The wastewater treatment system is a complex unknown system with nonlinear and uncertain characteristics. It is necessary to control the concentration of the dissolved oxygen and the nitrate nitrogen at the set value in the wastewater treatment process. However, traditional control methods are difficult to meet the accuracy requirements for the required concentration value. At the same time, the digital twin (DT) technology has been widely applied to practical industrial systems like the wastewater treatment system in recent years. Based on the above backgrounds, an online DT adaptive critic design (DTACD) is developed by combining the long short-term memory (LSTM) neural network with the action-critic structure. First, a set of historical data is collected, which is used to build a digital model using LSTM. Then, the digital model is used to guide the real wastewater treatment system to control the nitrate nitrogen concentration and the dissolved oxygen concentration at set values. Finally, we select the Benchmark Simulation Model No.1 as the model for the simulation experiment. Compared with other methods, DTACD shows a better performance.

In this paper, we propose a new noise-tolerant neurodynamic algorithm with fixed-time convergence to solve mixed variational inequality problems (MVIPs) and design the circuit framework for its hardware implementation. We prove that the proposed neurodynamic algorithm converges to a unique solution within fixed-time under some conditions and give its convergence time upper bound, which is independent of the initial states. Meanwhile, the robustness of the neurodynamic algorithm under additive perturbations is also demonstrated. In addition, the proposed neurodynamic algorithm is implemented using numerical simulation, analog circuits, and field-programmable gate array (FPGA) respectively. Finally, the superiority of the proposed algorithm is verified by two applications of image reconstruction and elastic net logistic regression.

This paper addresses the calibration-free robotic eye-hand coordination in a way other than the conventional image Jacobian matrix approach that has been studied extensively in literature. A nonlinear mapping rather than the linear mapping used in the image Jacobian matrix between the image space and the robotic control space is proposed. This mapping is regarded as the system's unmodeled dynamics expressed in system state equations. An extended state observer is designed first to estimate the unmodeled dynamics as well as the external disturbance of the system. With the estimation results as the compensation, a system controller is designed based on the nonlinear state-error feedback control strategy. Convergence of the extended state observer as well as the overall controller for a typical eye-hand coordination system is proved. Compared with the conventional calibration-free robotic eye-hand coordination with a Jacobian matrix, the proposed controller is independent of specific tasks and system configurations. Thus, a general design procedure is proposed for the calibration-free robotic eye-hand coordination. Simulation and experiment results demonstrate the satisfactory performance and effectiveness of the proposed approach.

This paper proposes a standard method to approach the uncalibrated robotic hand-eye coordination problem that is system configuration- and task-independent. The unknown hand-eye relationship is first modeled as the modeling errors of a dynamic system. An extended state observer is then implemented to estimate summation of the system's modeling error and the system's external disturbances. With the estimation results as the compensation, the system control is accomplished from a nonlinear combination of the system state errors. A universal framework of controller design is provided for decoupled and coupled hand-eye systems of different configurations to execute dynamic tracking task.

This paper studies a novel method to deal with the unknown image Jacobian matrix model for the uncalibrated robotic hand-eye coordination. An extended state observer is designed for online estimation of the image Jacobian matrix that is regarded as the system's unmodeled dynamics. A nonlinear feedback control law is then adopted for the coordination controller. With this scheme, a universal calibration-free controller is developed, which is independent from specific tasks and system configuration, for uncalibrated hand-eye coordination. Simulations and experiments of 3-D robotic visual positioning and tracking tasks show the validity and feasibility of the proposed control scheme.