This paper proposes a novel model-free adaptive control method with practical prescribed performance for coupled mechatronic systems subject to uncertain friction, unknown delays, mismatched disturbance, and input quantization. Different from the existing ultra-local model-based controllers, a multi-input multi-output (MIMO) ultra-local model with an alpha gain matrix is used to approximate the plant within a short time interval. This allows that the method in this paper can be directly applied to MIMO systems without the decoupling process. A recursive least square technique is utilized to identify the alpha gain matrix. Additionally, a practical prescribed performance function applicable to any initial condition is designed to transform the tracking error, achieving predefined convergence time and pre-assignable tracking precision. Then, a global sliding mode control with adaptive switch gain is constructed to stabilize the transformed error. Afterward, the stability and convergence of the closed-loop system with the designed controller are analyzed by using Lyapunov theorem. The co-simulations on PUMA 560 robotic manipulator and iReHave exoskeleton, and experiment on 2-DOF upper-limb exoskeleton are completed. The obtained results demonstrate the effectiveness and superiority of the proposed control method.

Assist-as-needed (AAN) control for upper-limb rehabilitation exoskeletons is crucial for promoting active patient participation and facilitating neuroplasticity after a stroke. However, a significant challenge lies in designing controllers that can both seamlessly switch assistance levels in response to a patient's fluctuating ability and rigorously enforce safety-critical position constraints across different interaction modes. This study proposes a Multi-Mode Assist-as-Needed (MMAAN) control scheme to address these issues. The proposed framework features an outer loop that uses a virtual tunnel and a novel, continuously differentiable Robot-Assisted Level Metric (RALM) to ensure smooth transitions between Human-Dominated, Human-Robot shared, and Robot-Dominated modes. The inner loop implements a Performance-Varying Barrier Lyapunov Function controller, whose constraint boundaries dynamically adapt based on the RALM to guarantee safety and tracking accuracy. Experimental results with human participants demonstrate that the MMAAN controller automatically adjusts assistive force according to the user's performance while Maintaining tracking errors within prescribed boundaries. This approach offers a robust framework for personalized and safe robotic rehabilitation, potentially enhancing clinical outcomes by adapting to the unique needs of each patient.

A detailed modeling approach that utilizes the virtual work idea is developed for modeling the dynamical formulas of the rigid-flexible coupled robotic mechanisms (RFCRMs) with large beam-deformations across the horizontal plane. To follow the required angular positions of RFCRMs, a virtual robust linear quadratic state feedback (RLQSF) input is constructed using the converted full-actuated model in conjunction with an event-triggered robust adaptive fault-tolerant control (ETRAFTC) approach. The integration of virtual input and the proposed RLQSF law design enables simultaneous angular tracking and vibration elimination. To make up for the defective actuators with part loss of efficacy and evaluate the unknown fault parameters, an adaptive estimation law with a projection mapping operator is adopted. With the help of the Lyapunov direct approach, the angular position tracking errors and the flexible vibration of RFCRMs are demonstrated to converge to a tiny confined compact set with fewer communications. At last, the performance of the designed ETRAFTC is presented via three numerical scenarios.

Abstract This paper introduces a particle‐based framework for simulating the behavior of elastoplastic materials and the formation of fractures, grounded in Peridynamic theory. Traditional approaches, such as the Finite Element Method (FEM) and Smoothed Particle Hydrodynamics (SPH), to modeling elastic materials have primarily relied on discretization techniques and continuous constitutive model. However, accurately capturing fracture and crack development in elastoplastic materials poses significant challenges for these conventional models. Our approach integrates a Peridynamic‐based elastic model with a density constraint, enhancing stability and realism. We adopt the Von Mises yield criterion and a bond stretch criterion to simulate plastic deformation and fracture formation, respectively. The proposed method stabilizes the elastic model through a density‐based position constraint, while plasticity is modeled using the Von Mises yield criterion within the bond of particle paris. Fracturing and the generation of fine fragments are facilitated by the fracture criterion and the application of complementarity operations to the inter‐particle connections. Our experimental results demonstrate the efficacy of our framework in realistically depicting a wide range of material behaviors, including elasticity, plasticity, and fracturing, across various scenarios.

We present a particle-based smoothed particle hydrodynamics (SPH) framework for simulating hyperelastic materials with anisotropic stiffness models . While most elastic simulations predominantly rely on mesh-based approaches, such as the Finite Element method , the relationship between Lamé’s first parameter and Poisson’s ratio complicates the strict enforcement of volume conservation, making it challenging to stabilize simulations for common biological tissues like fat and muscle. In this paper, we couple an implicit divergence-free SPH solver with particle-based deformation gradient computation and apply various elastic energy functions to achieve incompressible elastic simulations. The incompressibility of elastic objects and collisions between different bodies are managed by the implicit SPH algorithm. We further incorporate anisotropic energy functions, constructed from the extrapolation of Cauchy–Green invariants, to introduce anisotropic properties to the objects. By integrating activation and contraction coefficients into the energy functions, particles can simulate muscle contractions and lift heavy objects. Our method can effectively represent elastic objects with varying mechanical properties across different directions and be further employed to mimic muscle contractions. Experiments demonstrate that our approach provides realistic simulations for a wide range of animal and human body movements.

