In various practical applications, collaborative inspection systems in which multiple agents work together to accomplish inspection tasks are becoming increasingly important for enhancing operational efficiency. This paper addresses the routing problem in multi-agent collaborative inspection systems, where certain inspection points require the simultaneous presence of multiple agents to perform inspection operations. A novel approach using max-plus algebra is presented to model the collaborative inspection process and it provides a foundation for research and applications in system control and scheduling optimization. The max-plus linear (MPL) model is then converted into a Mixed Integer Linear Programming (MILP) formulation to tackle the routing problem with complex constraints inherent in the collaborative scene. Experimental results validate that the proposed MPL model and MILP approach reduce inspection completion time and waiting time at collaborative points.

This article examines for the first time an integrated structure of smart injection molding systems (IMS) based on Industry 4.0 technologies and provides a system-level solution for manufacturing smart products. The fully automated smart IMS structure allows manufacturers to produce thermoplastic products directly from raw materials without requiring any human labor. Following this, we focus on the control problem associated with the auxiliary robot manipulators that support the smart IMS. A cooperative distributed predictive control (DPC) algorithm with one-tap memory is proposed to achieve optimal closed-loop performance for multiple robot manipulators simultaneously performing their respective tasks. Using one-tap memory in smart IMS, we optimize the local performance index, which includes penalized terms diverging from it, and the global cooperation effort with limited memory acceleration to reach manifold consensus, without requiring manipulators to exchange information iteratively at each step. The cooperative DPC algorithm is also applied to five robot manipulators in the smart IMS, which require them to work cooperatively to achieve rhythmic and synchronized movements. Industrial experiment results demonstrate the feasibility of smart IMS combined with the cooperative DPC. Moreover, the cooperative DPC method outperforms the other two predictive control methods based on three metrics of the smart IMS.

Model-based control plays an important role in batch process control. With more data collected online, real-time model updates using Gaussian process regression (GPR) are becoming increasingly practical for improving the performance of model-based control strategies. However, in batch processes, process configurations are often adjusted, which can be costly if a new model has to be identified from scratch each time. Although several GPR migration methods exist, they are primarily designed for static models and are not well-suited for dynamic system modeling for control purposes. Therefore, we propose a selective migration-based online GPR identification method that enables the dynamic model for batch process control to learn selectively from the previously identified old process model as needed. In our method, we present selective migration strategies for two types of GPR model parameters: 1) hyperparameters and 2) data points. Additionally, we provide a complete algorithm for online dynamic model identification. Beyond that, for data point parameters selective migration, we propose a fast migration dataset-seeking method for a smaller computational cost and a mixed integer programming migration dataset-seeking method for a smaller prediction loss. Theoretical analysis of the framework reveals the initial improvement and final convergence. Finally, we provide two illustrative numerical examples to show the effectiveness of the proposed methods.

Batch processes are typically nonlinear systems with constraints. Model predictive control (MPC) and iterative learning control (ILC) are effective methods for controlling batch processes. By combining batch-wise ILC and time-wise MPC, this article proposes a multirate control scheme for constrained nonlinear systems. Two-dimensional (2-D) framework is used to combine historical batch data with current measurements. The ILC part uses run-to-run control with previous iteration data, and the MPC part uses real-time control with current sampled measurements. Real-time feedback-based MPC in the time axis and run-to-run ILC in the batch axis are combined to optimize the current inputs based on previous batch input–output data and real-time system measurements. Rather than achieving control objectives in a single batch, our design allows multiple batches to be executed successively. To establish the stability of the combined scheme, rigorous theoretical analysis is presented next. The combined scheme with improved performance is then validated through two illustrative numerical examples.

In this article, we study the optimal iterative learning control (ILC) for constrained systems with bounded uncertainties via a novel conic input mapping (CIM) design methodology. Due to the limited understanding of the process of interest, modeling uncertainties are generally inevitable, significantly reducing the convergence rate of the control systems. However, huge amounts of measured process data interacting with model uncertainties can easily be collected. Incorporating these data into the optimal controller design could unlock new opportunities to reduce the error of the current trail optimization. Based on several existing optimal ILC methods, we incorporate the online process data into the optimal and robust optimal ILC design, respectively. Our methodology, called CIM, utilizes the process data for the first time by applying the convex cone theory and maps the data into the design of control inputs. CIM-based optimal ILC and robust optimal ILC methods are developed for uncertain systems to achieve better control performance and a faster convergence rate. Next, rigorous theoretical analyses for the two methods have been presented, respectively. Finally, two illustrative numerical examples are provided to validate our methods with improved performance.

In this article, we develop data-driven optimal synchronization control architectures for leader-follower multiagent systems with additive disturbances and unknown system matrices. To minimize output synchronization error, algebraic Riccati equations (AREs) are derived, and unique feedback gains are determined by policy iteration. On that basis, two data-driven optimal synchronization control algorithms are developed without relying on the dynamics of the system, which guarantee output synchronization while minimizing synchronization errors and rejecting disturbances. The first algorithm uses the output synchronization error data to perform online data-driven learning (DDL), while the second algorithm uses the input data to perform DDL, where both data sample requirements are transformed into rank conditions. We have presented rigorous theoretical analyses of our proposed algorithms, which demonstrate that if an initial control protocol can make the system achieve output synchronization under mild conditions, our proposed two algorithms can take advantage of the data from reaching synchronization to optimize the closed-loop performance. Finally, a numerical example is provided to emphasize the effectiveness of our methods.

In batch processes, the ability to learn from previous process data results in high-value and batch-improved products. For batch processes with constraints and state-dependent uncertainty, this article presents a conic iterative learning control (ILC) approach, which uses cone theory to incorporate historical process data into optimization-based ILC design. The proposed conic ILC approach uses rank conditioning to select distinct data samples and conic mapping to map the data to the to-be-optimized control input variables, since adding all historical process data would be computationally intensive. Our method yields a tradeoff between learning ability from historical experience and computational efficiency from solving the optimization problem. Provable constraint satisfaction and robust stability are considered separately. To demonstrate the proven properties and effectiveness of the approach, we present a case study of the injection molding process.

In this article, we develop a learning-based secure control framework for cyber-physical systems in the presence of sensor and actuator attacks. Specifically, we use a bank of observer-based estimators to detect the attacks while introducing a threat-detection level function. Under nominal conditions, the system operates with a nominal-feedback controller with the developed attack monitoring process checking the reliance of the measurements. If there exists an attacker injecting attack signals to a subset of the sensors and/or actuators, then the attack mitigation process is triggered and a two-player, zero-sum differential game is formulated with the defender being the minimizer and the attacker being the maximizer. Next, we solve the underlying joint state estimation and attack mitigation problem and learn the secure control policy using a reinforcement-learning-based algorithm. Finally, two illustrative numerical examples are provided to show the efficacy of the proposed framework.