A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with the previous work, the agent's dynamics includes the uncertainties and external disturbances, which is more practical in real-world applications. Due to the approximation capability of neural networks, the uncertain dynamics is compensated by the adaptive neural network scheme. The effects of the approximation error and external disturbances are counteracted by employing the robustness signal. The proposed algorithm is decentralized because the controller for each agent only utilizes the information of its neighbor agents. By the theoretical analysis, it is proved that the consensus error can be reduced as small as desired. The proposed method is then extended to two cases: agents form a prescribed formation, and agents have the higher order dynamics. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed method.

This article addresses the issue of output-feedback consensus control of multiagent systems under the directed topology and subject to bounded external disturbances. By employing a smooth time-varying function, a distributed practical predefined-time (PPT) observer is developed to estimate the reference trajectory for the entire team (i.e., the leader’s state) and a practical preset-time extended-state observer is also proposed to estimate bounded disturbances and unmeasurable system states. Next, a novel continuous and nonsingular PPT consensus control law is designed on the basis of the observers. Furthermore, the designed control protocol can achieve PPT stability, that is, consensus tracking errors are enforced to a neighborhood around zero within a predetermined time, which can be specified a priori , independent of initial states of agents and/or any other design parameters. Finally, illustrative numerical examples, including a comparative one, are provided to demonstrate the performance of the present predefined-time control approach.

Abstract Motor imagery based brain-computer interface (MI-BCI) has been studied for improvement of patients’ motor function in neurorehabilitation and motor assistance. However, the difficulties in performing imagery tasks limit its application. To overcome the limitation, an enhanced MI-BCI based on functional electrical stimulation (FES) and virtual reality (VR) is proposed in this study. On one hand, the FES is used to stimulate the subjects’ lower limbs before their imagination to make them experience the muscles’ contraction and improve their attention on the lower limbs, by which it is supposed that the subjects’ motor imagery (MI) abilities can be enhanced. On the other hand, a ball-kicking movement scenario from the first-person perspective is designed to provide visual guidance for performing MI tasks. The combination of FES and VR can be used to reduce the difficulties in performing MI tasks and improve classification accuracy. Finally, the comparison experiments were conducted on twelve healthy subjects to validate the performance of the enhanced MI-BCI. The results show that the classification performance can be improved significantly by using the proposed MI-BCI in terms of the classification accuracy (ACC), the area under the curve (AUC) and the F1_score (paired t-test, p<0.05).

Accurate acquisition of interactive information is crucial for the effective execution of rehabilitation training. However, due to model and sensor errors, it is difficult to obtain interactive information accurately and quickly. To overcome these challenges, a novel accurate and fast estimation method for the human–robot interaction torques (HRITs) is proposed in this article. First, the HRIT model with order adaptive adjustment ability (HMOAA) is constructed. The polynomial order of HMOAA can be adaptively adjusted based on the partial state estimation, which is more consistent with the dynamic time-varying characteristics of HRIT. Second, the Sage–Husa adaptive strong tracking Kalman filter (SHASTKF) is designed based on the modified Sage–Husa adaptive Kalman filter (SHAKF) and strong tracking Kalman filter (STKF). The SHASTKF can quickly track the abrupt HRIT changes when the subject suddenly exerts active torques in rehabilitation training. Moreover, it also has the ability to recursively estimate the noise characteristics, and can stably complete the HRIT estimation task when the noise characteristics are unknown. Finally, simulations and experiments are conducted to validate the proposed method, and the comparison results demonstrate that the proposed method has good torque estimation precision and fast tracking ability of abrupt changes in HRITs.

Functional assessment is an essential part of rehabilitation protocols after stroke. Conventionally, the assessment process relies heavily on clinical experience and lacks quantitative analysis. In order to objectively quantify the upper-limb motor impairments in patients with post-stroke hemiparesis, this study proposes a novel assessment approach based on motor synergy quantification and multi-modality fusion. Fifteen post-stroke hemiparetic patients and fifteen age-matched healthy persons participated in this study. During different goal-directed tasks, kinematic data and surface electromyography (sEMG) signals were synchronously collected from these participants, and then motor features extracted from each modal data can be fed into the respective local classifiers. In addition, kinematic synergies and muscle synergies were quantified by principal component analysis (PCA) and k weighted angular similarity (kWAS) algorithm to provide in-depth analysis of the coactivated features responsible for observable movement impairments. By integrating the outputs of local classifiers and the quantification results of motor synergies, ensemble classifiers can be created to generate quantitative assessment for different modalities separately. In order to further exploit the complementarity between the evaluation results at kinematic and muscular levels, a multi-modal fusion scheme was developed to comprehensively analyze the upper-limb motor function and generate a probability-based function score. Under the proposed assessment framework, three types of machine learning methods were employed to search the optimal performance of each classifier. Experimental results demonstrated that the classification accuracy was respectively improved by 4.86% and 2.78% when the analysis of kinematic and muscle synergies was embedded in the assessment system, and can be further enhanced to 96.06% by fusing the characteristics derived from different modalities. Furthermore, the assessment result of multi-modality fusion framework exhibited a significant correlation with the score of standard clinical tests (R= –0.87, P=1.98e–5). These promising results show the feasibility of applying the proposed method to clinical assessments for post-stroke hemiparetic patients.