Cooperative line-of-sight guidance with optimal evasion strategy for three-body confrontation.

This paper focuses on the three-body confrontation problem of a Target-Defender team against an Attacker possessing greater maneuverability. Based on the input-to-state stability approach, a cooperative robust line-of-sight guidance law is proposed for the Target-Defender team to ensure that the Attacker has a certain tolerance range on the unavailable guidance information. In addition, the optimal evasion strategy is developed for the Target to maximize the terminal distance between the Target and Attacker. A key feature of the proposed guidance law is that the interception of the Attacker can be guaranteed as long as the uncertainty generated by the Attacker's maneuver is bounded. Furthermore, by using the optimal control technique, the control input of the Target-Defender team is minimized. Simulation examples are given to illustrate the effectiveness of the proposed guidance law.

Optimal Output Consensus of Heterogeneous Linear Multiagent Systems Over Weight-Unbalanced Directed Networks.

This article investigates the distributed optimal output consensus problem of heterogeneous linear multiagent systems over weight-unbalanced directed networks. A novel distributed continuous-time state feedback controller is proposed to steer the outputs of all the agents to converge to the optimal solution of the global cost function. Under the standard condition that the unbalanced digraph is strongly connected and the local cost functions are strongly convex with global Lipschitz gradients, the exponential convergence of the closed-loop multiagent system is established. Then, the proposed state feedback control law is extended to an observer-based output feedback setting. Two examples are finally provided to illustrate the effectiveness of the proposed control schemes.

Giant bladder stone: A case report.

Giant bladder stones are rare in younger patients. Herein, we report a case of acute renal failure in a 31-year-old male with a giant bladder stone. The patient presented with lower urinary tract symptoms and urinary retention with milky-white urine. An emergency suprapubic cystotomy was performed and an 11-cm oval, solid stone was removed. Post-operative creatinine levels progressively decreased to normal and the patient exhibited smooth urination and good recovery at follow-up. A comprehensive physical examination and thorough investigation of the patient's medical history are required during clinical evaluation, diagnosis and treatment, thus preventing misdiagnoses and ensuring prompt treatment.

Distributed Nash Equilibrium Seeking for Aggregative Games With Nonlinear Dynamics Under External Disturbances.

In this paper, we study the distributed Nash equilibrium (NE) seeking problem for a class of aggregative games with players described by uncertain perturbed nonlinear dynamics. To seek the NE, each player needs to construct a distributed algorithm based on information of its cost function and the exchanging information obtained from its neighbors. By combining the internal model principle and the average consensus technique, we propose a distributed gradient-based algorithm for the players. This paper not only assures the NE seeking of aggregative games but also achieves the disturbance rejection of external disturbances.

Distributed Optimization for a Class of Nonlinear Multiagent Systems With Disturbance Rejection.

The paper studies the distributed optimization problem for a class of nonlinear multiagent systems in the presence of external disturbances. To solve the problem, we need to achieve the optimal multiagent consensus based on local cost function information and neighboring information and meanwhile to reject local disturbance signals modeled by an exogenous system. With convex analysis and the internal model approach, we propose a distributed optimization controller for heterogeneous and nonlinear agents in the form of continuous-time minimum-phase systems with unity relative degree. We prove that the proposed design can solve the exact optimization problem with rejecting disturbances.