For individuals with limited mobility who are bedridden for extended periods, providing comfortable assisted feeding services is one of the most significant actions to enhance their quality of life. Despite the development of various feeding assistive robots, there remain limitations in terms of interaction convenience and safety, which restrict the overall feeding experience for users. To address these challenges, this study first establishes a feeding assistive robot system that integrates multimodal interaction methods. Furthermore, we propose an interactive feeding method that combines both safety and comfort. This method utilizes visual recognition to detect the user’s active meal intent, food selection preferences, and chewing status. Additionally, based on a Large Language Model (LLM), a monitoring thread is designed to conduct voice interactions regarding the user’s ambiguous intentions, temporary changes in intent, emergency situations, and risky behaviors throughout the feeding process. Comprehensive experimental results demonstrate that the proposed multimodal interaction method, which aligns with the natural eating patterns, incorporates both language and visual interactions, the two most convenient forms for users. It also matches force sensing and pose control techniques during the feeding stages, thereby enhancing the flexibility and safety of the assisted feeding system.

This article focuses on the Pareto optimal issues of nonlinear game systems with asymmetric input saturation under dynamic event-triggered mechanism (DETM). First, the safe control is guaranteed by transforming the system with safety constraints into the one without state constraints utilizing barrier function. The united cost function integrating nonquadratic utility function is constructed to provide the foundation to achieve the Pareto optimal solutions. Then, the adaptive dynamic programming method with concurrent learning is proposed to approximate the Pareto optimal strategies wherein both current and historical data are utilized. To further lessen the consumptions of computation/communication resources, the DETM is integrated into the adaptive algorithm framework which can avoid Zeno phenomena. All the signals of the closed-loop system are proved to be uniformly ultimately bounded. Finally, the simulation results are given to validate the effectiveness of the proposed method from several aspects.

In this article, the tracking problem of the adaptive sliding-mode control (SMC) design for human support robots based on a disturbance observer is investigated. First, a finite-time controller using nonsingular fast terminal SMC is proposed. Then, a robust disturbance observer is developed to estimate system uncertainties and disturbances. Simultaneously, to deal with the unknown bounded disturbance observer error, an adaptive control technology is developed. Furthermore, the proposed controller is synthesized to ensure that the tracking errors can be stabilized in finite time. Finally, simulations are performed to demonstrate that human support robots employing the proposed controller can converge to the desired trajectory.

This article conducts the issue of event-triggered reduced-order filtering for continuous-time semi-Markov jump systems with imperfect measurements as well as randomly occurring uncertainties (ROUs). Specifically, the sojourn-time-dependent transition probability matrix (TPM) is presumed to be polytopic and a quantizer is introduced to quantize output signals aiming to reflect the reality. Both ROUs and sensor failures are generated by individual random variables belonging to be mutually independent Bernoulli-distributed white sequences. First, sufficient conditions for the existence of the event-triggered reduced-order filter are obtained by utilizing the dissipativity-based technique to ensure the asymptotical stability with a strictly dissipative performance of the filtering error system. The time-varying TPM is then fractionalized, which enhances the results as stated. Furthermore, the required reduced-order filter parameters are obtained by introducing slack symmetric matrix as well as cone complementarity linearization algorithm. The effectiveness of the suggested event-triggered reduced-order filter design method is shown through simulation results.

This article addresses the problem of event-triggered dynamic output feedback controller design for networked Takagi-Sugeno (T-S) fuzzy systems subject to actuator failure and deception attacks. In order to save network resources effectively, two event-triggered schemes (ETSs) are introduced to test whether the measurement output and control input are transmitted under network communication. While the ETS brings advantages, it also leads to a mismatch between the premise variables of the system and the controller. To solve this problem, an asynchronous premise reconstruct method is considered, which relaxes the condition of the previous results that the premises of the plant and the controller are synchronous. Furthermore, two crucial factors, including actuator failure and deception attacks, are taken into consideration simultaneously. Then, the mean square asymptotic stability conditions of the resultant augmented system are derived by utilizing the Lyapunov stability theory. Besides, controller gains and event-triggered parameters are co-designed with the help of linear matrix inequality techniques. Finally, a cart-damper-spring system and a nonlinear mass-spring-damper mechanical system are presented to verify the theoretical analysis.

