Observer-Based Consensus Control for MASs With Prescribed Constraints via Reinforcement Learning Algorithm

Reinforcement-Learning-Based Fixed-Time Prescribed Performance Consensus Control for Stochastic Nonlinear MASs with Sensor Faults

This paper proposes the fixed-time prescribed performance optimal consensus control method for stochastic nonlinear multi-agent systems with sensor faults. The consensus error converges to the prescribed performance bounds in fixed-time by an improved performance function and coordinate transformation. Due to the unknown faults in sensors, the system states cannot be gained correctly; therefore, an adaptive compensation strategy is constructed based on the approximation capabilities of neural networks to solve the negative impact of sensor failures. The reinforcement-learning-based backstepping method is proposed to realize the optimal control of the system. Utilizing Lyapunov stability theory, it is shown that the designed controller enables the consensus error to converge to the prescribed performance bounds in fixed time and that all signals in the closed-loop system are bounded in probability. Finally, the simulation results prove the effectiveness of the proposed method.

Consensus-Based Power System State Estimation Algorithm Under Collaborative Attack

Due to its vulnerability to a variety of cyber attacks, research on cyber security for power systems has become especially crucial. In order to maintain the safe and stable operation of power systems, it is worthwhile to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker's perspective. The consensus-based distributed state estimation problem is investigated for power systems subject to collaborative attacks. In order to describe such attack behaviors, the denial of service (DoS) attack model for hybrid remote terminal unit (RTU) and phasor measurement unit (PMU) measurements, and the false data injection (FDI) attack model for neighboring estimation information, are constructed. By integrating these two types of attack models, a different consensus-based distributed estimator is designed to accurately estimate the state of the power system under collaborative attacks. Then, through Lyapunov stability analysis theory, a sufficient condition is provided to ensure that the proposed distributed estimator is stable, and a suitable consensus gain matrix is devised. Finally, to confirm the viability and efficacy of the suggested algorithm, a simulation experiment on an IEEE benchmark 14-bus power system is carried out.

Reinforcement learning-based consensus control for MASs with intermittent constraints

In this article, an adaptive optimal consensus control problem is studied for multiagent systems in the strict-feedback structure with intermittent constraints (the constraints appear intermittently). More specifically, by designing a novel switch-like function and an improved coordinate transformation, the constrained states are converted into unconstrained states, and the problem of intermittent constraints is resolved without requiring "feasibility conditions". In addition, using the composite learning algorithm and neural networks to construct the identifier, a simplified identifier-actor-critic-based reinforcement learning strategy is proposed to obtain the approximate optimal controller under the framework of backstepping. Meanwhile, with the aid of the nonlinear dynamic surface control technique, the issue of "explosion of complexity" in backstepping is removed, and the requirements for filter parameters are loosened. Based on Lyapunov stability theory, it is demonstrated that all signals in the closed-loop system are bounded. Finally, two simulation examples are used to verify the effectiveness of the proposed method.