فصلنامه هوش محاسباتی در مهندسی برق
سال چهارم شماره 4 (زمستان 1392)

This paper presents a variable structure rule-based fuzzy control for trajectory tracking and vibration control of a flexible joint manipulator by using chaotic anti-control. Based on Lyapunov stability theory for variable structure control and fuzzy rules، the nonlinear controller and some generic sufficient conditions for global asymptotic control are attained. The fuzzy rules are directly constructed subject to a Lyapunov function obtained from variable structure surfaces such that the error dynamics of control problem satisfy stability in the Lyapunov sense. Also in this study، the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal، the chaos dynamic must be created in the flexible joint system. So، the flexible joint system has been synchronized to chaotic gyroscope system. In this study، control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a controller which is capable to satisfy the control and anti- control aims. The performances of the proposed control are examined in terms of input tracking capability، level of vibration reduction and time response specifications. Finally، the efficacy of the proposed method is validated through experimentation on QUANSER’s flexible-joint manipulator.

Passive harmonic Filters (PHF) are the simplest, cheapest, and the most effective way to reduce harmonics of the voltage and current waveforms. In designing the PHF, the main goals are to minimize the cost of the filters and reduce the total harmonic distortion of currents and voltages at the Point of Common Coupling (PCC) to the acceptable range which are defined by standards. In this paper, an important multiobjective optimization approaches namely Strength Pareto Evolutionary Algorithm (SPEA) has been used for tuning the PHF parameters. This approach is applied to IEEE 13 node test system with a six-pulse converter as a harmonic source. Another approach, called Nash GA, is based on non-cooperative game gives an optimal point known as Nash equilibrium. This approach is also simulated on IEEE test system. The result of optimization shows lower THD and cost for optimal PHFs in comparison with the conventionally designed one. To make comparison, some results of these methods are compared and discussed.

This paper focuses on day-ahead (DA) retailing for fixed and Time-of-Use (TOU) price taker customers and DA real time pricing for active customers who participate in short-term markets. Customers’ response to the offered hourly prices are modeled using an hourly acceptance function which includes decreasing linear probability density functions based on the hourly minimum and maximum retail prices allowed by market regulators. Furthermore, the retailer offers to its active customers to participate in the DA demand response program and voluntary reduce their real time consumption for offered incentives. Numerical studies represent the effect of implementing demand response programs on the total benefit of retailing.

This paper proposes an adaptive Nonlinear Model Predictive Controller (NMPC) for hybrid position/velocity control of robot manipulators. Robot dynamics have generally uncertainties, including parameters variations, unknown nonlinearities of the robot, payload variations, and torque disturbances form the environment. The cost function of the NMPC is defined in such a way that by adjusting its weighting parameters, the end-effector of the robot tracks a predefined geometry path in Cartesian space with a constant velocity. Moreover, to eliminate the uncertainties, a neural network with Levenberg-Marquardt training algorithm is used to estimate adaptively the model of the robot. The closed-loop stability is demonstrated using Lyapunov theory. The validity of the proposed control method is shown by simulation results on a 3-DOF robot manipulator actuated by DC servomotors.

Hybrid electric vehicles (HEV) in addition to provide the benefits of electric vehicles could satisfy consumers for some performances of conventional internal combustion engine (ICE) vehicles such as acceleration and long range. On this way, suitable energy optimization strategies should be employed to get desired efficiency, less fuel consumption and pollution. One of the favorite and simple configurations of HEVs is parallel type. A student team at University of Kashan, IRAN have designed and manufactured Shaheb 2 hybrid electric vehicle. It is a separated-axle (or Through-to-Road (TTR)) parallel HEV type based on Pride platform. Employed energy management in Shaheb 2 is on/off strategy and three modes; motor, engine and hybrid have been implemented. This paper investigates the modeling of separated-axle (or TTR) parallel type of HEV in ADVISOR software and then evaluates two control strategies for Shaheb 2; on/off strategy and an intelligent control based on fuzzy logic. On this way, maximizing the engine is considered as objective function. The simulation results indicate that the fuzzy strategy leads to less fuel consumption and lower pollution for given UDDS driving cycle rather than on/off strategy for Shaheb 2.

This paper presents an emotional controller for brushless DC motor (BLDC) drive. The proposed controller is called brain emotional learning based intelligent controller (BELBIC). The utilization of the new controller is based on the emotion processing mechanism in brain. This intelligent control is inspired by the limbic system of mammalian brain, especially amygdala. The controller is successfully implemented in simulation using MATLAB software, brushless dc drive with trapezoidal back-emf. In this work, a novel and simple implementation of BLDC motor drive system is achieved by using the intelligent controller, which controls the motor speed accurately. This emotional intelligent controller has simple structure with high auto learning feature. Simulation results show that both accurate steady state and fast transient speed responses can be achieved in wide range of speed from 20 to 300 [rpm]. Moreover, to evaluate this emotional controller, the performance of the proposed control scheme is compared with both Fuzzy Logic (FL) and PID controllers, in different conditions. This indicates proper operating in comparison to the FLC and PID controllers. And also shows excellent promise for industrial scale utilization.