This article investigates a class of finite-time cooperative tracking problems of heterogeneous mixed-order multiagent systems (MASs) with higher-order dynamics. Different from the previous works of heterogeneous MASs, the agents in this study are considered to have different first-, second-, or even higher-order nonlinear dynamics. It means that, according to different tasks and situations, the following agents can have nonidentical orders or different numbers of states to be synchronized, which is more general for the practical cooperative applications. The leader is a higher-order nonautonomous system and contains full state information to be synchronized for all agents with mixed-order dynamics. Accordingly, the spanning tree is defined based on the specific state rather than on the agent to guarantee that each following agent can receive adequate state information. Distributed control protocols are designed for all agents to achieve the ultimate state synchronization to the leader in finite time. The Lyapunov approach is used for the stability analysis and a practical example of mixed-order mechanical MASs verifies the effectiveness and performance of the proposed distributed control protocols.

This article investigates the problem of fixed-time event-triggered output consensus tracking for high-order multiagent systems (MASs) under directed interaction graphs. First, a fixed-time event-triggered distributed observer and triggering functions are proposed. Next, fixed-time convergence of the presented distributed observer is proved by the Lyapunov function approach, and an analysis is conducted to show the proposed distributed observer excludes zeno behavior. Then, an event-triggered adaptive dynamic surface fixed-time controller is designed to stabilize the tracking error system. Finally, simulation results are given to show the effectiveness and superiority of the consensus scheme developed. The contribution of this article is to present a novel event-triggered fixed-time distributed observer and a novel fixed-time controller, which can reduce frequency of communication and control update, avoid continuous monitor, exclude zeno behavior, eliminate the effect of mismatched disturbance caused by observation error, and achieve practical fixed-time output consensus tracking of high-order MAS under directed interaction graphs.

Care robots have been developed to address the shortage of caregivers in hospitals and homes. However, providing care services considering abstract physiological desires (e.g., hunger, thirst, hot, and cold) rather than specific commands (e.g., serve a cup of water) has not been fully investigated. The key to such services that satisfy abstract requests, e.g., thirst, is to evaluate the available options (e.g., milk, tea, biscuit, and Coke) with the consideration of the care recipient’s preferences. In this study, we argue that both general taste (long-term care preferences) and sequential influence [short-term care preferences (STCPs)] should be considered for a robot to provide satisfying services. In this article, a long STCP model (LSTCPM) is introduced. The model can be initialized and updated incorporating the feedback from the care recipients. Then, a virtual agent-based simulation system is presented, which provides a way to evaluate and fine-tune the LSTCPM prior to its deployment to actual care robots. Finally, experiments were conducted in a real household domain using our care robot KUT-PCR to demonstrate the feasibility of the proposed approach.

Load-carrying capability is an essential criterion in legged robots' practical application. This paper proposes an unpowered hydraulic auxiliary system to improve the legged robot's loading capability and energy efficiency. For humans, it has been widely hypothesized that intra-abdominal pressure can reduce potential injurious compressive force imposed on spinal discs when a person lifts heavy objects. Inspired by this human biomechanical phenomenon, we design a novel loading-carrying strategy using hydraulic cylinders, valves and accumulators. Different from ordinary powered hydraulic systems, this design provides continuous support force to share the load applied on knee joint actuator without consuming extra energy. The bent-leg theoretical model is constructed to validate the design and analysis. A bipedal hydraulic-assisted electric leg prototype (HyELeg) is fabricated and tested for squatting and walking gaits. The results show that with hydraulic assistance, the prototype can save energy by 45.9% for squatting with a load of 125% of its own body weight, and the walking performance is enhanced by 16.9% in energy efficiency with carrying a load of 67.5% of its own body weight.

This article investigates the problem of event-triggered-based sliding mode control (SMC) for a class of Takagi-Sugeno (T-S) fuzzy systems with actuator failures and signal quantization. The classical dynamic uniform quantization strategy is employed to quantize data in both sensor-controller side and controller-actuator side. A new event-triggered adaptive SMC scheme is proposed to stabilize the fault closed-loop systems, where the event-triggered condition is based on quantized state vectors. Reachability of the proposed sliding surface can be ensured by the designed control scheme. Furthermore, the existence of minimal inter-event time and sufficient conditions under which zeno behavior can be avoided is analyzed and presented. Finally, two examples are provided to demonstrate the effectiveness of the proposed new design techniques.

This article addresses the problem of continuous-time dynamic sliding mode control for Takagi-Sugeno fuzzy nonlinear systems based on an event-triggered mechanism. First, a novel sliding mode dynamic system is established by introducing a corresponding sliding mode function and an efficient event-triggered mechanism. Then, based on a new Lyapunov function and the reciprocally convex method, sufficient conditions are presented to guarantee that the constructed network sliding mode dynamics is asymptotically stable. It is shown that the trajectories of the sliding mode dynamic system can converge into a bounded domain of a sliding mode surface in finite time. Furthermore, the explicit expression of the desired sliding mode controller is given in the form of linear matrix inequalities. Finally, the applicability and effectiveness of the proposed design techniques are demonstrated by a numerical example.

