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Neural Network Controller (neural + network_controller)
Selected AbstractsThe application of NN technique to automatic generation control for the power system with three areas including smes unitsEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 4 2003A. Demirören The study includes an application of layered neural network controller to study automatic generation control (AGC) problem of the power system, which contains superconducting magnetic energy storage (SMES) units. The effectiveness of SMES unit over frequency oscillations improvement against load perturbations in power system is well known. In addition, the proposed control scheme provides the steady state error of frequency and inadvertent interchange of tie-lines to be maintained in steady state values. The power system considered has three areas two of which including steam turbines while the other containing a hydro turbine, and all of them contain SMES units, in addition. In the power system each area with a steam turbine contains the non-linearity due to reheat effect of the steam turbine and all of the areas contain upper and lower constraints for generation rate. Only one neural network (NN) controller, which controls all the inputs of each area in the power system, is considered. In the NN controller, back propagation-through-time algorithm is used as neural network learning rule. The performance of the power system is simulated by using conventional integral controller and NN controller for the cases with or without SMES units in all areas, separately. By comparing the results for both cases, it can be seen that the performance of NN controller is better than conventional controllers. [source] Load frequency control for power system with reheat steam turbine and governor deadband non-linearity by using neural network controllerEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 3 2002H. L. Zeynelgil In this paper, a neural network (NN) controller is presented for load-frequency control of power system. The NN controller uses back propagation-through-time algorithm. In the power system, the reheat effect of the steam turbine and the effect of governor deadband non-linearity are considered by describing function approach in the state space model. By comparing the results of simulations, the performance of the NN controller is better than conventional controller. NN controller gives a shorter settling time and eliminates the necessity of parameter estimation time required in conventional adaptive control techniques. [source] How to optimize the TS-fuzzy knowledge base to achieve desired performances: Accuracy and robustnessOPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 1 2008A. Soukkou Abstract Designing an effective criterion/learning to find the best rule and optimal structure is a major problem in the design process of fuzzy neural controller. In this paper, we introduce a new robust model of Takagi Sugeno fuzzy logic controller. A hybrid learning algorithm, called hybrid approach to fuzzy supervised learning (HAFSL), which combines the genetic algorithm (GA) and gradient descent technique (GD) is proposed for constructing an efficient and robust fuzzy neural network controller (FNNC). Two phases of design and learning process are presented in this work. A GA is used for finding near optimal structure/parameters of the FNNC that minimizes the number of rules (initialization procedure). The second stage of learning algorithm uses the backpropagation algorithm based on GD method to fine tune the consequent parameters of the controller. The genes of chromosome are arranged into two parts, the first part contains the control genes (the certainty factors) and the second part contains the parameters genes that representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the HAFSL are compared to these found by the traditional PI with genetic optimization (GA-PI). Simulations demonstrate that the proposed HAFSL and GA-PI algorithms have good generalization capabilities and robustness on the water bath temperature control system. Copyright © 2007 John Wiley & Sons, Ltd. [source] Robust Tracking Control For A Wheeled Mobile Manipulator With Dual Arms Using Hybrid Sliding-Mode Neural NetworkASIAN JOURNAL OF CONTROL, Issue 4 2007Ching-Chih Tsai ABSTRACT In this paper, a robust tracking controller is proposed for the trajectory tracking problem of a dual-arm wheeled mobile manipulator subject to some modeling uncertainties and external disturbances. Based on backstepping techniques, the design procedure is divided into two levels. In the kinematic level, the auxiliary velocity commands for each subsystem are first presented. A sliding-mode equivalent controller, composed of neural network control, robust scheme and proportional control, is constructed in the dynamic level to deal with the dynamic effect. To deal with inadequate modeling and parameter uncertainties, the neural network controller is used to mimic the sliding-mode equivalent control law; the robust controller is designed to compensate for the approximation error and to incorporate the system dynamics into the sliding manifold. The proportional controller is added to improve the system's transient performance, which may be degraded by the neural network's random initialization. All the parameter adjustment rules for the proposed controller are derived from the Lyapunov stability theory and e-modification such that uniform ultimate boundedness (UUB) can be assured. A comparative simulation study with different controllers is included to illustrate the effectiveness of the proposed method. [source] | ||||||||||