This article proposes a game-based distributed decision optimization method for heterogeneous multiagent systems with unknown nonlinear dynamics. Due to the information exchange between agents in the network, the unknown nonlinear dynamics lead to the degradation of local and all-agent control performance, which causes the strategies of all agents to deviate from the Nash equilibrium under a given goal. To address this problem, an adaptive distributed algorithm is designed to seek Nash equilibrium by combining two optimization levels. Specifically, the decision layer uses a distributed consensus algorithm to achieve benefit evaluation and a gradient algorithm to generate reference signals. Then, the control layer uses the virtual reference signal from the decision layer and the neural network estimation information to design an adaptive control algorithm. The proposed method performs real-time adaptive optimization of the strategies and control performance of the decision and control layers, ensuring the successful implementation of the distributed Nash equilibrium search. The convergence of the proposed algorithm is proved in the Lyapunov sense. Finally, simulation examples demonstrate the performance and effectiveness of the proposed method.

This article proposes a projection-aided robust distributed monitoring and control optimization approach for interconnected systems with disturbances. The disturbances and state coupling between subsystems are a challenge in achieving accurate distributed process monitoring using data-driven techniques. To address the problems, a distributed adaptive residual generator uses the average consensus algorithm to perform data fusion on the subsystem residual generator to implement disturbance decoupling process monitoring. The key to implementing this process is to use input and output data disturbance in the perturbed orthogonal complementary space to drive the adaptive residual generator. Then, using the projection technique, the residual signal in the disturbance space drives the distributed learning of plug-and-play (PnP) controller parameters. The average consensus algorithm ensures that the subsystem PnP controller parameter gradient consistency converges to the centralized design. The feasibility and effectiveness of the proposed approach are verified and demonstrated through a simulation.

This article focuses on utilizing process data to detect faults in nonlinear systems. To accomplish this, stable image/kernel representation is learned for nonlinear systems using deep neural networks, which serve as the basis for residual generators and fault detection. First, the closed-loop image representation of nonlinear systems is identified using gate recurrent units and fully connected neural networks. The involved network topology is designed to learn the nonlinear mapping in the form of linear time-varying state space, allowing the extension of existing linear methods to nonlinear systems. Then, with the identified image representation, the data-driven realization of kernel representation is derived. Finally, the residual generator is developed utilizing the system's kernel representation to enable precise fault detection in nonlinear systems. The effectiveness of our study is demonstrated through a numerical benchmark study and an actual experiment on a real Mecanum-wheeled vehicle platform.

This article proposes a distributed performance recovery method for multiagent systems with actuator faults in noncooperative games. The local agent (player) can only obtain the policy information of neighboring agents through the communication network. The strategies of nonneighbors in the cost function are unknown, and a leader–follower consensus algorithm is introduced to estimate nonneighbors' strategy. When the actuator faults occur in any agents and lead to performance degradation, i.e., the agents' strategy is biased from its optimal strategy. A distributed optimization control method is proposed to recover performance without changing the original control scheme. An observer-based residual feedback plug-and-play optimization method is used to ensure that the strategies of all agents can still converge to the optimal strategy (or close to the optimal strategy). Numerical case studies are applied to demonstrate the performance and effectiveness of the proposed method.

The intelligent manufacturing system is a complex, large-scale, interconnected system composed of many intelligent agents, and there may be physical or information space couplings between the agents. A distributed monitoring system and optimization control method are proposed to ensure the system completes its tasks safely and efficiently. The distributed monitoring system based on the average consensus algorithm is equivalent to the centralized design method, in which the submonitoring system only requires local and neighbor subsystem information. The advantage of this design is that it uses local and interactive information to achieve global diagnosis. In addition, sending data from all subsystems to a central computing node is challenging to implement in large-scale manufacturing systems. Based on the centralized plug-and-play (PnP) optimization control method, an average consensus algorithm distributed manufacturing system PnP optimization control method is proposed. Its advantage is that it uses local information and interactive information to achieve global control optimization. On this basis, an integrated architecture for distributed fault detection and optimization control is developed. The simulation results verify the feasibility and effectiveness of proposed method.