This article considers the decentralized stabilization problem of stochastic interconnected systems. The considered stochastic systems simultaneously contain unknown interconnections, power integrator dynamics, and time-varying delays. First, both state and output feedback controllers are presented for the stochastic interconnected nonlinear systems. Novel barrier Lyapunov–Krasivskii functions are used for the stability analysis of the proposed method. Second, fuzzy systems are adopted to approximate the complex and unknown dynamics in the systems with guaranteed compact set conditions. Based on barrier functions and switching mechanism, it is strictly proved that all the states will always remain in a compact set. Third, an adaptive compensator is presented to handle the resulting errors from input quantization, which finally renders asymptotic stability of the whole interconnected systems. Simulations are conducted to verify the main results.

This article studies the adaptive neural controller design for a class of uncertain multiagent systems described by ordinary differential equations (ODEs) and beams. Three kinds of agent models are considered in this study, i.e., beams, nonlinear ODEs, and coupled ODE and beams. Both beams and ODEs contain completely unknown nonlinearities. Moreover, the control signals are assumed to suffer from a class of generalized backlash nonlinearities. First, neural networks (NNs) are adopted to approximate the completely unknown nonlinearities. New barrier Lyapunov functions are constructed to guarantee the compact set conditions of the NNs. Second, new adaptive neural proportional integral (PI)-type controllers are proposed for the networked ODEs and beams. The parameters of the PI controllers are adaptively tuned by NNs, which can make the system output remain in a prescribed time-varying constraint. Two illustrative examples are presented to demonstrate the advantages of the obtained results.

With the aim to solve the problems related to the power distribution and current chattering in a distributed energy storage system (DESS), which can be considered as a multiagent system in dc microgrid, a model-free cooperative sliding mode control scheme with a directed-graph-based observer is proposed in this article. First, a state-of-charge (SoC)-based droop control strategy is combined with a directed-graph-based voltage regulation method to balance the SoCs of each energy storage unit (ESU), allocate the power, and maintain the bus voltage stability. Besides, a directed-graph-based observer is designed to estimate the battery current considering the communication between the ESUs, and based on this observer, an estimation algorithm for adaptive coefficients is developed. Then, the multiagent sliding mode control strategy with an antiwindup compensator of input constraint is developed to track the reference current with little chattering in the DESS, and the stability proof is given. Finally, simulations are conducted to validate the effectiveness and superiority of the newly designed control strategy for the DESS.

The cooperative control problem of nonlinear multiagent systems is studied in this paper. The followers in the communication network are subject to unmodeled dynamics. A fully distributed neural-networks-based adaptive control strategy is designed to guarantee that all the followers are asymptotically synchronized to the leader, and the synchronization errors are within a prescribed level, where some global information, such as minimum and maximum singular value of graph adjacency matrix, is not necessarily to be known. Based on the Lyapunov stability theory and algebraic graph theory, the stability analysis of the resulting closed-loop system is provided. Finally, an numerical example illustrates the effectiveness and potential of the proposed new design techniques.

This paper concentrates on the problem of event triggered adaptive leader-following control for a class of multiagent systems. The considered systems contain completely unknown interconnections and arbitrary asynchronously switching. To handle these nonlinearties, a new hierarchical barrier Lyapunov method is proposed. Based on this method, a novel adaptive fuzzy control strategy is designed to make the output tracking errors of the multiagent systems converge to a small neighborhood of origin. Stability analysis shows that the proposed method can guarantee the compact set conditions of the fuzzy logic systems during the entire design process. Meanwhile, to reduce the computational burden, novel event triggered mechanisms are presented for both the transmission between two connected agents, and the communication between the sensor and the controller in one agent. An illustrative example is presented to verify the effectiveness of the proposed controller.

The dexterity of body motion when performing skills are being actively studied. In this paper, singular value decomposition is used to extract the dexterous features from the time-series data of body motion. A matrix is composed by overlapping the subsets of the time-series data. The left singular vectors of the matrix are extracted as the patterns of the motion and the singular values as a scalar, by which each corresponding left singular vector affects the matrix. A gesture recognition experiment, in which we categorize gesture motions with indexes of similarity and estimation that use left singular vectors, was conducted to validate the method. Furthermore, in order to understand the features better, the features of the left singular vectors were described as fuzzy sets, and fuzzy if-then rules were used to represent the knowledge.

