This paper proposes a unified observer–identifier–learning framework (OILF) for predefined-time optimal tracking control with prescribed performance for robotic systems subject to unmeasurable states and uncertain dynamics. Most existing optimal control approaches rely on full-state information or accurate dynamic models, which are often unavailable in practice. To overcome this issue, a predefined-time dynamic regression extension and mixing (PTDREM) method is proposed to realize the co-design of state observer and parameter identifier, enabling synchronous predefined-time estimation of unknown states and dynamic parameters. Subsequently, to achieve optimal tracking control for robotic systems, a prescribed-performance-based critic–actor (PPCA) structure is developed via reinforcement learning (RL), in which all hierarchical tracking errors are driven into prescribed neighborhoods of the origin within a predefined time. In contrast to most existing works that solely ensure uniform ultimate boundedness (UUB) of the closed-loop system, the proposed scheme enables the upper bounds of the convergence time of the state observer, system identifier, and optimal controller to be preset through independent parameter design, thereby establishing global predefined-time stability (G-PTS) for the overall closed-loop system. Numerical simulations on a two-degree-of-freedom (DOF) robotic manipulator verify the effectiveness of the proposed OILF.

For contact-intensive tasks, the ability to generate policies that produce comprehensive tactile-aware motions is essential. However, existing data collection and skill learning systems for dexterous manipulation often suffer from low-dimensional tactile information. To address this limitation, we propose DexTac, a visuo-tactile manipulation learning framework based on kinesthetic teaching. DexTac captures multi-dimensional tactile data—including contact force distributions and spatial contact regions—directly from human demonstrations. By integrating these rich tactile modalities into a policy network, the resulting contact-aware agent enables a dexterous hand to autonomously select and maintain optimal contact regions during complex interactions. We evaluate our framework on a challenging unimanual injection task. Experimental results demonstrate that DexTac achieves a 91.67% success rate. Notably, in high-precision scenarios involving small-scale syringes, our approach outperforms force-only baselines by 31.67%. These results underscore that learning multi-dimensional tactile priors from human demonstrations is critical for achieving robust, human-like dexterous manipulation in contact-rich environments.

Output tracking control has been extensively applied in the cooperative control of multiagent systems (MASs), including mobile robot obstacle avoidance and autonomous aerial vehicle formation. This article investigates the $H_{\infty }$ output consensus tracking problem of interval type-2 (IT2) fuzzy heterogeneous MASs subject to multiple-channel denial-of-service (DoS) attacks. First, in the presence of DoS attacks on both leader-follower and follower-follower communication channels, a fully distributed adaptive compensator is designed to approximate the convex hull of the leader's state. Then, to address DoS attacks occurring in the compensator-controller and observer-controller channels, an observer-based fuzzy switched controller is developed for each follower agent to accomplish the tracking objective. Furthermore, a two-layer hierarchical hybrid event-triggered mechanism (ETM) is established to significantly reduce the communication burden of the MAS network. In the proposed ETM, asynchronous communication and Zeno behavior are rigorously excluded, while the triggering frequency is effectively decreased. Moreover, a sufficient condition is derived to guarantee the exponential stability of the tracking error with a prescribed $H_{\infty }$ performance. Finally, simulation results are provided to demonstrate the feasibility and superiority of the proposed approach.

In this article, a novel general clustered network framework with hybrid communication is constructed for multiple uncertain Euler-Lagrange (EL) systems. The objective is to ensure that the systems under consideration achieve containment tracking in the predefined path at the prescribed time. First, in the light of the hierarchical control design, the output tracking problem is decomposed into the desired signals tracking and the stability of the nonlinear uncertain coupled systems. Second, the wide-area network is described by a combination of a directed graph and an intermittent control scheme, then all agents are divided into different subnetworks in demand or scenarios. Based on this, a distributed prescribed-time hybrid observer under a time-varying scaling function and a novel containment error method is designed to achieve the containment tracking. In addition, an adaptive distributed prescribed-time hybrid control strategy is proposed for the uncertainty estimation. Then, the prescribed-time stability of uncertain EL systems is analyzed and guaranteed using the general Lyapunov theory and intermittent control method. Finally, the proposed hybrid control strategy is verified by the simulation results of multiple flexible manipulator systems.

