This paper investigates distributed optimal teleoperation control for networked mobile manipulators (NMMs) subject to model uncertainties, nonholonomic constraints, and external disturbances. To achieve cost-minimization cooperative control with prescribed transient and steady-state performance, a hierarchical optimization prescribed performance (HOPP) framework is proposed by integrating a reinforcement learning-based optimization estimator (RLOE) with a prescribed performance stability controller (PPSC). In the proposed scheme, the RLOE generates distributed reference trajectories for slave mobile manipulators through local neighbor interactions while minimizing a cooperative performance index. The PPSC is then designed to guarantee bounded tracking errors with prescribed convergence behavior for both the master and slave manipulators. Lyapunov-based analysis is provided to establish the boundedness and convergence properties of the closed-loop system. Rooted in Lyapunov stability theory, the proposed control algorithm is designed to ensure reliability and efficacy. Simulation studies on a teleoperation system of 2-DoF mobile manipulators demonstrate that the proposed method achieves accurate tracking, reduced cooperative cost, and improved transient performance.

The process industry is a continuous manufacturing system that comprises intricate physical and chemical reactions. Given the increasing constraints on resources and energy, it is urgent to optimize process indicators by maintaining an efficient reaction atmosphere. Reinforcement learning (RL), using trial and error to learn control strategies, has become a topic of interest in the control community. However, practical implementation reveals that the mapping between observed state variables and the reaction atmosphere is subject to uncertain disturbances, which seriously affect the reliability of process indicator control. To address these issues, a robust RL (RRL) control method based on knowledge-constrained adversarial perturbation is proposed. It applies the adversary to perturb the observed state to characterize the uncertain disturbance. First, the insight of composite modeling for the process industry is presented to factorize the inherent and external uncertainties. Based on this insight, a reaction atmosphere indicator surrogate model is built to quantify the inherent uncertainty. Second, by leveraging the variation boundary information of the surrogate model, a dynamic state perturbation set and its update policy are proposed to ensure the rationality of the state perturbation. Last, an external uncertain time series generation method with continuity constraints is proposed to incorporate reasonable external uncertainty in the training process. Case validation in zinc electrowinning demonstrates that the proposed method effectively enhances control performance in uncertain scenarios.

The process industry is a key manufacturing process that consumes a vast amount of energy consumption. On the premise of ensuring process stability, controlling process variables to operate the process close to the optimal working condition plays a critical role in reducing energy consumption. Reinforcement learning (RL), using trial and error to learn control strategies, has received much attention. However, the substantial fluctuations of process variables and the switching delay gap of the process industry result in a high-dimension state-action space, making it difficult to learn control strategies efficiently, and there is no guarantee of control stability. To get around these issues, first, a generic knowledge-extracted method for process industry RL control is proposed. It does not require laborious expert knowledge acquisition processes. Second, to improve learning efficiency, the implicit knowledge is extracted using decision trees from operation trajectory data and embedded into agent controllers. Third, an explicit knowledge-oriented reward constructing method is designed to guarantee control stability. A case of the zinc electrowinning process is provided to validate its superiority. The result shows that it can reduce power consumption while stabilizing process variables within the spec limits, without a laborious expert knowledge acquisition process.

Process industry indicator describes the production status and is crucial to the stable process operation. Its low sampling frequency makes it difficult to meet the indicator perception needs for real-time process control. Indicator estimation is a promising alternative to improve its obtaining frequency. However, the low sampling frequency of indicators leads to observation scarcity, discouraging shortening the estimation period. Moreover, fluctuations in working conditions (WCs) result in difficulty in reliable estimation. Therefore, a variable-period estimation method is proposed to change the estimation period reliably in the absence of observations. First, the WCs are identified by extracting semantic information from logs. Second, the network group is proposed, which achieves variable-period estimation by adjusting the number of subnetworks. Moreover, two mechanism constraints and a continuous accumulation mapping are proposed to ensure the estimation credibility. A case study of the zinc electrowinning process is provided to validate the method.

