Adaptive Force Control Using a Standard Deviation-based Hybrid Approach

A good tracking response is essential for a force control system to achieve high performance in object handling. One of the significant challenges of the force control system is that the plant dynamics are directly dependent on the environment and the mechanical properties of the object. It's well-known that the mechanical properties of an object in manufacturing vary based on the type of product, gripper tools, and environment; hence, a non-adaptive controller may not be efficiently adopted. To address this issue, this paper proposes impedance control with hybrid adaptive algorithms to ensure the actuator system tracks the desired force command accurately. Particle Swarm Optimization (PSO) is adopted to adapt the impedance control parameters, thereby applying the capabilities of the learning system. A hybrid adaptive force control with a fitness function of the standard deviation and summation of error is proposed. The tracking response of the conventional adaptive controller was examined and compared with the proposed controller. The simulation results demonstrate the effectiveness of the proposed system.