Exoskeleton robots hold immense promise in rehabilitation, serving as crucial aids for patient mobility and exercise. However, harnessing their capabilities requires overcoming significant control challenges arising from complex nonlinear dynamics and uncertainties in both models and actuators. This paper introduces a novel adaptive backstepping controller designed specifically for exoskeleton robots navigating uncertain dynamics and actuator parameters. Unlike conventional approaches that rely on basis functions, the proposed controller integrates a Modified Function Approximation Technique (MFAT) to approximate dynamic parameters without the need for such functions. The MFAT effectively manages mismatched perturbations, while the backstepping control compensates for uncertainties associated with state variables, enhancing resilience to disturbances, especially in scenarios where the exoskeleton's dynamics model is unknown. Lyapunov stability analysis ensures uniformly ultimately bounded signals within the closed-loop system. A comparative study conducted on the industrial robot IRB 120, validates the effectiveness of the proposed controller and highlights its superior performance. Real-time implementation on a 7 Degrees of Freedom (DOFs) wearable robot named ETS-MARSE confirms the efficiency of the control algorithm. The results from simulations and experiments underscore the efficacy of the proposed approach. The insights gained from this research pave the way to unlocking the full potential of exoskeleton robots in rehabilitation settings, promising improved patient outcomes and advancing human-technology interaction to new heights.

This paper presents a robust adaptive control design for robot manipulators to track desired trajectories amid unknown disturbances and input saturation. The suggested controller integrates backstepping and super-twisting techniques, ensuring system stability and robustness. The backstepping method mitigates unmatched disturbances in a two-step process, while the super-twisting algorithm addresses matched perturbations and overshoot apparitions. A nonlinear observer enhances control efficacy against matched disturbances and input saturation, ensuring fast convergence via a quasi-non-singular terminal sliding surface. This approach enables precise tracking with smooth control signals and avoids large feedback gains. An advanced adaptive reaching law dynamically adjusts the controller's behavior through a potential function, mimicking and enhancing various established reaching control laws. The designed method provides a flexible strategy with rapid convergence, minimal chattering, and adaptability to variation of system dynamics. Stability is confirmed using Lyapunov's direct method, proving uniform boundedness of signals in the closed-loop system. The proposed controller was validated through simulations, experiments, and comparative analysis, demonstrating its superior performance.