Self-balancing lower limb rehabilitation exoskeletons (SLLREs) allow individuals with lower limb dysfunction to walk without the use of crutches. Stable and human-like walking motions are crucial for SLLREs because achieving a close imitation of healthy human walking is a key goal in rehabilitation therapy. Existing SLLREs can realize stable walking but lack human-like features such as knee-stretched, heel-strike and toe-off. This paper designs a walking motion generator based on hierarchical optimization to generate a human-like walking motion with variable hip height, heelstrike, toe-off, and knee-stretched features. This generator consists of a knee-stretched optimizer and a stabilizing filter. Specifically, the knee-stretched optimizer realizes the stretched knee feature by optimizing the hip trajectory with varying heights. And the stabilizing filter realizes stable walking by optimizing the hip trajectory in the sagittal plane direction. To validate the effectiveness of the proposed human-like walking motion generator, walking experiments were conducted on SLLRE AutoLEE-G3 both in a simulation environment and the real world. The experimental results show that the humanlike walking motions look more natural and reduce the required torque for the knee joint compared with knee-bent walking.

Self-balancing walking exoskeletons (SBWEs), which enable paralyzed individuals to walk without assistive devices, have been increasingly employed in rehabilitation training. This paper proposes a Kelvin-Voigt viscoelastic model-based bioinspired viscoelastic compliance controller (BVCC) for a novel SBWE named AutoLEE-II, which features high structural rigidity, low leg inertia relative to center of mass (CoM), and small hip joint axis misalignment between the user and SBWE. First, a novel series-parallel hybrid mechanism is designed for AutoLEE-II. This mechanism, inspired by the lower limbs of humans, reduces hip axis misalignment between the user and SBWE, decreases leg inertia relative to CoM, and improves structural stiffness. Second, a BVCC mimicking biological muscle is proposed to introduce viscoelastic compliance to SBWE to maintain locomotion stability of the SBWE during standing and walking. The BVCC is robust to the variable physical parameters of different users. Finally, self-balancing walking experiments are conducted with AutoLEE-II with empty load, manikin load and human subject load to validate the performance of AutoLEE-II and the proposed compliance controller BVCC. Note to Practitioners—This paper aims to design a self-balancing walking exoskeleton (SBWE) that provides rehabilitation training exercise and walking assistance services for individuals with hemiplegia, paraplegia, and quadriplegia. First, we biomimetically designed the mechanical structure of the SBWE, named AutoLEE-II based on the distribution of human joints and connecting links. The bionic mechanism reduces axis misalignment between the SBWE and users, improves stiffness and reduces the inertia of the legs relative to center of mass. We then designed a bioinspired viscoelastic compliance controller (BVCC) based on the centroid dynamics model, which is robust to the physical properties of the user and introduces the SBWE with active compliance. Finally, self-balancing walking experiments with an empty load, a manikin load and human subject loads are performed to validate mechanical structure of the proposed AutoLEE-II and the locomotion stability of the physical parameter robust BVCC.

The design and development of new exoskeleton robots can help patients with lower limb paralysis realize autonomous walking. During the motion of an exoskeleton robot carrying patients, the inertia of the hip joint will have time-varying characteristics, which will cause fluctuations in the rotation angle and affect the walking stability of the exoskeleton robot. In this paper, we present the rotation angle control strategy for an exoskeleton robot assisted by paraplegic patients with the BP neural network tuning control strategy. First, based on the skeleton structure of human lower limbs, an exoskeleton robot with 12 degrees of freedom is designed and manufactured to help patients autonomously walk. Next, the dynamic model of the exoskeleton robot hip joint is established, which takes into account nonlinear factors such as transfer flexibility, friction torque, and time-varying load inertia. Then, BP neural networks are used to adjust the parameters of the position loop PID controllers in the hip joint, and the tracking error is reduced by adjusting the controller parameters in real time. Finally, walking experiments of the physical prototype of the exoskeleton robot show that the exoskeleton robot designed in this paper can help patients with lower limb paralysis walk autonomously, and the proposed control strategy can reduce the rotation angle tracking error of the hip joint.Note to Practitioners—This paper addresses the importance of dynamic modeling and control for the hip joint in the motion accuracy of exoskeleton robots. The mechanical structure of the proposed exoskeleton robot can realize the autonomous walking of patients with lower limb paralysis. The proposed dynamic modeling method is suitable for the servo system time-varying model of split-limb robots. In addition, the proposed BP neural network control strategy can improve the position control accuracy of time-varying systems, which is suitable for the real-time control of robots. Numerical simulation and physical experiments demonstrate the effectiveness of the proposed control strategy.

In this paper, considering overall deformations of the exoskeleton, we couple deformations relationship network (DRN) with fractional order viscoelastic (FOV) controller, proposing a novel DRN-FOV closed-loop control method, endowing exoskeleton with stable dynamic walking ability. Simply by utilizing only the data from the 6-axis force/torque sensors, the DRN can directly capture the mapping relationship between the foot reaction force/torque of the exoskeleton and its overall deformations. We introduce the FOV to eliminate disturbances and stabilize during walking tasks. The closed-loop control method directly compensates for the overall deformations of the exoskeleton and enables the wearer to walk stably wearing the exoskeleton. To assess the effectiveness of the proposed control method, walking tasks were effectively carried out on subjects with varying body parameters using the developed exoskeleton. The experimental results show that the DRN-FOV closed-loop control method accurately estimates and compensates for deformations, resulting in an improved dynamic walking ability of the exoskeleton with wearers.

Animals, including humans that have muscles with viscoelastic compliance can achieve improved stability. Hence, we investigate a biomimetic control framework with viscoelastic compliance and subsequently apply it to a self-balancing lower limb exoskeleton robot (SBLLER), to ensure stability during locomotion. This article presents a novel biomimetic viscoelastic compliance control framework (VCCF) for an SBLLER that enables wearers to walk without the need for crutches or other external stabilization tools during self-balancing locomotion and rehabilitation training tasks. First, we devised a biomimetic viscoelastic mathematical model (BVMM) and subsequently analyzed its viscoelastic properties. Second, the VCCF, which incorporates the desired and real center of mass (CoM) and a BVMM that connects the desired and real CoMs, is designed to introduce active viscoelastic compliance for an SBLLER so that the SBLLER can absorb the early landing impact forces and stabilize itself. In addition, the VCCF exhibits robustness to the physical parameters of diverse wearers and can effectively accommodate a wide range of subjects, which is essential for promoting the application of exoskeletons. Finally, the validity of the proposed VCCF is confirmed through disturbance and walking experiments.

Accurately estimating uncertain body disturbances is critical for the effective integration of wearable exoskeletons for active human users. In this article, considering nonlinear time-varying human disturbances, we propose a TDE-BVC feedback control method that performs biomimetic viscoelastic compliance (BVC) with transformer-based deformation estimator (TDE). The method provides human-exoskeleton with stable and dynamic walking capabilities. We developed a transformer-based end-to-end deformation estimation sequence network that simultaneously captures the mapping relationship between foot force/torque and exoskeleton deformation. Moreover, we integrated the BVC to eliminate the impact and external disturbances experienced by the human-exoskeleton, enabling it to closely follow a reference gait, and utilized Lyapunov’s theorem to prove its stability. The control strategy is independent of the parameters of the human exoskeleton. To evaluate the effectiveness of the proposed method, walking experiments were conducted on different subjects. Our results indicate that with only 6-axis force/torque sensors, the TDE-BVC controller could accurately estimate and compensate for the deformation of different human-exoskeletons in each control cycle$(p\lt 0.001)$, with robustly stable and adaptive dynamic walking within a bounded control error.