control inputs, control outputs, and state variables. However, the computational load for this method is too heavy for implementation in most actual systems because the quadratic programming problem must be solved within the sampling period. As the number of inequality constraints, control variables, and state variables in the control system increases, more calculation time is required. In this study, a deep neural network designed to learn the model predictive control policy was developed to reduce the computational load. It is expected that a relatively small neural network can be used to learn the model predictive control policy. In the proposed system, the motion controller calculates the learned neural network in real time instead of solving the quadratic programming problem, realizing almost the same control performance as the original model predictive control approach. The effectiveness of the proposed approach was verified by applying it to the control of a personal robot designed to follow the user, which can provide daily support to the elderly. In Matlab simulations, the calculation time for the proposed approach was approximately times faster than that of the conventional method of solving the quadratic programming problem. In addition, an experiment using an actual personal robot was conducted to confirm the control performance.