In this paper, we focus on the issue of adaptive fuzzy finite-time tracking control for a class of uncertain fractional-order nonlinear systems with external disturbance. A new finite-time fractional-order command filtered implementation scheme is presented for the adaptive backstepping method. In the command filtered implementation approach, analytical computation of the fractional derivatives of the stabilizing functions is not necessary. Therefore, the controller and adaptive law of the systems are easier to derive and implement. Based on the proposed command filter, adaptive backstepping technique and fractional Lyapunov’s direct method, a novel adaptive fuzzy finite-time controller is designed, which can guarantee that the tracking error converges to a small neighborhood of the origin in finite time, and other signals of the closed-loop systems are semi-global uniform ultimate bounded. In the design process of the controller, fuzzy logic systems (FLSs) are employed to approximate the unknown nonlinear functions, and an auxiliary function is adopted to compensate for the unknown external disturbance and approximation errors of FLSs. Finally, a simulation example is given to show the effectiveness and availability of the proposed control strategy.

Reinforcement learning (RL) and imitation learning (IL), especially equipped with deep neural networks, have been widely studied for autonomous robotic skill acquisition and control tasks. However, these methods and their extensions require extensive environmental interactions during training, which greatly prevents them from being applied to real-world robots. To alleviate this problem, we present an efficient model-free off-policy actor-critic algorithm for robotic skill acquisition and continuous control, by fusing the task reward with a task-oriented guiding reward, which is formulated by leveraging few and imperfect expert demonstrations. In this framework, the agent can explore the environment more intentionally, thus sampling efficiency can be achieved; moreover, the agent can also exploit the experience more effectively, thereby substantially improved performance can be realized simultaneously. The empirical results on robotic locomotion tasks show that the proposed scheme can lower sample complexity by 2-10 times in contrast with the state-of-the-art baseline deep RL (DRL) algorithms, while achieving performance better than that of the expert. Furthermore, the proposed algorithm achieves significant improvement in both sampling efficiency and asymptotic performance on tasks with sparse and delayed reward, wherein those baseline DRL algorithms struggle to make progress. This takes a substantial step forward to implement these methods to acquire skills autonomously for real robots.

A novel disturbance observer (DOB)-based switching control strategy is proposed to achieve fast, smooth, and accurate control performance in the presence of both external disturbances and internal uncertainties. This methodology consists of two DOBs designed with different frequency responses, and the switching mechanism is designed meticulously to schedule different DOBs. To further study the DOB-based control, a novel model transformation is introduced to alleviate the conservatism of robust stability of the closed-loop system. Thus, H _∞ theory is adopted to establish the cost function for performance evaluation and Q filter optimization. Different weighting functions can be selected for specific requirements of smooth dynamic responses or high disturbance rejection performance. A critical switching mechanism is proposed to schedule the different parameters according to the variation of output error and settling time. Experiments on dc servo system show that both smooth dynamic responses at transient state and high-accuracy performance at steady state can be achieved, and the effectiveness of the proposed control scheme is verified.

A soft, or continuum, robot has great potential in many practical fields with confined space, such as manufacturing system, electrical industry and other, due to its flexibility and benign bending attribute. However, its innate nonlinear dynamics and infinite degrees of freedom pose challenges both in modeling and control. How to build a proper model, which can be used to analyze the characteristics of soft robots, and how to design a robustly stabilized controller, are still open issues. In this work, we build a model for cable-driven continuum robots using a piecewise constant-curvature method. An improved state parameterization is proposed to avoid singularities, which produce pathological behaviors mainly on straight configurations of continuum robots. Then, a novel disturbance-rejection control framework is proposed with a tailored sliding mode controller to achieve stabilized control. Aiming at better robustness, we lump all system uncertainties, including unmodeled dynamics, unknown external disturbances, and unknown parameter variations, together as an extended state of the system. A linear extended state observer is then proposed to estimate the total uncertainties online, enabling compensation for their negative effects. Stability analysis and numerical simulations are carried out to show the feasibility and effectiveness of the proposed approaches.