This paper explores the computation enhancement problem in an intelligent reflecting surface (IRS) enabled wireless powered mobile edge computing system serving the intelligent transportation relying on powerful computational support. Initially, in the downlink, the base station (BS) with edge server transmits energy signals to the battery-powered roadside units (RSUs) with computing capabilities, which are grouped into distinct clusters. Subsequently, RSUs leverage the harvested energy for local computing and offloading their tasks to the BS by using a hybrid rate splitting multiple access (RSMA) and time division multiple access (TDMA) strategy. Finally, the computed outcomes from the edge server are transmitted for integration with the computations carried out locally at the RSUs. The objective is to maximize the minimal computation rate of RSU clusters, where the transmit power of the BS and RSUs, the downlink and uplink beamforming of the IRS, the CPU frequency of the RSUs, and the time slot assignment are jointly optimized. To address the bottleneck issues constraining computation rate, this paper proposes an iterative algorithm that combines sequential rank-one constraint relaxation and block coordinate descent methods. Ultimately, the simulation results confirm the effectiveness of the proposed algorithm in enhancing the weakest link constraining the system computation rate when contrasted with the baseline algorithms.

This paper addresses the fault-tolerantH∞output regulation problem of multi-agent systems (MASs) subject to input saturation, structural uncertainties, and actuator faults. TheH∞output regulation problem is reformulated as a distributed two-step zero-sum game problem to enhance both steady-state and transient performance in the presence of disturbances. A novel anti-saturation reinforcement learning algorithm with a feedforward-feedback structure is proposed, enabling saturation-free optimal output regulation while effectively mitigating both modeled and unmodeled disturbances. An active fault-tolerant control (FTC) approach based on the anti-saturation policy algorithm is also introduced to compensate for actuator faults and structural uncertainty. The salient feature of the proposed algorithm is its ability to prevent saturation during optimal control policy learning, while improving both steady-state and transient performance. Finally, simulation studies are conducted to validate the effectiveness of the proposed approach.

The topside separation system is an important device installed on offshore oil exploration platforms for the treatment of produced water. Due to its operation in high-moisture and salt-infested environments, the system is susceptible to valve malfunctions. Additionally, the presence of strong couplings and slugging disturbances in the system further complicate the development of fault-tolerant control (FTC). To achieve this, this article investigates the fault-tolerant$ H_{\infty } $control problem in the topside separation system. To recover control performance against actuator faults while reducing disturbance sensitivity, the fault-tolerant$ H_{\infty } $control problem is formulated for the topside separation system and is expressed as a two-player differential game problem. A Nash equilibrium solution to the fault-tolerant$ H_{\infty } $control problem is derived by solving the game algebraic Riccati equation (GARE). Considering the tailor-made property and difficulty in full-state sensing in industry, an output feedback reinforcement learning (RL) algorithm is proposed to implement the fault-tolerant$ H_{\infty } $control method without the need for system dynamics. Simulation studies are performed to verify the effectiveness of the proposed algorithm.

Fast and smooth driving is a preferable consideration in platooning algorithm development for intelligent autonomous vehicular systems. It can improve traffic efficiency while guaranteeing passenger comfort. To this end, this work investigates the fast distributed platooning problem of connected vehicular systems. Taking the inaccurate velocity measurement into consideration, an extended state observer (ESO) based on a fractional order faster nonsingular terminal sliding mode (FNTSM) is proposed for velocity and disturbance estimation simultaneously. An FNTSM control algorithm based on the double power reaching law is developed to reach fast platooning while guaranteeing string stability of the connected vehicular systems regardless of zero or nonzero initial spacing error conditions. The salient features of the proposed platoon controller are that system convergence can be achieved in finite time and the time-varying external disturbances can be estimated accurately. Finally, simulation and experiment studies are conducted to demonstrate the effectiveness and efficiency of the proposed control algorithm.

The output regulation approach has been extensively applied for the cooperative control of heterogeneous multiagent systems (MASs) with known dynamics, but rarely for MASs subject to stochastic and unknown dynamics. One challenging problem is that it is still unclear how to construct the regulator equations of stochastic MASs with unknown dynamics for dynamic exosystem compensation. Toward this end, this article develops a novel distributed control scheme for the approximate output regulation of discrete-time stochastic MASs subject to heterogeneous and unknown nonlinear dynamics. A distributed observer is designed for the exosystem-state estimation of agents, based upon which nonlinear regulator equations are constructed for the feedforward control design, thereby achieving distributed exosystem compensation. A high-order neural network is deployed to approximately solve the nonlinear regulator equations with unknown nonlinearity, and an adaptive control law is developed for the approximate output regulation of discrete-time stochastic MASs. Stability analysis shows that the closed-loop system is semiglobally uniformly ultimately bounded. Simulation results demonstrate the effectiveness and efficiency of the proposed control law.

This paper investigates the output consensus problem of heterogeneous stochastic nonlinear multiagent systems with directed communication topologies, with a view of making the outputs of a group of follower agents track the output of a leader. Fuzzy logic systems are applied to approximate the unknown nonlinear functions of agents. A special case that all followers can get access to the leader is first considered, and a novel decentralized adaptive fuzzy control law based on the output regulation framework is presented. Next, the proposed control scheme is further applied to design the distributed adaptive fuzzy control law for a more general case that only part of agents can get access to the leader. By applying Lyapunov stability analysis, it is shown that the outputs of followers will achieve consensus to a sufficient small bound of the output of the leader under the proposed control law. Finally, simulation results demonstrate that the proposed control law is effective and efficient. The developed distributed control scheme can be widely applied to solve the cooperative control problem of practical autonomous systems with uncertain dynamics such as synchronization of mechanical systems with vibration, formation control of autonomous underwater vehicles, etc.

In this paper the formation control problem of heterogeneous multi-agent systems is investigated. The formation control problem is first transformed to an output regulation problem, and then a distributed adaptive control law based on state feedback is designed to solve the problem. The salient feature of the developed control law is that the feedback gains are independent of the Laplacian matrix of the underlying system topology, which is of global nature. Furthermore, it is shown that all agents can form a formation and keep a desired relative position to a leader under a necessary and sufficient condition, and all feedback gains will approach some constant as time goes to infinity. An example demonstrates that the proposed control law is highly effective and efficient.

In this paper, the output containment control problem of heterogeneous linear multi-agent systems is investigated. The objective of the output containment control problem is to make a group of agents converge to a convex hull spanned by some leaders. A distributed control law based on output regulation framework is proposed, where a distributed observer is designed in the control law for agents estimating the leaders' states. The salient feature of the proposed control law is that convergence trajectories of the distributed observers can only be determined by system communication topology and the initial states of leaders, which is helpful to obtain local necessary and sufficient condition for the solvability of the output containment control problem. Simulation studies demonstrate that the proposed control law is effective and efficient.

This paper investigates the output consensus problem of heterogeneous discrete-time multiagent systems with individual agents subject to structural uncertainties and different disturbances. A novel distributed control law based on internal reference models is first presented for output consensus of heterogeneous discrete-time multiagent systems without structural uncertainties, where internal reference models embedded in controllers are designed with the objective of reducing communication costs. Then based on the distributed internal reference models and the well-known internal model principle, a distributed control law is further presented for output consensus of heterogeneous discrete-time multiagent systems with structural uncertainties. It is shown in both cases that the consensus trajectory of the internal reference models determines the output trajectories of agents. Finally, numerical simulation results are provided to illustrate the effectiveness of the proposed control schemes.