Controlling the interaction forces between a human and an exoskeleton is crucial for providing transparency or adjusting assistance or resistance levels. However, it is an open problem to control the interaction forces of lower-limb exoskeletons designed for unrestricted overground walking. For these types of exoskeletons, it is challenging to implement force/torque sensors at every contact between the user and the exoskeleton for direct force measurement. Moreover, it is important to compensate for the exoskeleton's whole-body gravitational and dynamical forces, especially for heavy lower-limb exoskeletons. Previous works either simplified the dynamic model by treating the legs as independent double pendulums, or they did not close the loop with interaction force feedback. The proposed whole-exoskeleton closed-loop compensation (WECC) method calculates the interaction torques during the complete gait cycle by using whole-body dynamics and joint torque measurements on a hip-knee exoskeleton. Furthermore, it uses a constrained optimization scheme to track desired interaction torques in a closed loop while considering physical and safety constraints. We evaluated the haptic transparency and dynamic interaction torque tracking of WECC control on three subjects. We also compared the performance of WECC with a controller based on a simplified dynamic model and a passive version of the exoskeleton. The WECC controller results in a consistently low absolute interaction torque error during the whole gait cycle for both zero and nonzero desired interaction torques. In contrast, the simplified controller yields poor performance in tracking desired interaction torques during the stance phase

Pneumatic transmission has several advantages in developing powered ankle foot orthosis (AFO) systems, such as the flexibility in placing pneumatic components for mass distribution and providing high back-drivability via simple valve control. However, pneumatic systems are generally tethered to large stationary air compressors that restrict them for being used as daily assistive devices. In this study, we improved a previously developed wearable (untethered) custom compressor that can be worn (1.5 kg) at the waist of the body and can generate adequate amount of pressurized air (maximum pressure of 1050 kPa and a flow rate of 15.1 mL/sec at 550 kPa) to power a unilateral active AFO used to assist the dorsiflexion (DF) motion of drop-foot patients. The finalized system can provide a maximum assistive torque of 10 Nm and induces an average 0.03±0.06 Nm resistive torque when free movement is provided. The system was tested for two unilateral drop-foot patients. The proposed system showed an average improvement of 13.6° of peak dorsiflexion angle during the swing phase of the gait cycle.

High mechanical impedance, sensor resolution, and computing bandwidth are desirable for achieving stability in feedback control. In contrast, although the human body is physically inferior to man-made systems in terms of feedback control due to signal transmission delay, humans can achieve not only stable but also robust and adaptive control ability. For these reasons, the significance of the feedforward control of the human has been emphasized in neuroscience. In previous studies, virtual trajectory control and internal model hypotheses were used to explain the principle of human feedforward control in view of the mechanical stiffness of joints and the internal model of the brain, respectively. Inspired by these insights, in this paper, we focus on the relationship between the joint stiffness and inverse model accuracy and attempted to apply it to the field of robotics. We present a variable stiffness actuator developed using variable radius gear transmission inspired by muscle cross-bridge kinematics. The developed mechanism allows the joint stiffness to be accurately controlled without any sensor-based feedback control. Using the developed actuator, we conduct a feedforward motion generating experiment with respect to variations in the inverse dynamics model uncertainty and verified that joint stiffness can compensate for inverse model uncertainty and external disturbances. These results indicate that the developed variable stiffness actuator can be applied to the robotics field for feedforward applications and support the hypothesis that humans may utilize joint stiffness to compensate for the inverse dynamics model uncertainty.

Inspired from Hogan's myoelectric processor in 1980, this study presents a stochastic sEMG processing method to estimate the muscle activation level for manipulator control. Hogan's previous study showed the feasibility to estimate the muscle activation level with multi-channel sEMG under static force condition. However, it is difficult to continuously estimate muscle activation during dynamic contraction because of the nonlinear effects by the time-varying nature of sEMG. To enhance the performance of high SNR and rapid response in force-varying contraction, we propose a new method with statistical analysis extended from a whitening method of Hogan's study. The signals from eight sEMG channels were used to estimate the muscle activation level during isometric force-varying contractions. Experimentally, a two-DoF manipulator was controlled by input signals from the estimated muscle activation signal.

The closer distance between robots and human partners in the same space makes compliant robots essential in these days. While torque sensor based impedance control scheme suffers from insufficient bandwidth, musculoskeletal systems can achieve similar ability with smaller resources by using mechanically guaranteed dynamics. In this study, inspired by active stiffness mechanism of biological muscle, we developed a variable stiffness actuator which can realize precise joint stiffness without torque sensor based feedback control. The actuator consists of two antagonistically allocated DC motors with a variable radius gear transmission mechanism. The equilibrium position and joint stiffness can be independently controlled adjusting the activation level of motors by feedforward control. Theoretically predicted stiffness is realized well. In this paper, the design, functional principle and control of the actuator are introduced with analytical investigation.

The stabilization of man-made dynamic systems has been achieved by sensor-based state feedback control with high computational bandwidth, fast signal transmission speed, and stiff joints. In contrast, many biological systems can achieve similar or superior stable behavior with low computational bandwidth, slow signal transmission speed via the nervous system, and flexible joints. The concept of self-stabilization has recently been proposed and widely investigated to explain this phenomenon. Self-stabilization is defined as the ability to restore its original state after a disturbance without any feedback control. In this paper, the stabilizing function of a musculoskeletal system for arbitrary motion in the vertical plane is analytically investigated using Lyapunov stability criteria. Based on this investigation, the method of designing a new actuator that can assign a self-stabilizing function to a robotic arm is introduced and a self-stabilizing manipulator is physically realized. As a result, a theoretically predicted self-stabilizing function is experimentally verified and explains why a biological musculoskeletal system can be stabilized with feedforward control.

The stabilization of man-made artificial systems has been achieved by sensor based state feedback control with high computational bandwidth and high stiffness structures. In contrast, many biological systems have been achieved similar or superior stable behavior with low speed signal transmission via nervous systems, which is easy to introduce unstable performance from a control engineering perspective. In order to explain this phenomenon, the concept of self-stabilization has recently been proposed and investigated. Self-stabilization is defined as the ability to restore its original state after a disturbance without any feedback control. In this paper, the self-stabilizing function of a musculoskeletal system for arbitrary motion in the vertical plane is analytically investigated using Lyapunov stability theory. Based on this investigation we propose a design method to realize the self-stabilizing function of a musculoskeletal system, and experimentally verify that the self-stabilizing function can be physically realized by the proposed Lyapunov function.