Due to limitations in large-area communication and computation, it can be challenging to apply centralized diagnosis and optimization control design approaches to cascaded systems. This work proposes a distributed diagnosis and optimization control approach, which is realized using data-driven techniques. Specifically, an adaptive observer-based subdiagnosis system design approach is proposed for cascaded systems using only the local input/output (I/O) data and the state estimations of adjacent subsystems. The state estimations from neighboring subsystems are treated as known inputs in the local subsystem. In the centralized design approach, the residual signals generated by all subsystem observers need to be sent to the central computing node to reconstruct controller parameters. The learning process of the local optimization controller only needs to be driven by the residual signals from local and adjacent subsystems, avoiding centralized calculation and reducing the computational burden of the central node. The learning process of the locally optimal controller only needs to be driven by residual signals from the local and neighboring subsystems. In the end, the simulation results verify the effectiveness of the proposed distributed approach.

Coverage control describes the optimal deployment problem of multi-agent system with communication sensors, aiming to drive the multi-agent system reach the optimal deployment location. To accomplish coverage tasks, agents are required to communicate with each other. Thus, the connectivity of multi-agent system is a fundamental requirement in case of agent disconnection and disappearance. In this paper, we propose a distributed coverage control algorithm with connectivity optimization. According to the designed cost function, the controller is divided into two parts: coverage controller and connectivity optimization controller, and the method of gradient descent is used to minimize the cost function to get the controller. Finally, the simulation serves to show the effectiveness of the algorithm.

A new fault-tolerant controller is designed in this paper, which ensures the event-triggered leader-following consensus of multiple robotic manipulators subject to switching topologies. A Markov distributed sequence with a known probability transition matrix is employed to describe the switching communication topologies of multiple robotic manipulators. The investigated actuator fault model contains both constant and time-varying faults. Also, the norm-bounded conditions are utilized to denote the manipulators nonlinearities and additive noises. Furthermore, a set of output-based control gains are explicitly characterized via ensuring mean-square stability on dynamics of consensus errors. Finally, simulation results illustrate the designed consensus protocol can compensate actuator failure effectively.

For manufacturing industry, scheduling problem is a very important problem. Good scheduling scheme can greatly improve the production efficiency of enterprises. The flexible job shop scheduling problem (FJSP) not only needs to arrange the processing sequence for the operations of each workpiece, but also needs to consider how to allocate machines to the operations to improve the processing efficiency. Dynamic flexible job shop scheduling problem (DFJSP) is based on FJSP, which studies how to dynamically reschedule enterprise production according to the actual situation when disturbance events occur, so as to minimize the impact of emergencies on production. For DFJSP under machine fault, this strategy can dynamically schedule in time for different states before and after the machine fault is repaired, maximize the use of workshop resources, and reduce the impact of machine fault on production. A scheduling scheme combining predictive scheduling and real-time scheduling is proposed for DFJSP under machine fault. Then the standard test cases are used to verify the scheduling scheme from multi-objective with NSGA-II. The experimental results show that the scheduling scheme is feasible.

The problem of fault diagnosis (FD) and fault tolerant control for a class of Takagi–Sugeno (T–S) fuzzy stochastic distribution control systems subject to sensor and actuator faults is discussed in this article. First, fuzzy logic models are used to approximate the output probability density function (PDF). Next, an adaptive augmented state/FD observer is proposed to estimate the system state, sensor and the actuator faults simultaneously. New expected weights based on the sensor fault estimation information and a PI-type fuzzy feedback fault tolerant controller are designed to compensate the effect of sensor fault and actuator fault simultaneously. When the sensor fault occurs, the expected objective is redesigned to compensate the sensor fault. Meanwhile, the PI controller can compensate the effect of actuator fault, and the output PDF of the system can still track the desired PDF after the fault occurs. Finally, an example of quality distribution control in chemical reaction process is given to confirm the effectiveness of the algorithm.