Series elastic actuators (SEAs) have been the most popular compliant actuators as they possess a variety of advantages, such as high compliance, good backdrivability, and tolerance to shocks. They have been adopted by various rehabilitation robots to provide appropriate assistance with suitable compliance during human–robot interaction. For a multijoint SEA-driven rehabilitation robot, a big challenge is to develop an assist-as-needed (AAN) method without losing stability during uncertain physical human–robot interaction. For this purpose, this article proposes a human–robot interaction evaluation-based AAN method for upper limb rehabilitation robots driven by SEAs. First, in order to stabilize the SEA-level dynamics, singular perturbation theory is adopted to design a fast time-scale controller. Second, for the robot-level dynamics, an iterative learning algorithm is adopted for impedance adaption according to the task performance and human intention. The interaction force feedback is introduced for human–robot interaction evaluation, and the intensity of robotic assistance will be adjusted periodically according to the evaluation results. The stability of human–robot interaction is provided with the Lyapunov method. Finally, the proposed rehabilitation method is constructed and implemented on a two-degree-of-freedom SEA-driven robot. It handles the uncertain interaction in such a principle that correct movements will lead to less assistance for encouraging participation and incorrect movements will lead to more assistance for effective training. The proposed method adapts to the subject's intention and encourages higher participation by decreasing impedance learning strength and increasing allowable motion error. It can fit the participants with different motor capabilities and provide adaptive assistance when a specific trainee tries to change his/her participation during rehabilitation. The performance of the AAN method was validated with experimental studies involving healthy subjects.

Motion control of multi-joint Series Elastic Actuator (SEA)-driven robots still faces challenges including intrinsic oscillatory dynamics, high-order robotic dynamics, low-bandwidth inner loop, and dynamic nonlinearities. In this letter, a nonlinear disturbance observer (NDOB)-based robust controller with the singular perturbation theory is proposed to perform stable and precise motion control of multi-joint SEA-driven robots. First, a fast-time control term is designed according to the singular perturbation theory to stabilize the SEA-level dynamics. Then, for the robot-level dynamics, a NDOB is designed to estimate the effects of unmodeled dynamics and external disturbance. The NDOB is combined with a baseline computed torque controller (CTC) to construct a composite controller NDOB-CTC. In addition, bounded stability is achieved with Lyapunov-type analysis. Finally, the proposed controller was implemented on a 2 DOFs SEA-driven robot. Comparative experiments were conducted for validations.

This paper presents the results for model-independent control of uncertain n -degree of freedom robotic manipulators in the presence of external disturbances and backlash hysteresis. In order to improve the response characteristics of the system and attenuate the uncertainties, the developed robust model-free controller incorporates time delay control (TDC) as well as adaptive terminal sliding mode control (ATSMC) methods. Particularly, the time delay estimation is designed to estimate the unknown dynamics of the robotic manipulators by adopting the theory of TDC. Further, the ATSMC is utilized to obtain the robustness, finite-time convergence, and an adaptive tuning is exploited to deal with bounded derivative of unknown dynamics. The overall system stability is investigated by the Lyapunov theorem application and computed the finite convergence rate thereafter. Finally, a simulation comparison with an existing adaptive fractional-order terminal sliding mode control under backlash hysteresis is provided to illustrate the effectiveness and the superiority of the proposed method.

This paper investigates a force-position controller using neural network model free control with time-delay estimation(TDE-MFNNC) for the motion of the upper limb exoskeleton. The system establishes the muscle force prediction model based on the scaling principle of Hill model to estimate the torque provided by the upper limb. Then the upper limb exoskeleton is built in Solidworks as a virtual prototype which is used to instead the real exoskeleton model. We performed control stability to verify the effectiveness of the controller by comparing with TDE-iPD controller and with compensation the simulation result has a more satisfactory tracking performance.

This paper presents a new adaptive nonlinear control for trajectory tracking of non-affine nonlinear systems. The referred proposed controller which is based on a L2norm prescribed to minimize a trajectory tracking error has adaptive and optimal characters. Moreover with the systems output feedback, the proposed controller which dose not require any knowledge of nonlinear internal dynamics can be adopted to a recursive control structure and ensures the trajectory tracking of systems output. Finally, to validate the proposed method, an illustrative numerical example validates the controller performance and robustness.