Predictive Control of Robot Arm Trajectory Tracking Based on Control Lyapunov Function

Modern robotic systems are endowed with superior mobility and mechanical skills, making them suitable for using in special real-world scenarios, such as disaster scenarios where robots can be used to remove hazardous substances. Therefore, it is crucial to develop intelligent control algorithms that enable complex robots to accurately perform their movements. Firstly, considering optimization and input constraints, this article designs a robotic arm model predictive control (MPC) scheme based on control Lyapunov functions for the first time. It combines the stability guarantee provided by control Lyapunov functions (CLFs) with the optimality provided by MPC, and constructs a CLFs-MPC framework based on the premise of ensuring actuator performance. Secondly, by designing the arctangent function and further introducing the contraction constraint of the bounded controller, the recursive feasibility and stability of the robot control system are ensured. The stability of bounded controllers was analyzed using Lyapunov theory and LaSalle invariance principle. Finally, numerical simulation verifies the validity and superiority of the CLFs-MPC controller.