In this paper, a novel high-order disturbance observer (HODO) for the Mobile Wheeled Inverted Pendulum (MWIP) system is firstly proposed. Based on a choice method of optimal gain matrices, the estimation accuracy of the HODO can be improved. Combining the proposed HODO and sliding mode control (SMC), a new control strategy is designed for the balance and speed control of the MWIP system. The boundness of the estimation error of HODO is proved and the stability of the closed-loop control system is achieved through the appropriate selection of sliding surface coefficients. The effectiveness of all proposed methods is verified by experiments on a real MWIP system.

In rehabilitation practice, motivating patients with neurological injuries to actively increase muscle activity and ensure their safety are important. Therefore, this study proposed a position-constrained assist-as-needed (AAN) control method for rehabilitation robots. A human–robot interaction system with position constraints was first established based on prescribed performance. Aiming at implementing the AAN strategy, the robot assistance level metric (RALM), a constructed global continuous differentiable function incorporating dead zone and saturation characteristics, was introduced to quantify the robotic assistance and facilitate seamless operation. To bridge the gap between the position constraints and the AAN strategy, a sliding manifold was constructed for the constrained human–robot dynamic system, where RALM was regarded as a weight factor to achieve a human-dominated mode, a robot-dominated mode, and their smooth transition, regarded as a human–robot shared mode. The stability of the closed-loop system was guaranteed by using the Lyapunov theory, and the proposed controller was verified by several physical experiments on a knee exoskeleton driven by pneumatic muscles.

In Pneumatic Muscle Actuators (PMAs)-driven robotic applications, there might exist unpredictable shocks which lead to the sudden change of desired trajectories and large tracking errors. This is dangerous for physical systems. In this paper, we propose a novel adaptive proxy-based robust controller (APRC) for PMAs, which is effective in realizing a damped response and regulating the behaviors of the PMA via a virtual proxy. Moreover, the integration of the APRC and the nonlinear disturbance observer (NDO) further handles the system uncertainties/disturbances and improves the system robustness. According to the Lyapunov theorem, the tracking states of the closed-loop PMA control system are proven to be globally uniformly ultimately bounded through two motion phases. Extensive experiments are conducted to verify the superior performance of our approach, in multiple tracking scenarios.

In this article, a robust trajectory tracking control method is proposed for the passive gait training exoskeleton system driven by pneumatic muscles (PMs). Conventional model-based controllers suffer from limitations with respect to model uncertainties and external disturbances caused by PMs and complex robotic systems. An echo state network (ESN) is used in this study to approximate the model uncertainties and external disturbances of the exoskeleton system. Based on the approximation of ESN, a super-twisting control (STC) algorithm is designed to guarantee accurate tracking control at both hip and knee joint levels. Because there are both weight error and global approximation error in neural network approximation, a standard quadratic form cannot be obtained which plays an important role in the stability analysis of the traditional super twisting algorithm. To solve this issue, a dedicated positive definite matrix is constructed in this article, which bridges the ESN and STC by providing a parameter selection criteria. The stability with respect to the tracking problem of the exoskeleton system is then guaranteed according to the Lyapunov theorem. Both numerical simulations and experimental results present better tracking accuracy and robustness compared with the traditional sliding mode control and STC.

In rehabilitation robotics, both compliance and high-precision motion are critical to effective rehabilitation training. However, there is an inherent conflict between system compliance and control accuracy, which presents significant challenges in achieving optimal performance. To address this issue, this article proposes a surface electromyogram (sEMG)-driven musculoskeletal model-based adaptive variable impedance controller with flexible prescribed performance, ensuring a balance between compliance and precision motion in the human–robot interaction. We begin by formulating a constrained human–robot system and subsequently transform it into an unconstrained system using prescribed performance techniques. A novel impedance model is introduced to ensure system stability while maintaining prescribed performance. Furthermore, the controller integrates the Joint Strength Index, derived from an sEMG-driven musculoskeletal model, and incorporates a flexible prescribed performance function combined with adaptive stiffness and damping. The method supports both robot-dominant and human-dominant modes, and the seamless transition between them. Our findings show that lower human involvement increases system stiffness and narrows motion constraints, enabling high-precision motion. In contrast, greater human participation improves system compliance and broadens motion constraints, allowing for more freedom of movement. Finally, experiments were conducted on an upper-limb rehabilitation robot to validate the proposed method.