An Efficient MPC-CPG Bionic Controller for Periodic Motions and Bounded Transitions

The traditional controllers for performing desired periodic motions are often computationally intensive and can hardly satisfy the constraints of the control system. This article proposes a bionic controller for such motion patterns inspired by the control mechanism of central pattern generators (CPGs) found in vertebrates. The bionic controller combines the advantages of model predictive control (MPC) and CPGs to track arbitrary periodic trajectories, with smooth transitions between different gaits under constraints. The controller consists of three typical components: the CPG component as the inner loop to generate steady-state periodic motions, the MPC-CPG component as the outer loop for gait transitions, and the decision component to determine which loop should be utilized. The stability of the controller is demonstrated through Lyapunov’s method. The controller is then used to track a sinusoidal trajectory under various constraints, and its performance is compared to that of the separate CPG and MPC controllers. The results demonstrate the effectiveness of the proposed controller in tracking periodic motions with constraints, providing a promising approach for developing more efficient and robust controllers for periodic motions.