A large number of the WRLSs (wearable robots lumbar support) research have been presented for working efficient increase and injure risk reduction in recent years. However, the previous research can only complete the sagittal-plane lifting task, which can not adapt to the mixed lifting tasks in the actual work scene. Therefore, we presented a novel lumbar assisted exoskeleton with mixed lifting tasks by various postures based on position control, which can not only carry out the lifting tasks of sagittal-plane, but also complete the lifting tasks of sides. First, we proposed a new generation method of raising reference curves that can generate assistance curve for each user with each task, which is very convenient in mixed lifting tasks. Then, an adaptive predictive controller was designed to track the reference curves of different users under different loads, the maximum tracking errors of the angles are 2.2° and 3.3° respectively at 5kg and 15kg, and all the errors are within 3%. Compared to the condition of no exoskeleton, the average RMS (root mean square) of EMG (electromyography) for six muscles are reduced by 10.33 ± 1.44%, 9.62±0.69%, 10.97±0.81% and 14.48±2.11% by lifting loads with stoop, squat, left-asymmetric and right-asymmetric respectively. The results demonstrate that our lumbar assisted exoskeleton presents outperformance in mixed lifting tasks by various postures.

How to improve the walking efficiency while ensuring the wearability is an important issue of lower limb exoskeletons. Active devices can provide greater forces, while the passive devices have advantage in weight. We presented a multi-joint exoskeleton with active hip extension assistance and passive ankle plantarflexion assistance in this work. An admittance controller based on a feedforward model was proposed to track the desired active force of the hip extension. An underfoot clutch mechanism was adapted to realize the passive ankle plantarflexion assistance. To assess the efficacy of the multi-joint exoskeleton in assisting walking, we conducted comprehensive experiments to evaluate the force tracking performance, lower limb muscle activities and metabolic cost. The results demonstrated that: (i) The average tracking error of the peak hip extension assistance force from three subjects was less than 3%. (ii) The reductions of normalized root-mean-square EMG in the lateral soleus, medial soleus and gluteus maximus of eight subjects achieved 15.33%, 11.11%, and 3.74%, respectively. (iii) The average metabolic cost of six subjects was reduced by 10.41% under exoskeleton on (EO) condition comparing to the condition of walking with no exoskeleton (NE). This work proved that the concept of the multi-joint exoskeleton with active-passive assistance can improve the walking efficiency.

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.

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.

This paper describes a dual-loop control framework of self-balancing lower-limb exoskeletons (SBLLEs) to estimate deformations and to stabilize SBLLEs for assisted walking tasks conducted on wearers with diverse physical parameters. First, considering the nonlinear time-varying characteristic of deformation and the time dependency and periodic nature of walking motion, a bidirectional long short-term memory (Bi-LSTM) neural network is utilized to estimate multiple deformations. In particular, the force sensor and Bi-LSTM-based deformation estimator (FBDE) loop are used to estimate and compensate for the deformation based on the force and moment signals. Second, a physical parameter-independent controller (PPIC) based on centroidal dynamics is proposed to stabilize exoskeletons that can accommodate wearers with varying physical parameters. The PPIC loop is also based on force and moment signals, indicating that only six-dimensional force sensors are necessary to estimate deformation and stabilize SBLLEs. Finally, a series of experiments is conducted on three subjects with varying physical parameters to validate the effectiveness of the dual-loop control framework. The results of walking experiments show that the dual-loop control framework can estimate deformation and stabilize the SBLLE during walking under different loads.

Exoskeleton robot can assist people and reduce energy consumption when they walk with heavy weight, so as to protect their health and travel longer distances. This work analyzes the cross gait during walking and designs a dual-drive four joint time-sharing assistance exoskeleton system, which controls the four joints through two motors to realize the assistance to the wearer’s movement process. The control curve and adaptive control algorithm are designed to help different people with various walking gaits and speeds, the effectiveness of exoskeleton system is proved by testing metabolism. When the exoskeleton wearer carries 25 kg weight (load equal to 36% of body mass) and travels at the average speed of 5 km/h, the metabolic rate of the exoskeleton wearers decreases by an average of 7.79%, the reduction magnitude is comparable to the effect of taking off 7.33 kg during walking.