This article addresses the finite-time optimal control problem for a class of nonlinear systems whose powers are positive odd rational numbers. First of all, a finite-time controller, which is capable of ensuring the semiglobal practical finite-time stability for the closed-loop systems, is developed using the adaptive neural networks (NNs) control method, adding one power integrator technique and backstepping scheme. Second, the corresponding design parameters are optimized, and the finite-time optimal control property is obtained by means of minimizing the well-defined and designed cost function. Finally, a numerical simulation example is given to further validate the feasibility and effectiveness of the proposed optimal control strategy.

Adaptive Neural Networks Finite-Time Optimal Control for a Class of Nonlinear Systems

Neural fuzzy approximation enhanced autonomous tracking control of the wheel-legged robot under uncertain physical interaction

Background Many lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user. Methods In addition to covering the recent publications on the control of lower-limb exoskeletons for gait assistance, an effort has been made to review the controllers independently of the hardware and implementation aspects. The common 3-level structure (high, middle, and low levels) is first used to separate the continuous behavior (mid-level) from the implementation of position/torque control (low-level) and the detection of the terrain or user's intention (high-level). Within these levels, different approaches (functional units) have been identified and combined to describe each considered controller. Results 291 references have been considered and sorted by the proposed classification. The methods identified in the high-level are manual user input, brain interfaces, or automatic mode detection based on the terrain or user's movements. In the mid-level, the synchronization is most often based on manual triggers by the user, discrete events (followed by state machines or time-based progression), or continuous estimations using state variables. The desired action is determined based on position/torque profiles, model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared and discussed in the paper. Conclusions By listing and comparing the features of the reviewed controllers, this work can help in understanding the numerous techniques found in the literature. The main identified trends are the use of pre-defined trajectories for full-mobilization and event-triggered (or adaptive-frequency-oscillator-synchronized) torque profiles for partial assistance. More recently, advanced methods to adapt the position/torque profiles online and automatically detect terrains or locomotion modes have become more common, but these are largely still limited to laboratory settings. An analysis of the possible underlying reasons of the identified trends is also carried out and opportunities for further studies are discussed.

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Review of control strategies for lower-limb exoskeletons to assist gait

Skill transfer learning for autonomous robots and human-robot cooperation: A survey

Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed.

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Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning

Humans can adapt to complex environments by voluntarily adjusting the impedance parameters and interaction force. Traditional robots perform tasks independently without considering their interactions with the external environment, which leads to poor flexibility and adaptability. Comparatively, humans can adapt to complex environments by voluntarily adjusting the impedance parameters and interaction force. In order to solve the problems of human–robot security and adaptability to unknown environment, a human-inspired control with force and impedance adaptation is proposed to interact with unknown environments and exhibit this biological behavior on the developed dual-arm exoskeleton robots. First, we propose a computationally model utilizing the sampled surface electromyogram (sEMG) signals to calculate the human arm endpoint stiffness and define a co-contraction index to describe the dynamic behaviors of the muscular activities in the tasks. Then, the obtained human limb impedance stiffness parameters and the sampling position information are transferred to the slave arm of the exoskeleton as the input variables of the controller in real-time. In addition, a variable stiffness observer is used here to compensate for the errors of the calculated stiffness by sEMG signals. The experimental studies of human impedance transfer control have been conducted to show the effectiveness of the developed approach. Results of the experimental suggest that the proposed controller can achieve human motor adaptation and enable the subjects to execute a skill transfer control by a dual-arm exoskeleton robot.

Human-Inspired Control of Dual-Arm Exoskeleton Robots With Force and Impedance Adaptation

Considering neuronal coordination between limbs, this article presents a study on the control of lower-limb exoskeletons for assistance of human gait by transferring the motor skills. The synergy-based robotic controller captures kinesiological information and biological signals from the healthy leg and generates intended motor patterns for the assisted leg in different gait phases of the slope walking behavior. First, we have developed a computationally efficient stiffness estimation model of the lower-limb joints and identified the experimental parameters in accord with the subject’s locomotion behavior. The estimated stiffness matrix at minimum muscular contraction is scaled by cocontraction index and mapped to joint stiffness to be utilized in the control design. Then, we have proposed the impedance matching model and realized human skills transfer by surface electromyography signals. Considering the uncertain dynamics of the human–robot system, we have developed an adaptive fuzzy approximator to estimate robot’s dynamic parameters and drive the robot tracking the referenced trajectories. The developed synergy-based control has been verified using three subjects with varying locomotor abilities. Results from these participants have shown a symmetrical and consistent adaptability between two legs with the synergy-based control, while the range of motion of the assisted leg in the affected side is more volitional and individualized.

Synergy-Based Control of Assistive Lower-Limb Exoskeletons by Skill Transfer

The multi-mode tracking control of robot manipulators with unknown dynamics is investigated in this paper. In view of varying loads, robot manipulators are modeled as multi-mode switched systems. Adaptive PID (proportion–integration–differentiation)–like switched controllers are designed via the error transformation and multiple Lyapunov function method, which can make the error system practical stability within prescribed time under a class of switching signals satisfying average dwell time. Furthermore, two cases under the healthy actuator and faulty actuator are discussed. For the case of the partial failure of the actuator, the fault-tolerant adaptive PID-like controller is presented, which consists of two parts. Finally, a simulation example is given to demonstrate the effectiveness of the proposed method.

Qiang Wei

Papers

Human-Inspired Control of Dual-Arm Exoskeleton Robots With Force and Impedance Adaptation

Synergy-Based Control of Assistive Lower-Limb Exoskeletons by Skill Transfer

Fault-tolerant adaptive PID switched control of robot manipulator based on average dwell time