Model predictive control of an active ankle-foot orthosis with non-linear actuation constraints

• Published in: Control engineering practice Vol. 136; p. 105538
• Main Authors: DeBoer, Benjamin, Hosseini, Ali, Rossa, Carlos
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2023
• Subjects: Active ankle-foot orthosis; Active disturbance rejection; Non-linear model predictive control; Active ankle-foot orthosis; Non-linear model predictive control; Active disturbance rejection
• DOI: 10.1016/j.conengprac.2023.105538
• ISSN: 0967-0661

The goal of active ankle-foot orthoses (AAFO) is to assist the user in recreating a nominal gait motion. Previously proposed control systems for AAFOs have been reactive, with controllers based on the current tracking error. However, the optimal AAFO controller must ensure adequate trajectory tracking while minimizing the amount of assistance provided to the user. To this end, a model predictive controller (MPC) can be considered to determine the optimal control sequence for a given trajectory. The MPC minimizes the control effort, thereby determining the minimal assistance required for a user to regain a natural gait. This work outlines the formulation of a non-linear MPC for an AAFO and its combination with an extended state observer (ESO) for tracking of an AAFO. In this work, a MPC controller is proposed for optimal control of an AAFO, a linear plant subject to non-linear actuation constraints. A novel method is expressed to determine the initial control horizon selection in combination with variable step Newton-based optimization to enable real-time implementation. The MPC control effort is then combined with the estimated disturbance from an ESO to accurately track the desired gait. Simulations and experimental results prove the tracking capabilities of the proposed method. Compared to a tuned proportional–derivative controller, the simulated controller reduced the trajectory tracking error by up to 65.1% and 15.3% in an undisturbed and disturbed environment, respectively. The MPC and ESO combination allows the controller to identify the optimal control action for accurate trajectory tracking. Concurrently, the ESO rejects both external and internal disturbances, with superior tracking capabilities in both undisturbed and heavily disturbed systems, making it the optimal choice for AAFO control.