Motor imagery-based brain–computer interfaces (MI-BCIs) hold significant promise for rehabilitation training in individuals with neurological impairments such as stroke and spinal cord injury (SCI). Achieving precise and robust lower limb movement prediction for each patient is crucial. However, the variability in MI response frequencies and brain activation patterns among subjects presents a great challenge to the generalizability of MI-BCIs. This paper proposes a Tuned Heuristic Fusion Graph Convolutional Network (THFGCN) for limb movement prediction in rehabilitation scenarios. THFGCN innovatively designs a learnable EEG frequency band tuned module and a heuristic space topology module. These two modules allow for the intricate extraction of both frequency and spatial topological features, utilizing graph adjacency matrices that encapsulate channel correlations and spatial relationships, hence fostering individualized analysis and enhanced generalizability across subjects. Furthermore, a spatio-temporal convolution module paired with a feature map attention mechanism is proposed to extract the critical spatio-temporal features of electroencephalogram (EEG) data. Validation experiments on the PhysioNet and LLM-BCImotion datasets against six mainstream methods demonstrate that THFGCN outperforms state-of-theart methods, achieving 88.41% and 82.82% accuracy in the within-subject case, and 65.93% and 60.56% accuracy in the cross-subject case, respectively. Detailed frequency band weight and T-distributed Stochastic Neighbor Embedding visualization validate the effectiveness of proposed modules. Furthermore, feature interpretability analysis proves the extracted features’ profound MI task relevance, underlining THFGCN’s exceptional interpretability.

Gait training with human-like gait patterns can be provided by lower limb exoskeletons (LLEs) for patients with gait impairments. For patients with little effort to keep balance, using a mobile robotic walker to assist gait training with LLEs is an effective way. Since gait patterns are varying with walking speeds, it is a critical issue to coordinated control the robotic walker and the exoskeleton to obtain a natural and human-like walking posture. In this paper, a novel adaptive assistance approach named SQG-OPT is proposed to tackle the problem, which comprises of two parts: the Spatially Quantized Gait (SQG) and the optimization. The SQG generates reference joint angles and reference trajectory of the Center Of Mass (COM) for the human-exoskeleton system in space domain. The optimization part is constructed to convert the reference joint angles from the space domain to the time domain, which is based on the dynamics model of the human-exoskeleton-walker system and adaptive to different walking speeds. The proposed approach has been tested on the robot simulation platform CoppeliaSim, the experimental results indicate that the proposed approach can generate human-like gait patterns for different walking speeds from 0 to 0.8 m/s. Additionally, in comparison with other methods, the proposed approach has a better performance on the movement tracking of the COM for a natural walking posture.Note to Practitioners—The coordinated control is important for the exoskeleton robot with a mobile robotic walker, one of the potential challenge is the adaptive coordinated motion planning for these robotic devices. The proposed approach is for the coordinated control of the exoskeleton robots with a robotic walker and adaptive to different walking speeds, which may inspires more extended coordinated control strategies for human-exoskeleton systems in more gait training applications. The proposed approach is also potential for the coordinated motion planning of the other human-centered assistance robots, such as the wheeled walking assistance robots for the elderly.

In this article, we propose a novel online learning algorithm based on weighted policy iteration (WPI) for addressing optimal control problems of nonlinear systems. WPI is proposed to deal with the influence of the neural network (NN) approximation error on the admissibility of the improved control policy. It is shown that the new iterative method can converge to the optimal solution uniformly. Utilizing NN approximation and experience replaying techniques, a WPI-based online learning algorithm is proposed. The new online algorithm distinguishes from previously known ones in that there can be fewer neurons in the hidden layer, giving rise to significant computational improvement. The assumption that the number of neurons needs to be sufficiently large can be dropped. Besides, instead of a standard persistently excited (PE) condition, only a relaxed PE condition is needed, which is also easier to check. Finally, numerical experiments are conducted to verify the effectiveness of our method.

This article addresses the problem of designing an attack-resilient adaptive event-triggered (AET) controller for complex dynamical networks (CDNs) under DoS attacks and actuator saturation, with a focus on robust practical stability (RPS). First, considering the impact of DoS attacks on closed-loop systems, an AET controller against DoS attacks is designed. Unlike other event-triggered controllers, the complete timeline is divided, and the AET controller is built with two switching modes based on the intervals of dormant and active periods of DoS attacks in which the system is located. Second, to reconcile AET controller with actuator saturation, a switched system modeling approach is established that explicitly incorporates saturation constraints into the coupled network dynamics. Third, a switched Lyapunov-Krasovskii functional (LKF) is proposed, with which sufficient conditions are provided to ensured the RPS, and a joint design strategy is developed for the desired triggered matrix and feedback gain using linear matrix inequalities (LMIs). Moreover, the results are generalized to the case of actuator faults, indicating that the system is able to achieve RPS with actuator faults. Finally, the proposed method is verified through an example.

