Reinforcement learning-based integrated active fault diagnosis and tracking control.

Reliable and real-time active diagnosis of system faults with uncertainties is strongly dependent on the input design. This paper establishes a data-driven framework for integrated design of active fault diagnosis and control while ensuring the tracking performance. To be specific, the input design is formulated as a constrained optimization problem that can be solved with the aid of constrained reinforcement learning algorithms. Moreover, based on the maximum mean discrepancy metric, a novel active fault isolation scheme is proposed to implement model discrimination using system outputs. At the end, the effectiveness of the proposed approach is evaluated in two case studies in the presence of probabilistic disturbances and uncertainties.

Efficient Remediation of p-chloroaniline Contaminated Soil by Activated Persulfate Using Ball Milling Nanosized Zero Valent Iron/Biochar Composite: Performance and Mechanisms.

In this study, efficient remediation of p-chloroaniline (PCA)-contaminated soil by activated persulfate (PS) using nanosized zero-valent iron/biochar (B-nZVI/BC) through the ball milling method was conducted. Under the conditions of 4.8 g kg-1 B-nZVI/BC and 42.0 mmol L-1 PS with pH 7.49, the concentration of PCA in soil was dramatically decreased from 3.64 mg kg-1 to 1.33 mg kg-1, which was much lower than the remediation target value of 1.96 mg kg-1. Further increasing B-nZVI/BC dosage and PS concentration to 14.4 g kg-1 and 126.0 mmol L-1, the concentration of PCA was as low as 0.15 mg kg-1, corresponding to a degradation efficiency of 95.9%. Electron paramagnetic resonance (EPR) signals indicated SO4•-, •OH, and O2•- radicals were generated and accounted for PCA degradation with the effect of low-valence iron and through the electron transfer process of the sp2 hybridized carbon structure of biochar. 1-chlorobutane and glycine were formed and subsequently decomposed into butanol, butyric acid, ethylene glycol, and glycolic acid, and the degradation pathway of PCA in the B-nZVI/BC-PS system was proposed accordingly. The findings provide a significant implication for cost-effective and environmentally friendly remediation of PCA-contaminated soil using a facile ball milling preparation of B-nZVI/BC and PS.

Probabilistic Motion Prediction and Skill Learning for Human-to-Cobot Dual-Arm Handover Control.

In this article, we focus on human-to-cobot dual-arm handover operations for large box-type objects. The efficiency of handover operations should be ensured and the naturalness as if the handover is going on between two humans. First of all, we study the human-human dual-arm large box-type object natural handover process to guide this research. Then, for efficiency, we combine the probabilistic approach with the online learning algorithm to predict the beginning of the handover task and handover positions. The online updating probabilistic models can deal with not only human givers' regular motion patterns but also their irregular motion patterns. Then, to guarantee that human givers can perform handover operations naturally, we apply the probabilistic robot skill learning method kernelized movement primitives (KMPs) to adapt the learned receiving skills and fulfill some constraints for safety based on online predicted results. Furthermore, we give special attention to the dual-arm grasp strategy and control design to guarantee a stable grasp. In addition, we equip this handover system on a Baxter cobot and extend its grippers to make it more suitable for dual-arm handover operations. The experimental results show that the proposed handover system can solve human-to-cobot dual-arm handover operations for large box-type objects naturally and efficiently.

Disturbance Observer-Based Neural Network Control of Cooperative Multiple Manipulators With Input Saturation.

In this paper, the complex problems of internal forces and position control are studied simultaneously and a disturbance observer-based radial basis function neural network (RBFNN) control scheme is proposed to: 1) estimate the unknown parameters accurately; 2) approximate the disturbance experienced by the system due to input saturation; and 3) simultaneously improve the robustness of the system. More specifically, the proposed scheme utilizes disturbance observers, neural network (NN) collaborative control with an adaptive law, and full state feedback. Utilizing Lyapunov stability principles, it is shown that semiglobally uniformly bounded stability is guaranteed for all controlled signals of the closed-loop system. The effectiveness of the proposed controller as predicted by the theoretical analysis is verified by comparative experimental studies.

Neural-Learning-Based Control for a Constrained Robotic Manipulator With Flexible Joints.

Nowadays, the control technology of the robotic manipulator with flexible joints (RMFJ) is not mature enough. The flexible-joint manipulator dynamic system possesses many uncertainties, which brings a great challenge to the controller design. This paper is motivated by this problem. In order to deal with this and enhance the system robustness, the full-state feedback neural network (NN) control is proposed. Moreover, output constraints of the RMFJ are achieved, which improve the security of the robot. Through the Lyapunov stability analysis, we identify that the proposed controller can guarantee not only the stability of flexible-joint manipulator system but also the boundedness of system state variables by choosing appropriate control gains. Then, we make some necessary simulation experiments to verify the rationality of our controllers. Finally, a series of control experiments are conducted on the Baxter. By comparing with the proportional-derivative control and the NN control with the rigid manipulator model, the feasibility and the effectiveness of NN control based on flexible-joint manipulator model are verified.

Adaptive Neural Network Control of a Flapping Wing Micro Aerial Vehicle With Disturbance Observer.

The research of this paper works out the attitude and position control of the flapping wing micro aerial vehicle (FWMAV). Neural network control with full state and output feedback are designed to deal with uncertainties in this complex nonlinear FWMAV dynamic system and enhance the system robustness. Meanwhile, we design disturbance observers which are exerted into the FWMAV system via feedforward loops to counteract the bad influence of disturbances. Then, a Lyapunov function is proposed to prove the closed-loop system stability and the semi-global uniform ultimate boundedness of all state variables. Finally, a series of simulation results indicate that proposed controllers can track desired trajectories well via selecting appropriate control gains. And the designed controllers possess potential applications in FWMAVs.