Actuation efficiency is a significant consideration for soft exosuits. It has important significance for reducing system weight and improving the effectiveness of walking assistance. Inspired by a fact that desired assistance from exosuits varies over the gait stage, this study developed a power modulation unit (PMU) to adapt to human walking characteristics, aiming to reduce the power requirement of exoskeletons. The underlying principles consist of: 1) dividing the gait phase into assistance period and idle period by different gait characteristics, where the terminal stance phase is defined as the assistance period and other phases are defined as the idle period; 2) motor power is stored in elastic elements during the idle period with mechanical advantage; 3) an amplified power bursts during the assistance period. Preliminary experiments were carried out on an ankle exosuit with human users. Experimental results indicate that the proposed PMU can amplify the output power by 3.1 times while affecting little on wearers’ normal gait, which implies the potential of the developed PMU for use in wearable actuation systems. Note to Practitioners—This work was motivated by the trade-off between multipath assistance and lightweight actuation. That is, multipath assistance typically necessitates multiple motors, consequently adding to the overall weight of the system. In this work, we developed a PMU to adapt to human walking characteristics (motor power is stored in elastic elements during the idle period and released when needed), aiming to reduce the requirement for peak actuation power. Experimental results show that the PMUs can provide an average power amplification ability of 3.1 times and does not interfere with normal human gait. The developed PMU holds great potential in walking-assisted exosuits or other devices for human movement assistance.

Accurate estimation of channel state information (CSI) is crucial to realize the full potential of massive multiple-input multiple-output (MIMO) communication systems. Despite the high accuracy of existing CSI estimators, the high computational complexity makes them impractical for real-world massive MIMO systems. So, developing a highly accurate and low-complexity estimator has been a long-standing challenge in the field of massive MIMO. In this paper, we present a channel estimation scheme that significantly reduces computational complexity while achieving high accuracy. Firstly, we design a two-stage training protocol whereby only part of the transmit and receive antennas are activated for signal emission or reception. Secondly, we apply the low-complexity Least-Square (LS) estimator to acquire two sub-blocks of the channel matrix. Relying on the inherent low-rank property of channel matrix, the complete channel matrix is reconstructed via a randomized matrix approximation technique. Thirdly, we consider three different power allocation schemes to further optimize the pilot power in 2-stage training process. The theoretical bounds of estimation error for our CSI estimator are derived, and the optimal power allocation strategy is thus obtained by minimizing this error bound. Numerical simulations are provided to demonstrate our proposed method. As shown, the theoretical error bound is tight, and the optimal power allocation can achieve the substantial gain. Our CSI estimator incurs the same complexity as the popular LS estimator, whilst the estimation accuracy is improved by ~5 dB, which has the great promise to new-generation massive MIMO communications.

Electric motors have been widely used as the actuators of robot and automation systems. This paper aims at achieving the high-precision position control of motor drive systems. For this purpose, a robust control scheme is presented by combining the internal model principle, the sliding mode technique and the extended state observer (ESO). The PID-type controller is firstly designed by using the internal model control (IMC) rules. Since the analysis of the IMC system is performed via a sliding surface, a robust sliding mode control (SMC) law is then synthesized to enhance the control ability of the system to uncertainties. However, this robust solution should make a trade-off between the chattering attenuation and the control accuracy. To handle this drawback, a linear ESO is employed to compensate the modeling errors for a higher control accuracy. The stability analysis is provided via a Lyapunov-based method, and the superiority of the proposed approach was validated by comparative experiments on a motor drive platform.