Showing papers by "Mojtaba Ahmadieh Khanesar published in 2021"

Electrical Load Prediction Using Interval Type-2 Atanassov Intuitionist Fuzzy System: Gravitational Search Algorithm Tuning Approach

Abstract: Establishing accurate electrical load prediction is vital for pricing and power system management. However, the unpredictable behavior of private and industrial users results in uncertainty in these power systems. Furthermore, the utilization of renewable energy sources, which are often variable in their production rates, also increases the complexity making predictions even more difficult. In this paper an interval type-2 intuitionist fuzzy logic system whose parameters are trained in a hybrid fashion using gravitational search algorithms with the ridge least square algorithm is presented for short-term prediction of electrical loading. Simulation results are provided to compare the performance of the proposed approach with that of state-of-the-art electrical load prediction algorithms for Poland, and five regions of Australia. The simulation results demonstrate the superior performance of the proposed approach over seven different current state-of-the-art prediction algorithms in the literature, namely: SVR, ANN, ELM, EEMD-ELM-GOA, EEMD-ELM-DA, EEMD-ELM-PSO and EEMD-ELM-GWO.

Prediction Interval Identification Using Interval Type-2 Fuzzy Logic Systems: Lake Water Level Prediction Using Remote Sensing Data

Abstract: This paper presents a novel approach to identify the prediction interval associated with data using interval type-2 fuzzy logic systems with support vector regression. For such a purpose, a constrained quadratic objective function is defined which is then solved using well-established quadratic programming approaches. Not only does the output of interval type-2 fuzzy logic system replicates the measured value, but also it provides the lower bound and the upper bound for measured data values. In the proposed approach, to have more valuable information, a penalty term is added in the cost functions to have full control over the width of prediction interval. This method has been successfully applied to two benchmark identification problems. It is observed that by using the control parameter in the cost function, it is possible to obtain a narrower, yet inclusive prediction interval. Furthermore, superior prediction accuracy is obtained compared to existing methods in literature. Motivated by these results, the proposed approach is used to predict time series collected using a satellite from Urmia lake water level which resulted in high accuracy and an inclusive prediction interval. The graphical abstract presented for the paper illustrates the overall data gathering as well as data analysis made to estimate the prediction interval associated with Urmia lake water level data.

Classical Sliding-Mode Controllers

Abstract: Lack of imprecise nonlinear model of real-time systems is inevitable due to several simplifications made, neglected frictions, dead-zones, and saturation. One of the most well-known nonlinear control design tools to deal with uncertainties is sliding-mode control approach. In this method the desired behavior is defined in terms of a sliding manifold. The sliding manifold is stable, and the controller is designed to push system states to this manifold and maintain them on it. The convergence of the sliding manifold to zero usually occurs in finite time which necessitates the use of a switching function; with which the robustness of the system is improved. This chapter deals with the design of classical sliding mode controllers, adaptive sliding mode control approach and terminal sliding mode controllers.

Adaptive Sliding-Mode Fuzzy Control Systems: Gradient Descent Method

Abstract: Gradient descent is a computational optimization method which is based on the first-order Taylor expansion of nonlinear functions. In order to find a local minimum for a nonlinear function, this algorithm uses the initial parameters of the nonlinear function and updates these parameters in the negative direction of the gradient of the function with respect to the parameters. In this chapter, adaptive fuzzy control design procedure using the partial derivatives of a sliding mode cost function and Gradient descent estimation method is explained.

Adaptive Sliding-Mode Fuzzy Control Systems: Lyapunov Approach

Abstract: This chapter deals with adaptive design of fuzzy controllers based on sliding-mode control law. As it was mentioned earlier, a challenge to design a sliding- mode controller is necessity to have the nominal dynamics of the system. This requires a series of modeling prior to the control of the system. Fuzzy logic systems as general function approximators are used to deal with un-modeled dynamics of the system. Lyapunov stability analysis method is used to tune the parameters of the fuzzy logic systems.

Adaptive Network Sliding-Mode Fuzzy Logic Control Systems

Abstract: Centralized direct digital control systems have several drawbacks, for example, massive wiring requirements, difficult diagnosis, and difficult fault detection procedures. Most of these drawbacks may impose heavy costs on the maintenance of the control system. These disadvantages have given rise to the development of NCSs. In a network-based control system, sensors, actuators, controllers, human–machine interfaces, and other possible components of the system share their data over a network [1]. In this chapter, adaptive network sliding mode fuzzy logic control system approach is explained. Lyapunov stability analysis is used to analyze the stability of the studied controller.

Intelligent Optimization of Sliding-Mode Fuzzy Logic Controllers

Abstract: Optimization is the selection process of the best elements with respect to some criterion from a feasible set of variables. There may be single or multiple objectives to be considered during optimization. The optimization process generally involves the minimization of a cost or maximization of a profit. Sliding mode controller design problem ends up with the selection of values for its parameters which may include trials and errors. In this chapter, using intelligent optimization approaches, the parameters of sliding mode controllers are optimized to minimize the effect of chattering while the tracking error is minimized. Since these two objective functions are conflicting objective functions, it is required to use multiobjective intelligent optimization approaches.