Despite the advances in the field of human-robot interface (HRI) based on biological neural signal, the use of the sole electroencephalography (EEG) signal to help robotic exoskeleton predict the limb movement is currently no mature in rehabilitation training, due to its unreliability. Multimodal HRI represents a very recent solution to enhance the performance of single modal HRI. These HRI normally include the EEG signal with surface electromyography (sEMG) signal. However, their use for the lower limb movement prediction in hemiplegia is still limited, and the deep fusion feature of sEMG and EEG signal is ignored. This paper proposes a Dense co-attention mechanism-based Multimodal Enhance fusion Network (DMEFNet) for the lower limb movement prediction in hemiplegia. The DMEFNet can realize the mapping and deep fusion between the sEMG and EEG signal features and get a high accuracy movement prediction of the lower limbs. A sEMG and EEG data acquisition experiment and an incomplete asynchronous data collection paradigm are designed to verify the effectiveness of DMEFNet. 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.

Lower Limb Exoskeletons (LLE) have received considerable interest in strength augmentation, rehabilitation, and walking assistance scenarios. For strength augmentation, LLE is expected to have the capability of reducing metabolic energy. However, the energy for adjusting Center of Gravity (CoG) is a main part of the total energy consumed during walking. This paper proposes a novel Human-exoskeleton Cooperative Balance (HCB) strategy which gives assistive torques balance ability and combine with the direction selected by the pilot to achieve balance walking of human-exoskeleton systems. In which, a Dynamic Torque Primitive Model (DTPM) is designed to plan a bionic assistive torque, and the balance parameters obtained by an Inverted Pendulum Model (IPM) is superimposed on it. Finally, the performance improved by the HCB strategy can break the limitation of traditional strategies and substantially increase the efficiency of assistance. We demonstrated the effectiveness of the proposed HCB strategy on the HUman-powered Augmentation Lower EXoskeleton (HUALEX) system. Experimental results indicate that the proposed HCB is more efficient than traditional strategies.

In this paper, an event-triggered optimal tracking control of discrete-time multi-agent systems is addressed by using reinforcement learning. In contrast to traditional reinforcement learning-based methods for optimal coordination and control of multi-agent systems with a time-triggered control mechanism, an event-triggered mechanism is proposed to update the controller only when the designed events are triggered, which reduces the computational burden and transmission load. The stability analysis of the closed-loop multi-agent systems with event-triggered controller is described. Further, to implement the proposed scheme, an actor-critic neural network learning structure is proposed to approximate performance indices and to on-line learn the event-triggered optimal control. During the training process, event-triggered weight tuning law has been designed, wherein the weight parameters of the actor neural networks are adjusted only during triggering instances compared with traditional methods with fixed updating periods. Further, a convergence analysis of the actor-critic neural network is provided via Lyapunov method. Finally, two simulation examples show the effectiveness and performance of the obtained event-triggered reinforcement learning controller.

In this article, a novel reinforcement learning (RL) method is developed to solve the optimal tracking control problem of unknown nonlinear multiagent systems (MASs). Different from the representative RL-based optimal control algorithms, an internal reinforce Q-learning (IrQ-L) method is proposed, in which an internal reinforce reward (IRR) function is introduced for each agent to improve its capability of receiving more long-term information from the local environment. In the IrQL designs, a Q-function is defined on the basis of IRR function and an iterative IrQL algorithm is developed to learn optimally distributed control scheme, followed by the rigorous convergence and stability analysis. Furthermore, a distributed online learning framework, namely, reinforce-critic-actor neural networks, is established in the implementation of the proposed approach, which is aimed at estimating the IRR function, the Q-function, and the optimal control scheme, respectively. The implemented procedure is designed in a data-driven way without needing knowledge of the system dynamics. Finally, simulations and comparison results with the classical method are given to demonstrate the effectiveness of the proposed tracking control method.

