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The current work concerns on cooperative control of exponential stabilization and control performance improvement in the spatial domain for a linear spatiotemporal dynamic system associated with multiple control actuators and multiple collaborative measurement sensors. By assuming that the system dynamics is modeled by a MIMO parabolic partial differential equation (PPDE) and each sensor can share measurement information with its topological neighbors in directed and switching topological networks, a cooperative control protocol is proposed to achieve the control aim of this article. With the help of a combination of multiagent consensus theory, Lyapunov’s method, and integral inequality technique, sufficient conditions are presented for the closed-loop exponential stability of the PPDE in the norm $|\cdot |_{2}$ . Moreover, some performance indexes are defined to evaluate the closed-loop control performance improvement in the spatial domain. Extensive simulation results are finally presented for a simple numerical example and a practical heat treatment process to verify the effectiveness and performance improvement capability of the proposed cooperative control protocol.

Industries, such as manufacturing, are accelerating their embrace of the metaverse to achieve higher productivity, especially in complex industrial scheduling. In view of the growing parking challenges in large cities, high-density vehicle spatial scheduling is one of the potential solutions. Stack-based parking lots utilize parking robots to densely park vehicles in the vertical stacks like container stacking, which greatly reduces the aisle area in the parking lot, but requires complex scheduling algorithms to park and take out the vehicles. The existing high-density parking (HDP) scheduling algorithms are mainly heuristic methods, which only contain simple logic and are difficult to utilize information effectively. We propose a hybrid residual multiexpert (HIRE) reinforcement learning (RL) approach, a method for interactive learning in the digital industrial metaverse, which efficiently solves the HDP batch space scheduling problem. In our proposed framework, each heuristic scheduling method is considered as an expert. The neural network trained by RL assigns the expert strategy according to the current parking lot state. Furthermore, to avoid being limited by heuristic expert performance, the proposed hierarchical network framework also sets up a residual output channel. Experiments show that our proposed algorithm outperforms various advanced heuristic methods and the end-to-end RL method in the number of vehicle maneuvers, and has good robustness to the parking lot size and the estimation accuracy of vehicle exit time. We believe that the proposed HIRE RL method can be effectively and conveniently applied to practical application scenarios, which can be regarded as a key step for RL to enter the practical application stage of the industrial metaverse.

This article investigates the event-triggered switching control (ETSC) of switched nonlinear time-delay systems (SNTDSs) with asynchronous switching. First, we study the input-to-state stability (ISS) and integral ISS (iISS) for SNTDSs with asynchronous switching, where switching instants are generated based on the designed event-triggered mechanism. Among existing works on the ETSC, systems behavior at event-triggered instants is neglected. In fact, whenever an event is triggered, the systems mode will jump suddenly such that a switch is imposed to the systems, leading to the change of subsystems. Moreover, asynchronous switching behavior may occur between the actual subsystem and its corresponding controller. These facts bring great challenges for the event-triggered mechanism design and ISS analysis. To tackle these problems, a new Lyapunov-based event-triggered mechanism with adjustable parameters is designed to establish the relationship between systems switches and event triggers, and exclude the Zeno phenomenon. The analysis of the asynchronous switching behavior can be implemented and some ISS and iISS criteria of SNTDSs are derived utilizing the merging switching technique. Finally, two numerical examples, including a practical stirred tank reactor system, are presented to show the validity of the proposed methods.

This article proposes adaptive internal model controls for the collocated output regulation of a flexible wing, where distributed disturbances, boundary disturbances, and references are from an exactly unknown exosystem. Observer-based tracking error feedback controls are first designed to address the robust output regulation in case of a known exosystem matrix. If the exosystem has an unknown matrix, an adaptive observer is further proposed with the observer error system converging to zero exponentially. Then, we can obtain adaptive observer-based controls by combining adaptive observers and observer-based controls, which are able to regulate the tracking errors toward zero in case of the exactly unknown disturbances and references. The corresponding closed-loop system is proved to be internally asymptotically stable. A simulation example is further provided for adaptive internal model control of the wing system.

This article studies the adaptive output-feedback consensus control problem of nonlinear multiagent systems (MASs) against denial-of-service (DoS) attacks. The attacks on the edges instead of nodes are considered, where we allow different attack intensities but at least one edge is connected in each attacking interval. Affected by output disturbance, the sensor feedback signal of every agent is inaccurate, which will reduce the approximation accuracy of the observer. Then, we design a signal to revise the sensor feedback signal subject to disturbance. Meanwhile, a prescribed performance function is used to ensure the transient and steady-state performance of error. Leveraging the Lyapunov stability theory and the backstepping technique, a distributed output-feedback control scheme subject to asymmetric saturation nonlinearity is designed. For the asymmetric input saturation, an auxiliary signal is designed to simplify the designed progress of controller input. To deal with the inherent problem of “explosion of complexity” emerging with backstepping, dynamic surface control is utilized. It is proved that the consensus errors converge to small neighborhoods of the origin, and all signals within the closed-loop system are bounded. Finally, simulation results are offered to demonstrate the effectiveness of the proposed method.