Sharif Digital Repository

Input nonlinearities of many physical systems due to their actuator constraints are important points which have to be considered by designers. One of the recent method for dealing with this problem is to use compensation after controller. In this paper, a new approach based on fuzzy sets theory, as a solution, has been proposed. The main objective of the proposed method is to provide a feasible controller output if it is not in the legal ranges to keep the system output performance as high as possible. © 2002 IEEE  

This paper proposes a novel decentralized control approach for islanded direct-current (DC) microgrids (MGs) based on model predictive control (MPC) to regulate the distributed generation unit (DGU) output voltages, i.e. the voltages of the point of common coupling (PCC). A local controller is designed for each DGU, in the presence of uncertainties, disturbances, and unmodeled dynamics. First, a discrete-time state-space model of an MG is derived. Afterward, an MPC algorithm is designed to perform the PCC voltage control. The proposed MPC scheme ensures that the PCC voltages remain within an acceptable range. Several simulation studies have been conducted to illustrate the effectiveness of... 

This paper presents a dynamic programming methodology for finding the optimum preventive maintenance schedule of generating units of a GENCO in open power systems. The objective function for the GENCO is to sell electricity as much as possible, according to the market price forecast. Various constraints such as generation capacity, duration of maintenance and maintenance crew are taken into account. In a case study, introducing a GENCO with six generating units, and implementing dynamic programming, we obtain the optimal maintenance schedule over the planning period. © 2002 IEEE  

A repeated inflation-unemployment game within the linear-quadratic frame-work of Barro and Gordon is studied assuming that the government would like to cheat optimally and the finite heterogeneous population of private agents attempts to learn the government's targets using a reinforcement learning algorithm. Private agents are heterogeneous in their initial expectations of inflation rate but are assumed to utilize an identical anticipatory reinforcement learning process, namely Q-learning. In our heterogeneous setting, the only way for the private agents to achieve a zero value for their loss function, is for all of them to correctly anticipate the Nash equilibrium. It is of particular... 

A repeated inflation-unemployment game within the linear-quadratic framework of Barro and Gordon is studied assuming that the government would like to cheat optimally and the finite heterogeneous population of private agents attempts to learn the government's targets using a reinforcement learning algorithm. Private agents are heterogeneous in their initial expectations of inflation rate but are assumed to utilize an identical anticipatory reinforcement learning process, namely Q-learning. In our heterogeneous setting, the only way for the private agents to achieve a zero value for their loss function, is for all of them to correctly anticipate the Nash equilibrium. It is of particular... 

Electromyogram (EMG) signal is an electrical manifestation of muscle contractions. EMG signal collected from surface of the skin, a non-invasive bioelectric signal, can be used in different rehabilitation applications and artificial extremities control. This study has proposed to utilize the surface EMG (SEMG) signal to recognize patterns of hand prosthesis movements. It suggests using an adaptive neuro-fuzzy inference system (ANFIS) to identify motion commands for the control of a prosthetic hand. In this work a hybrid method for training fuzzy system, consisting of back-propagation (BP) and least mean square (LMS) is utilized. Also in order to optimize the number of fuzzy rules, a... 

In the last three decades, optimality-based autolanding designs have been considered to the most effective way by many authors. However, it is known that the straight forward solution to the optimal control problem leads to Two Point Boundary Value Problem (TPBVP)(Riccati equation), which is usually too complex in solution, backward in the time, and real-time onboard implementation, or the final time, as a boundary condition, may also not be known precisely. To avoid these problems, first, a suboptimal solution by assuming tf →∞ has been considered and its inapplicability has been discussed. Then an optimal controller for landing phase of a typical commercial aircraft has been designed.... 

This book provides a comprehensive presentation of issues and challenges faced by researchers and practicing engineers in motion planning and hybrid control of dynamical legged locomotion. The major features range from offline and online motion planning algorithms to generate desired feasible periodic walking and running motions and tow-level control schemes, including within-stride feedback laws, continuous time update laws and event-based update laws, to asymptotically stabilize the generated desired periodic orbits. This book describes the current state of the art and future directions across all domains of dynamical legged locomotion so that readers can extend proposed motion planning... 

In this paper we propose a new method for image noise reduction based on wavelet transform. In this method we: introduce an improved version of thresholding neural networks. (TNN) by utilizing a new class of smooth nonlinear thresholding functions as the activation function. Using this approach we will find the best thresholds in the sense of minimum mean square error (MMSE). Then using, TNN with obtained thresholds, we employ a cycle-spinningbased technique to reduce image artifacts. Experimental results indicate that the proposed method outperforms several other established wavelet denoising techniques, in terms of Peak-Signal-to-Noise-Ratio (PSNR) and visual quality. © 2007 IEEE  

In this paper, a new control strategy for optimal attitude tracking control of a multivariable satellite system has been presented. The approach is based on Radial Basis Function Neural Network (RBFNN) and classical PD Controller for its initial stabilization. It is shown how the network can be employed as a multivariable self- organizing and self learning controller in conjunction with a PD controller for attitude control of a satellite. By using four reaction wheels and quaternion for kinematics representation, the attitude dynamics of the satellite has been presented. In contrast to the previous classical approaches, it is shown how this controller can be carried out in an on-line manner... 

