Predictive Tracking Control of Autonomous Mobile Platform with Stage Visual Effects Lighting

In this paper, a predictive control (MPC) approach is proposed to deal with the asynchronized problem of autonomous mobile platform in stage visual effects lighting. First, a tracking error model is established with intermittent measurement by defining the geometric center of the visual effects lighting as a virtual robot to generate a reference path for the autonomous mobile platform. The intermittent measurement is represented by a binary random variable. An unconstrained MPC controller is designed and a terminal penalty function is introduced, while the mean-square asymptotic stability of the system is analyzed. Moreover, the MPC optimization problem is reformulated into a constrained quadratic programming problem within the input and state constraints of the system. To solve the constrained problem, a primal-dual neural network (PDNN) is employed to solve the quadratic programming problem. The PDNN method is adopted for its fast computational ability, powerful real-time optimization capability, ease of hardware implementation and robust global convergence. Finally, the effectiveness of the proposed method is verified by a simulation example, which demonstrates the ability of the method to achieve accurate tracking in stage environments.