Estimating the center of mass (CoM) is essential for both gait planning and controlling of lower limb exoskeletons. Different from CoM estimation in human and humanoid robots, a critical issue in human-exoskeleton systems pis how to describe the effect of physical human-exoskeleton interactions in estimating the CoM of lower limb exoskeletons. This paper presents a novel center of mass estimation method Physically Coupled Serial Chain (PCSC) for human-coupled lower limb exoskeleton systems. Different from traditional serial chain methods, the proposed PCSC involves physical human-exoskeleton models to describe physical interactions between the pilot and the lower limb exoskeleton. We demonstrated the effectiveness of proposed PCSC model in the AIDER lower limb exoskeleton system. Experimental results indicate that the proposed PCSC model is more accuracy than traditional serial chain methods.

Lower Limb Exoskeletons (LLEs) are promising in gait rehabilitation for stroke survivors. In gait training of post-stroke patients with LLEs, one of the main challenges is how to generate appropriate gait patterns from the sound leg to the paretic leg for different patients with varying walking speeds. In this paper, we proposed a Synergetic Gait Prediction (SGP) model for rehabilitation LLEs with post-stroke patients, which can generate adaptive synergetic gait patterns for different patients with varying walking speeds. The proposed SGP model is based on Sequence-to-Sequence (Seq2Seq) neural networks with temporal attention mechanisms. In the training procedure of the proposed SGP model, a gait database with collected gait patterns from healthy subjects is employed to learn the parameters of SGP model. The SGP model takes current joint angles from the sound leg and a segment of observed history joint angles from both legs as input and predicts the future joint angles for the paretic leg. We compared the effectiveness of the SGP model with the Long Short Term Memory (LSTM) model, experimental results indicate that SGP model can generate synergetic gait patterns for different subjects via varying walking speeds with less prediction error.

This article investigates an output antisynchronization problem of multiagent systems by using an input-output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controller design, which is always invalid in practical scenarios. To address this issue, a new system representation is constructed by using just the available input/output data from the multiagent system. Then, a novel value iteration algorithm is proposed to compute the optimal control laws for the agents; moreover, a convergence analysis is presented for the proposed algorithm. In the implementation of the data-based controllers, an actor-critic network structure is established to learn the optimal control laws without the requirement of information of the agent dynamics. An incremental weight updating rule is proposed to improve the learning performance. Finally, simulation results are presented to demonstrate the effectiveness of the proposed antisynchronization control strategy.

Lower limb exoskeleton (LLE) has received considerable interests in strength augmentation, rehabilitation and walking assistance scenarios. For walking assistance, the LLE is expected to have the capability of controlling the affected leg to track the unaffected leg’s motion naturally. An important issue in this scenario is that the exoskeleton system needs to deal with unpredictable disturbance from the patient, which requires the controller of exoskeleton system to have the ability to adapt to different wearers. This paper proposes a novel Data-Driven Reinforcement Learning (DDRL) control strategy to adapt different hemiplegic patients with unpredictable disturbances. In the proposed DDRL strategy, the interaction between two lower limbs of LLE and the legs of hemiplegic patient are modeled in the context of leader-follower framework. The walking assistance control problem is transformed into a optimal control problem. Then, a policy iteration (PI) algorithm is introduced to learn optimal controller. To achieve online adaptation control for different patients, based on PI algorithm, an Actor-Critic Neural Network (ACNN) technology of the reinforcement learning (RL) is employed in the proposed DDRL. We conduct experiments both on a simulation environment and a real LLE system. Experimental results demonstrate that the proposed control strategy has strong robustness against disturbances and adaptability to different pilots.

Lower exoskeleton has gained considerable interests in walking assistance applications for both paraplegia and hemiplegia patients. In walking assistance of hemiplegia patients, the exoskeleton should have the ability to control the affected leg to follow the unaffected leg's motion naturally. One critical issue of walking assistance for hemiplegia patients is how to adapt the controller of both lower limbs with different patients. This paper presents a novel learning-based walking assistance control strategy for lower exoskeleton with hemiplegia patients. In the proposed control strategy, we modeled the control system of lower exoskeleton with hemiplegia patient as a Leader-Follower Multi-Agent System (LF -MAS). In order to adapt different patients with different conditions, reinforcement learning framework is utilized to adapt controllers online. In reinforcement learning framework with LF-MAS, we employed a Policy Iteration Adaptive Dynamic Programming (PI-ADP) algorithm, which aims to achieve better tracking control performance for lower exoskeleton with hemiplegia patient. We demonstrate the efficiency of proposed learning-based walking assistance control strategy in an exoskeleton system with healthy subjects who simulate hemiplegia patients. Experimental results indicate that the proposed control strategy can adapt different pilots with good tracking performance.