Fractional Fuzzy Force-Position Control of Constrained Robots

Modern robotic tasks often require interaction with the surrounding elements in the workspace. In some high-precision tasks, it is essential to stabilize the contact force on a smooth yet rigid surface, which can be modeled as a unilateral constraint. This challenge becomes increasingly complex in the presence of disturbances. This study addresses these issues using a robust fuzzy force-position controller that combines the approximation capabilities of fuzzy inference systems with the nonlocal properties of fractional operators. The proposed approach extends the error integration to include proportional-integral-derivative (PID) components of the position error, along with the integral of the contact force error. This formulation leverages the orthogonality between force and velocity subspaces to achieve accurate force-position stabilization. Additionally, an adaptive mechanism enhances closed-loop performance and robustness. The effectiveness of the proposed controller is validated through analytical derivations and simulations, thereby demonstrating its reliability in constrained environments.