In this paper, a novel decentralized adaptive neural network controller is proposed for a class of large-scale nonlinear systems with unknown nonlinear, nonaffine subsystems and unknown nonlinear interconnections. The stability of the closed loop system is guaranteed by introducing a robust adaptive bound based on Lyapunov stability analysis. A radial-basis function type neural network is used in the paper. To show the effectiveness of the proposed method, we performed some simulation studies. The results of simulation become very promising.

One of the biggest limitations of BP algorithm is its low rate of convergence. The Variable Learning Rate (VLR) algorithm represents one of the well-known techniques that enhance the performance of the BP. Because the VLR parameters have important influence on its performance, we use learning automata (LA) to adjust them. The proposed algorithm named Adaptive Variable Learning Rate (AVLR) algorithm dynamically tunes the VLR parameters by learning automata according to the error changes. Simulation results on some practical problems such as sinusoidal function approximation, nonlinear system identification, phoneme recognition, Persian printed letter recognition helped us better to judge the merit of the proposed AVLR method.

In this paper, the problem of control reconfiguration in the presence of actuator failure preserving the nominal controller is addressed. In the actuator failure condition, the processing algorithm of the control signal should be adapted in order to re-achieve the desired performance of the control loop. To do so, the so-called reconfiguration block, is inserted into the control loop to reallocate nominal control signals among the remaining healthy actuators. This block can be either a constant mapping or a dynamical system. In both cases, it should be designed so that the states or output of the system are fully recovered. All these situations are completely analysed in this paper using a novel structural approach leading to some theorems which are supported in each section by appropriate simulations.

This paper presents a novel approach for robust impulsive synchronization of uncertain complex dynamical networks, each node of which possesses chaotic dynamics with different parameters perturbation and external disturbances as well as unknown but bounded network coupling effects. A new sufficient condition is proposed that guarantees the global robust synchronizing of such a network. Finally, the effectiveness of the proposed approach is evaluated by performing simulations on two illustrative examples.

In this paper, we study the decentralized coverage control problem for an environment using a group of autonomous mobile robots with nonholonomic kinematic and dynamic constraints. In comparison with standard coverage control procedures, we develop a combined controller for Voronoi-based coverage approach in which kinematic and dynamic constraints of the actual mobile sensing robots are incorporated into the controller design. Furthermore, a collision avoidance component is added in the kinematic controller in order to guarantee a collision free coverage of the area. The convergence of the network to the optimal sensing configuration is proven with a Lyapunov-type analysis. Numerical simulations are provided approving the effectiveness of the proposed method through several experimental scenarios.

Careful inspection of efficiency in a DC-DC converter and its dependence on different parameters have been key concerns for power electronic specialists for a long time ago. Although extensive research has been done on the estimation of power loss for different components in a DC-DC converter separately, there isn't any comprehensive study regarding power loss analysis in a DC-DC converter. In this research, detailed description and necessary considerations in order to analyze the power loss of all components in a Push-Pull DC-DC converter are presented. Push-Pull topology is the best choice for investigating efficiency issues, since it exhibits all different types of power loss that are usually encountered in DC-DC converters. This research proposes and verifies appropriate power loss models for all components in a DC-DC converter that dissipate power. For this purpose, conduction and switching loss models of all the relevant components are fully developed. The related equations are implemented in MATLAB environment to simulate all possible causes of power loss in the converter. In order to provide a test bed for evaluation of the proposed loss models and the converter efficiency, a 50 W Push-Pull DC-DC converter was designed and implemented. The experimental results are in full accordance with the simulation results in different input voltages, load conditions and switching frequencies. It was finally shown that the proposed models accurately estimate the DC-DC converter's efficiency.

Model-based reinforcement learning uses the gathered information, during each experience, more efficiently than model-free reinforcement learning. This is especially interesting in multiagent systems, since a large number of experiences are necessary to achieve a good performance. In this paper, model-based reinforcement learning is developed for a group of self-interested agents with sequential action selection based on traditional prioritized sweeping. Every single situation of decision making in this learning process, called extensive Markov game, is modeled as n-person general-sum extensive form game with perfect information. A modified version of backward induction is proposed for action selection, which adjusts the tradeoff between selecting subgame perfect equilibrium points, as the optimal joint actions, and learning new joint actions. The algorithm is proved to be convergent and discussed based on the new results on the convergence of the traditional prioritized sweeping.