No abstract

Myoelectric control has gained much attention which translates the human intentions into control commands for exoskeletons. The electromyogram (EMG)-driven musculoskeletal (MSK) model shows prominent performance given its ability to interpret the underlying neuromechanical processes among the musculoskeletal system. This model-based scheme contains inherent physiological parameters, e.g., isometric muscle force, tendon slack length, or optimal muscle fibre length, which need to be tailored for each individual via minimising the differences between the experimental measurement and model estimation. However, the creation of the personalised EMGdriven MSK model through the evolutionary algorithms is timeconsuming, hurdling the use of the EMG-driven MSK model in practical scenarios. This paper proposes a computational efficient optimisation method to estimate the subject-specific physiological parameters for a wrist MSK model based on the direct collocation method. By constraining control variables to the experimentally measured EMG signals and introducing the physiological parameters into control variables, fast optimisation is achieved by identifying the discretised parameters at each grid simultaneously. Experimental evaluations on 12 healthy subjects are performed. Results demonstrate the proposed method outperforms the baseline optimisation algorithms used in the literature, including genetic algorithm, simulated annealing algorithm, and particle swarm optimisation algorithm. The proposed direct collocation method shows the possibility to alleviate the costly optimisation procedure and facilitate the use of the MSK model in practical applications.

No abstract

This is a repository copy of Robust Iterative Learning Control for Pneumatic Muscle with Uncertainties and State Constraints.

Wrist rehabilitation exoskeletons have gained much attention over the last decades, striving to restore motor functions for patients with neuromuscular disorders. Electromyography (EMG) signal has been employed to estimate the motion intention to achieve interactive training schemes. However, it is a challenging task to estimate the joint impedance in realtime, as it is a crucial parameter for control of exoskeletons. This paper proposes an adaptive cooperative control strategy for a wrist exoskeleton based on a real-time joint impedance estimation approach. By explicitly interpreting the underlying transformation in the muscular and skeletal systems, the proposed approach estimates the motion intention and the joint impedance of a human subject simultaneously without additional calibration procedures and regulates the training trajectories and assistance accordingly. Results indicate the proposed method outperforms other training protocols, including the trajectory tracking control and the fixed cooperative control. The proposed control strategy provides an additional 66.25% motion deviation when estimated joint torque increases 12.36%, which enhances the training effectiveness and the interaction safety and promotes subjects' active engagement.