Search

The affine formation maneuver (AFM) control problem using the zero-sum differential game is studied for a second-order multi-agent system (MAS) against cyber-injection attacks on the actuators. First and foremost, the distributed control signals and the cyber-injected attack signals are viewed as two competing teams. Then, the leader-follower tracking error system is established where the followers aim to track the AFMs of the leaders. The leader-follower distributed optimal control policies and the cyber-attack attenuation policies are derived from the Nash equilibrium solution of the Hamilton-Jacobi-Isaac (HJI) equation. The solution of the HJI equation is estimated with the aid of the critic neural network. The weights of the critic system are learned online based on the adaptive dynamic programming (ADP) scheme. The uniformly ultimately bounded stability of the closed-loop system under the formulated zero-sum differential game control strategy has been proven by the Lyapunov stability theorem. A simulation example illustrates that the presented control method can attenuate cyber-injection attacks on the actuators and maintain the AFMs of the agents. [ABSTRACT FROM AUTHOR]

In this paper, an optimisation problem for the monotone mean-variance (MMV) criterion is considered from the perspective of the insurance company. The MMV criterion is an amended version of the classical mean-variance (MV) criterion which guarantees the monotonicity of the utility function. With this criterion we study the optimal investment and reinsurance problem which is formulated as a zero-sum game between the insurance company and an imaginary player. We apply the dynamic programming principle to obtain the corresponding Hamilton–Jacobi–Bellman–Isaacs (HJBI) equation. As the main conclusion of this paper, by solving the HJBI equation explicitly, the closed forms of the optimal strategy and the value function are obtained. Moreover, the MMV efficient frontier is also provided. At the end of the paper, a numerical example is presented. [ABSTRACT FROM AUTHOR]

This paper designs a novel adaptive robust optimal sliding mode control (SMC) strategy for constrained input missile system with uncertain input and unknown disturbance. First, a smooth function is employed to map the constrained input. Based on SMC theory, a fast adaptive SMC scheme is designed to reduce the effects of unknown input and approximate error which has two advantages. One is that the proposed scheme is independent of the uncertain information. The other is that the proposed scheme forces the states to converge with exponential rate as they are far away from the sliding mode surface, and the states move with linear rate when they are closed to the sliding mode surface. Then, a zero-sum differential games strategy-based adaptive dynamic programming (ADP) technique is employed to obtain the optimal performance of sliding mode dynamics system. The loop-system and estimation weight error can be proved stable by utilising Lyapunov theory. Finally, the missile autopilot system is used to indicate the effectiveness of the designed approach. [ABSTRACT FROM AUTHOR]