The knowledge remembered by the human body and reflected by the dexterity of body motion is called embodied knowledge. In this paper, we propose a new method using singular value decomposition for extracting embodied knowledge from the time-series data of the motion. We compose a matrix from the time-series data and use the left singular vectors of the matrix as the patterns of the motion and the singular values as a scalar, by which each corresponding left singular vector affects the matrix. Two experiments were conducted to validate the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with indexes of similarity and estimation that use left singular vectors. The proposed method obtained a higher correct categorization ratio than principal component analysis (PCA) and correlation efficiency (CE). The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability. The first singular values derived from the walking acceleration were suggested to be a reliable criterion to evaluate walking disability. Finally we discuss the characteristic and significance of the embodied knowledge extraction using the singular value decomposition proposed in this paper.

Motion time-series data observed with various sensing systems are usually analyzed to extract embodied knowledge which is remembered by the human body and reflected by the dexterity in the motion of the body. A method based on singular value analysis through divided time-series data (SVA-DTS) is proposed for extracting features from time-series data. Matrices are composed from the subsets of time-series data and the left singular vectors of the matrices are extracted as the patterns of the motion and the singular values as a scalar, by which the corresponding left singular vectors affects the matrices. The SVA-DTS was applied to a walking difficulty evaluation experiment in which three levels of walking difficulty were simulated by restricting the right knee joint. The accelerations of the middles of the shanks and the back of the waist were measured. Singular values were calculated from the normalized acceleration time-series data with the SVA-DTS. The results showed that the first singular values inferred from the acceleration data of the right shank significantly related to the increase of the restriction to the right knee. The first singular values of the acceleration data of the right shank were suggested to be reliable criteria to evaluate walking difficulty. We visualize the first singular values in a 3D space to provide intuitive information about walking difficulty which can be used as a tool for evaluating walking difficulty.

Embodied knowledge is the knowledge remembered by the human body and reflected by the dexterity in the motion of the body. In this paper, we propose a new method using singular value decomposition for extracting embodied knowledge from the time-series data of the motion which is measured with various sensors such as an accelerometer, a motion capture system and a force sensor. We compose a matrix from the the time-series data and use the left singular vectors of the matrix as the patterns of the motion and the singular values as a scalar, by which each corresponding left singular vector affects the matrix. Two experiments were conducted to testify the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with the indexes of similarity and estimation using left singular vectors. The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability using a 3D hyperplane constructed by the singular values. Finally we discuss the characteristic and significance of the embodied knowledge extraction using singular value decomposition proposed in this paper.

Walking is a fundamental human ability necessary for everyday life. We have developed an omnidirectional walker (ODW) for walking support to those who have walking disabilities. It is necessary for the ODW to know which direction the user is intending to go during walking support. A novel interface is proposed for the ODW to recognize directional intention according to the user's forearm pressures which are measured by force sensors embedded in the armrest. The relationship between forearm pressures and directional intention was extracted as fuzzy rules and an algorithm is proposed for directional intention identification based on distance type fuzzy reasoning method. In this paper, we conduct walking support experiments with the proposed method. The results show that the algorithm is applicable to directional control in walking support.

Walking is a vital exercise for health promotion and a fundamental ability necessary for everyday life. In the authors' previous studies, an omni-directional walker (ODW) has been developed for walking rehabilitation and walking support. In the case of walking support, it is necessary for the ODW to know which direction its user is intending to go according to the user's manipulation. However, since the directional intention and physical manipulation of a user are not always consistent with each other, it is an important subject to identify the real directional intention from physical manipulation. In this paper, a method is proposed to recognize a user's directional intention according to the pressures, which are measured by sensors embedded in the ODW's armrest, from the user's forearms. Firstly, the relationship between forearm pressure and directional intention was extracted as fuzzy rules. Then an algorithm is proposed for directional intention identification based on Distance-Type Fuzzy Reasoning Method (DTFRM). Finally, the effectiveness of the algorithm is verified by experiments, which show that reasoning results are consistent with the intended directions.

In this paper, we discuss sport technique evaluation using motion analysis model by neural networks and data mining methods. For students of university, we recorded the continuous forehand stroke of the table tennis in the video frames, and analyzed the trajectory pattern of nine marking points attached at subject's body with a coach's technique evaluation and the motion analysis model. As a result, we obtained some technique rules classified member of table tennis club, middle level player and beginner as fuzzy rules, and also estimated the movement of the marking points to improve in table tennis technique.