Optimal H∞ tracking control of nonlinear systems with zero-equilibrium-free via novel adaptive critic designs.

In this paper, a novel adaptive critic control method is designed to solve an optimal H∞ tracking control problem for continuous nonlinear systems with nonzero equilibrium based on adaptive dynamic programming (ADP). To guarantee the finiteness of a cost function, traditional methods generally assume that the controlled system has a zero equilibrium point, which is not true in practical systems. In order to overcome such obstacle and realize H∞ optimal tracking control, this paper proposes a novel cost function design with respect to disturbance, tracking error and the derivative of tracking error. Based on the designed cost function, the H∞ control problem is formulated as two-player zero-sum differential games, and then a policy iteration (PI) algorithm is proposed to solve the corresponding Hamilton-Jacobi-Isaacs (HJI) equation. In order to obtain the online solution to the HJI equation, a single-critic neural network structure based on PI algorithm is established to learn the optimal control policy and the worst-case disturbance law. It is worth mentioning that the proposed adaptive critic control method can simplify the controller design process when the equilibrium of the systems is not zero. Finally, simulations are conducted to evaluate the tracking performance of the proposed control methods.

Trajectory Modulation for Impact Reducing of Lower-Limb Exoskeletons.

Lower-limb exoskeletons have received considerable attention because of their effectiveness in walking assistance and rehabilitation for paraplegic patients. Excessive foot-ground impacts during walking make patients uncomfortable and even lead to injury. In this paper, we propose an optimized knee trajectory modulation (OKTM) for foot-ground impact reduction. The OKTM can reduce the peak of ground reaction force (PGRF) by knee-joint trajectory modulation based on a parameters-optimizing spring-damping system. In addition, a hip trajectory modulation (HTM) is presented to compensate for torso pitch deflections due to the OKTM. Unlike traditional mechanical-device-based methods, the proposed OKTM and HTM require no bulky mechanical structures, and can adaptively adjust parameters to adapt to different impacts. We demonstrated the efficiency of the proposed approach in both simulations and experiments for engineering verifications. Results show that the approach can effectively reduce PGRF.

Multimodal Human-Exoskeleton Interface for Lower Limb Movement Prediction Through a Dense Co-Attention Symmetric Mechanism.

A challenging task for the biological neural signal-based human-exoskeleton interface is to achieve accurate lower limb movement prediction of patients with hemiplegia in rehabilitation training scenarios. The human-exoskeleton interface based on single-modal biological signals such as electroencephalogram (EEG) is currently not mature in predicting movements, due to its unreliability. The multimodal human-exoskeleton interface is a very novel solution to this problem. This kind of interface normally combines the EEG signal with surface electromyography (sEMG) signal. However, their use for the lower limb movement prediction is still limited-the connection between sEMG and EEG signals and the deep feature fusion between them are ignored. In this article, a Dense con-attention mechanism-based Multimodal Enhance Fusion Network (DMEFNet) is proposed for predicting lower limb movement of patients with hemiplegia. The DMEFNet introduces the con-attention structure to extract the common attention between sEMG and EEG signal features. To verify the effectiveness of DMEFNet, an sEMG and EEG data acquisition experiment and an incomplete asynchronous data collection paradigm are designed. The experimental results show that DMEFNet has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 82.96 and 88.44%, respectively.