Lateral spreading generated by earthquake induced liquefaction, is a major cause for significant damage to the engineered structures, during earthquakes. Knowing the amount of displacement which is likely to occur due to the lateral spreading, will lead to better construction policies, and will reduce unexpected damages. A Neuro-Fuzzy model based on subtractive clustering is developed to predict the amount of lateral spreading expected to occur due to an earthquake. A large database containing the case histories of observed lateral spreading during seven major earthquakes of the past is used for training and evaluating the models. The results of this study show that Neuro-Fuzzy method serves... 

In this paper a new control strategy for adaptive attitude control of multivariable satellite system has been presented. The approach is based on radial basis function neural network (RBFNN). By using four reaction wheels and Modified Rodrigues Parameters (MRPs) for attitude representation, the attitude dynamic of satellite has been considered. The Lyapunov stability theory has been used to achieve a stable closed loop system. Also to enhance the robustness of the controller, the RBF neural network has been employed to estimate the model base terms in control law. The control objective is the plant to track a reference model. Simulation results illustrate the performance of the on-line... 

Application of fractional order PID (FOPID) controller to an automatic voltage regulator (AVR) is presented and studied in this paper. An FOPID is a PID whose derivative and integral orders are fractional numbers rather than integers. Design stage of such a controller consists of determining five parameters. This paper employs particle swarm optimization (PSO) algorithm to carry out the aforementioned design procedure. PSO is an advanced search procedure that has proved to have very high efficiency. A novel cost function is defined to facilitate the control strategy over both the time-domain and the frequency-domain specifications. Comparisons are made with a PID controller and it is shown... 

Under-voltage load shedding is one of the most important tools for avoiding voltage instability. In this paper, an optimal load-shedding algorithm is developed. This approach is based on the concept of the static voltage stability margin and its sensitivity at the maximum loading point or the collapse point. The traditional load-shedding objective is extended to incorporate both technical and economic effects of load shedding. The voltage stability criterion is modeled directly into the load-shedding scheme. The proposed methodology is implemented over the IEEE 14 and 118 bus test systems and solved using a mathematical (GAMS/CONOPT) and two heuristic (PSO and GA) methods. The heuristic... 

Application of Fractional Order PID (FOPID) controller to an Automatic Voltage Regulator (AVR) is presented and studied in this paper. An FOPID is a PID whose derivative and integral orders are fractional numbers rather than integers. Design stage of such a controller consists of determining five parameters. This paper employs Particle Swarm Optimization (PSO) algorithm to carry out the aforementioned design procedure. A novel cost function is defined to facilitate the control strategy over both the time-domain and the frequencydomain specifications. Comparisons are made with a PID controller from standpoints of transient response, robustness and disturbance rejection characteristics. It is... 

In the restructured power markets, price of electricity has been the key of all activities in the power market. Accurately and efficiently forecasting electricity price becomes more and more important. Therefore in this paper, an Artificial Neural Network (ANN) model is designed for short term price forecasting of electricity in the environment of restructured power market. The proposed ANN model is a four-layered perceptron neural network, which consists of, input layer, two hidden layers and output layer. Instead of conventional back propagation (BP) method, Levenberg- Marquardt BP (LMBP) method has been used for the ANN training to increase the speed of convergence. Matlab is used for... 

Link adaptation of a wireless cellular network is of much attention due to the desire of maximizing both the a erage lin throughput and co erage reliability, which have conflicting effect on each other. In this paper, the intra-cell lin adaptation problem is formulated as a non-differentiable constrained optimization problem to maximize average link throughput while guaranteeing the best possible coverage reliability. To achieve this, a cutting plane optimization algorithm is employed. The performance of our method: adaptive modulation/coding with power management (AMCWPM) using proposed algorithm is compared with that of an adaptive modulation/coding (AMC) with no power management.... 

During the past years, various principal component analysis algorithms have been developed. In this paper, a new approach for local nonlinear principal component analysis is proposed which is applied to capture voice conversion (VC). A new structure of autoassociative neural network is designed which not only performs data partitioning but also extracts nonlinear principal components of the clusters. Performance of the proposed method is evaluated by means of two experiments that illustrate its efficiency; at first, performance of the network is described by means of an artificial dataset and then, the developed method is applied to perform VC  

In this paper, a new constructive auto-associative neural network performing nonlinear principal component analysis is presented. The developed constructive neural network maps the data nonlinearly into its principal components and preserves the order of principal components at the same time. The weights of the neural network are trained by a combination of Back Propagation (BP) and Genetic Algorithm (GA) which accelerates the training process by preventing local minima. The performance of the proposed method was evaluated by means of two different experiments that illustrated its efficiency. ©2007 IEEE  

Recently small satellites are used more commonly because of the low launching cost and development of microelectronics. Also lower weight, size, cost and the power consumption of magnetorquer, has made the application of them in controlling attitude of the satellites common. Intensive changes and non-ability of geomagnetic field are some of the problems, which have limited the efficiency of magnetorquers. In this paper a new control method is presented that keeps the attitude of satellite in desired condition only by electromagnetic coils. The distinction of this method is its abilities in comparison with other methods. In this analytic method a direct relation between design parameters,...