The electrowinning process is a critical operation in nonferrous hydrometallurgy and consumes large quantities of power consumption. Current efficiency is an important process index related to power consumption, and it is vital to operate the electrolyte temperature close to the optimum point to ensure high current efficiency. However, the optimal control of electrolyte temperature faces the following challenges. First, the temporal causal relationship between process variables and current efficiency makes it difficult to estimate the current efficiency accurately and set the optimal electrolyte temperature. Second, the substantial fluctuation of influencing variables of electrolyte temperature leads to difficulty in maintaining the electrolyte temperature close to the optimum point. Third, due to the complex mechanism, building a dynamic electrowinning process model is intractable. Hence, it is a problem of index optimal control in the multivariable fluctuation scenario without process modeling. To get around this issue, an integrated optimal control method based on temporal causal network and reinforcement learning (RL) is proposed. First, the working conditions are divided and the temporal causal network is used to estimate current efficiency accurately to solve the optimal electrolyte temperature under multiple working conditions. Then, an RL controller is established under each working condition, and the optimal electrolyte temperature is placed into the controller's reward function to assist in control strategy learning. An experiment case study of the zinc electrowinning process is provided to verify the effectiveness of the proposed method and to show that it can stabilize the electrolyte temperature within the optimal range without modeling.

Industrial cyber-physical systems (ICPSs) are deployed in many high-value facilities recently, and the monitoring of ICPS is more and more important. However, the prerequisite of ICPS monitoring is how to obtain an accurate network structure. In addition, the structure of ICPS may change over time and the observations data are limited and noisy. These situations make the ICPS network structure identification more difficult. In this article, we proposed the algorithm of temporal network identification from sparse data (ATNISD) to address these two issues simultaneously. First, we established the temporal network analysis model from the aspect of state equation and observation equation. Then, we analyze the characteristics of temporal networks in both time domain and space domain and propose a general framework of temporal networks structure identification, which is a combinatorial optimization problem. To improve the accuracy and alleviate the computational complexity, we decompose the combinatorial problem into small independent simple problems, which can be solved efficiently. The performance of the proposed algorithm is verified on synthetic evolutionary game dynamics on both homogeneous and heterogeneous temporal networks. The experimental results show that the proposed method can efficiently solve the problem of temporal networks structure identification from sparse data. Note to Practitioners—This article addresses the importance of network structure identification in industrial cyber-physical systems (ICPSs). The proposed method can effectively cope with the task of ICPS network structure identification in time-varying environments by exploiting the spatial and temporal features of networks in both time domain and space domain. The proposed algorithm can be implemented in typical slowing changing ICPS with or without observation noise, and it can decompose the combinatorial problem into small independent simple problems to improve the accuracy and release the computational complexity. Extensive simulation experiments demonstrate the accuracy and robustness of the proposed method for solving the structure identification task of temporal networks.

In order to simulate the flux dipping process of microbump flip chip bonding, multiparameter experiments are designed. The dipping process of microbumps is captured by the high-speed camera, so that we can obtain the quantitative relationship between the dipping quantity and its viscosity, dipping speed, dipping depth, and dipping time by virtue of image processing. Furthermore, corresponding mathematical models are established by curve fitting. Finally, all of the numerical models are integrated; numerical simulation is carried out by MATLAB-Simulink and graphical user interface; and a new simulation software is developed, thus providing an important basis and guidance for flux dipping application and parameter selection.

To develop integrated circuit (IC) test of wafer-level packaging, the electromechanical model of microprobe testing process and the IC final test system of wafer-level packaging based on microprobe arrays are first proposed. An electromechanical model of the process of microprobe testing is derived, which is based on the analysis of the collected real-time force and electrical data using designed force sensing system, voltage measuring circuit and loading system. It is found that the contact resistance is a quartic function with respect to the loading force, and the loading force has nonlinear hysteretic damping characteristics with respect to the displacement and speed of the microprobe. The real-time contact resistance is approximately an exponential function of the damping force. Finally, the effectiveness of the proposed electromechanical model on wafer-level packaging testing using our designed new microprobe arrays testing system is confirmed. It will provide models and methods for developing IC final test of wafer-level packaging.