In this paper, the multiagent pursuit-evasion (MPE) games are solved in order to obtain optimal strategic policies for all players. In these games, multiple pursuers attempt to intercept multiple evaders that try to avoid capture. A graph-theoretic approach is employed to study the interactions of the agents with limited sensing capabilities, such that distributed control policies are obtained for every agent. Furthermore, the minimization of performance indices associated with the goals of the agents is guaranteed. Nash equilibrium among the players is obtained by means of optimal policies that use the solutions of the Hamilton-Jacobi-Isaacs (HJI) equations of the game. Minmax strategies are also proposed to guarantee a security-level performance when the solutions of the HJI equations for Nash equilibrium do not exist. Scenarios for finite-time capture and for asymptotic rendezvous are analyzed, and emergent behaviors are obtained by means of modifications of the proposed general-case performance indices. The containment control results are shown to be special cases of the solutions of the MPE games.

An adaptive iterative learning reliable control (AILRC) strategy is developed in this study for a class of non‐linearly parameterised systems subject to unknown time‐varying state delays and input saturation as well as actuator faults. In regard to non‐linearly parameterised uncertainties, not only the non‐linearly parameterised controlled object, but also the non‐linearly parameterised input distribution matrix is investigated in this technical note. Without the need for precise system parameters or analytically estimating bound on actuator faults variables, the novel data‐driven AILRC is constructed by a non‐linear feedback term and a robust term. The non‐linear influence brought by actuator faults, input saturation and state delays can be compensated with the resultant algorithms. It is shown that the convergence of single‐input–single‐output and multiple‐input–multiple‐output systems is proved through a new time‐weighted Lyapunov–Krasovskii‐like composite energy function. The validity of the proposed AILRC is further verified by simulation.

Low-inertia dc microgrids often rely on storage devices to buffer energy and handle abrupt load changes. An alternative approach involves the concept of power buffers, power electronics converters with bulky storage components that precede the final point-of-load converters, and decouple the grid and load dynamics. Proper adjustment of the input impedances of power buffers helps to shape the trajectory of the transient imposed on a dc microgrid. A communication network facilitates information exchange among active loads (loads augmented with power buffers). Such group information helps to collectively respond to any load change; an optimal response with the least energy extract from individual buffers. A game-theoretic performance function is defined for active loads. Then, a distributed control policy simultaneously minimizes all performance functions. A low-voltage dc microgrid, simulated in MATLAB/Simulink environment, is used to study the effectiveness of the proposed methodology.

This work investigates the model-following control problem associated with a class of non-linear systems in the presence of modelling uncertainties and actuator failures. The particular interest lies in the development of designer-friendly and cost-effective control scheme. By combining model-reference mechanism with robust adaptive radial basis function (RBF) neural network (NN), several control algorithms are derived without the need for precise system parameters or analytical-bound estimation on actuator failure variables. It is shown that the developed control algorithms are structurally simple and computationally inexpensive. Application of the proposed strategies to individual pitch control of wind turbines is also addressed. Formative stability analysis and numerical simulation on severe failure scenarios confirm the effectiveness of the proposed methods.

Data-driven intelligent transportation systems (D²ITSs) have drawn significant attention lately. This work investigates a novel multi-agent-based data-driven distributed adaptive cooperative control (MA-DD-DACC) method for multi-direction queuing strength balance with changeable cycle in urban traffic signal timing. Compared with the conventional signal control strategies, the proposed MA-DD-DACC method combined with an online parameter learning law can be applied for traffic signal control in a distributed manner by merely utilizing the collected I/O traffic queueing length data and network topology of multi-direction signal controllers at a single intersection. A Lyapunov-based stability analysis shows that the proposed approach guarantees uniform ultimate boundedness of the distributed consensus coordinated errors of queuing strength. The numerical and experimental comparison simulations are performed on a VISSIM-VB-MATLAB joint simulation platform to verify the effectiveness of the proposed approach.

This study investigates the speed trajectory tracking problem of high‐speed trains with actuator failures and unknown speed delays as well as control input saturations. New adaptive iterative learning fault‐tolerant control (AILFTC) strategy is derived without the need for precise system parameters or analytically estimating bound on actuator failures variables. It is shown that with the proposed method, both actuator failures can be accommodated and the unknown time‐varying speed delays and control input saturations can be analysed by means of Lyapunov–Krasovskii function. As such, the resultant control algorithms are able to achieve the L _{[0, T ]} ² convergence of the train speed to desired profile during operations repeatedly in the presence of non‐linearities and parametric uncertainties, as validated by the theoretical analysis and numerical simulations.