An enhanced robust control algorithm with inequality constraints for service robot joint performance optimization

The joint module of collaborative robots must simultaneously guarantee accurate trajectory tracking and strict joint-limit compliance during human–robot interaction. Its nonlinear, strongly coupled and time-varying dynamics, however, amplify the influence of parametric uncertainty, external disturbances, and model errors, causing large tracking deviations. We present a robust, model-based PD controller that explicitly enforces state inequality constraints on joint displacement. A diffeomorphic transformation converts the constrained dynamics into an unconstrained equivalent, allowing standard robust-synthesis tools to be applied while preserving the original limits. Uniform ultimate boundedness of the closed-loop error is proven under lumped uncertainties. Experiments on a 1-DoF joint prototype yield a steady-state tracking error within ±0.1° without violating the prescribed angular bounds, outperforming both unconstrained H ∞ and PID baselines in accuracy and safety margin.