This paper investigates the symbol error rate (SER) performance of the pilot-aided simultaneous communication and localisation (PASCAL) system with a ground-to-air model. A scenario where multiple drones transmit communication signals to a base station (BS), which needs to simultaneously decode the signals and continuously locate the drones’ positions during the communication session, is considered. The BS operates in two stages: first, it estimates the drones’ location parameters using pilot signals; second, it performs data detection by reconstructing the channel response based on the estimated location parameters. The theoretical analysis presented in this paper demonstrates that the distributions of the estimated location parameters follow Gaussian distributions of which the mean values are equal to the actual values of the locations, and their variances are determined by the achievable mean square error of the estimator. Using these distributions, the average SER is derived to quantify the impact of localisation errors on decoding performance. This analysis highlights the synergy between communication and localisation, providing valuable insights into the influence of localisation inaccuracies on the performance of location-aware communication systems. Simulations are conducted to validate the theoretical derivations.

The existing inverse optimal methods for canonical nonlinear systems assume that the system is modeled precisely and accurately, but dynamic uncertainties commonly exist and are unavoidable and difficult to model in practical engineering and industrial systems. This work removes this limitation and solves the problem of inverse optimal adaptive control for canonical nonlinear systems with dynamic uncertainties. Technically, a criterion on inverse optimality under dynamic uncertainties is newly proposed based on a new auxiliary system and a meaningful cost functional. With the new criterion, a robust adaptive fuzzy inverse optimal control scheme is proposed to design an inverse optimal controller, which, however, is not necessarily a stable controller. To solve this issue, a projection-based adaptation law is proposed to update the inverse optimal controller. Then, a small-gain approach is proposed to construct the links between inverse optimality and stability and to render that the closed-loop system is input-to-state practically stable. The proposed methods are successfully applied to industrial robots for demonstrations.

Compared to conventional back-support exoskeletons (BSEs) with two motors, BSEs driven by a single motor have the advantage of light weight. However, current single-motor BSEs have problems with accommodating asynchronous hip movements, achieving precise force control and efficient force transmission, and giving autonomy to users when walking. In this article, we propose a novel BSE with a differential series elastic actuator (D-SEA) for lifting assistance. The unique differential working principle can accommodate the angular difference between the hip joints and provide the same assistive torque at both hip joints. The D-SEA achieves precise force control with a custom controller based on accurate spring deflection feedback, and drives the hip joints via an efficient cable-roller mechanism. Taking advantage of the active backdrivability of the D-SEA, we proposed an intelligent assistive strategy that automatically provides adequate support for lifting tasks and grants autonomy to users during walking. In experiments, the BSE reduced the activation level of the back muscles by up to 40% during lifting, without increasing the activation of the back and leg muscles during walking.

In this paper, the problem of joint direction of arrival (DOA) and Doppler frequency estimation with array model defects is investigated. To this end, a two-dimensional (2D) robust multiple signal classification (MUSIC) algorithm is proposed, which is a generalization of the conventional MUSIC algorithm. The introduced algorithm is based on a colocated multiple-input-multiple-output (MIMO) radar composed of uniform linear arrays, in which a mix of calibrated and uncalibrated antennas exist in its transmitter, while all the receiver antennas are assumed to be uncalibrated. To be specific, the 2D R-MUSIC algorithm adopts a separation scheme to automatically obtain the signals associated with the calibrated subset of transmitter antennas and then eliminates the effect of the signals belonging to the uncalibrated transmitting and receiving antennas. Afterward, the localization is performed by modifying the conventional algorithms in accordance with the separation scheme. In addition, a 2D Genie maximum likelihood (ML) algorithm under the assumption of precisely known errors at the receiver is derived to provide a lower bound benchmark for the introduced method. The simulation results demonstrate the superiority of the proposed algorithms relative to the conventional ones.

Conventional localization algorithms, including maximum likelihood (ML) and multiple signal classification (MUSIC), are significantly influenced by array model imperfections. To address this problem, two generalized algorithms are proposed in this paper by extending the two conventional algorithms. The presented techniques are evaluated within the context of a passive uniform rectangular array (URA) where a rectangular subarray is assumed to be calibrated perfectly, while the remaining antennas incur array model errors. In this case, the performance of the conventional algorithms degrades seriously or even the operations fail. Nevertheless, the proposed algorithms can eliminate this issue by employing a separation technique. In specific, a separation strategy is applied to the introduced algorithms to automatically selects signals belonging to the calibrated subarray from the received signal matrix and eliminate the signals belonging to the uncalibrated antennas prior to conducting the azimuth-elevation-Doppler estimation. In order to reduce the computation complexity, the azimuth-elevation-Doppler estimation is divided into two sub-problems: the estimation process of the Doppler frequency and the estimation process of the azimuth-elevation angles. The simulation results are shown to demonstrate the efficiency and superiority of the proposed algorithms compared to the conventional algorithms.

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.