Madhusudan Singh

Adaptive control of DC motor using bacterial foraging algorithm

This paper introduces a bacterial foraging algorithm (BFA) based high performance speed control system for a DC motor. The rotor speed of the DC motor is being made to follow an arbitrary selected trajectory. The unknown nonlinear dynamics of the motor and the load are captured by BFA. The trained BFA identifier is used with a desired reference model to achieve trajectory control of DC motor. In this paper bacterial foraging algorithm (BFA) has been implemented for identification and control of DC motor. Simulation study on proposed system has been carried out in MATLAB. System nonlinearities alpha and beta have been estimated using BFA and compared with actual plant nonlinearities of dynamical system. In tracking of motor speed using BFA based controller the performance of the motor have been observed and compared with reference one. Performance study of DC motor has been carried out through genetic algorithm (GA) also. A comparison of performance analysis using BFA controller and that of GA for trajectory tracking shows that BFA based adaptive controller works effectively for tracking the desired trajectory in DC motor with less computational time.

Efficiency optimization of vector controlled induction motor drive

In this paper an efficiency optimization algorithm for vector controlled Induction Motor (IM) Drive has been developed and the performance of the IM Drive under different operating conditions has been analyzed. The controllable electrical loss in IM Drive is minimized by the optimal control of direct axis (d-axis) stator current, which controls the magnitude of rotor flux. Thus, the efficiency of the motor is optimized by weakening the rotor flux, which in turn reduces the core losses. The implemented algorithm is tested under different operating conditions of IM Drive including sudden change in load, commanded speed and equivalent circuit parameter variations of IM and performance of the drive under dynamic condition is analyzed in detail.

Performance evaluation of BLDC motor with conventional PI and fuzzy speed controller

Performance evaluation of a brushless DC (BLDC) motor with conventional PI and Fuzzy Logic based speed controller is presented in this paper. The BLDC motor with non-sinusoidal (trapezoidal) back-electromotive force has been analyzed with both the speed and current controllers. It is the always preferred to have a drive with faster and smoother speed response and reduced ripples in current and torque. The controlling schemes for BLDCM using its Back EMF have been conventionally used into many applications. In this paper, a simple control scheme has been implemented which does not need to know the back emf or shape function and even no complicated calculations. In order to solve the problems associated with conventional PI speed controller, Fuzzy logic speed controller is proposed to reduce starting current, eliminate overshoot in the torque and fast speed response. It is simple in design and eliminates the complex mathematical computation. The effectiveness of proposed system has been validated by simulation results and improved performance of controlling BLDCM. The proposed algorithm gives robust control. The robustness of the proposed algorithm is demonstrated through the MATLAB simulation.

Performance evaluation of multilevel inverter with advance PWM control techniques

Performance evaluation of a five level cascaded multilevel inverter (MLI) using advance pulse width modulation (PWM) techniques for constant switching frequency (CSF) operation are presented in this paper. Multilevel inverter operation for CFO with Phase disposition (PD), Phase opposition disposition (POD), Alternate phase opposition disposition (APOD) PWM control methods is simulated in MATLAB. Also variations in MLI performance parameters such as Total Harmonic Distortion (THD) in output voltage, peak value of fundamental component of voltage, etc. are analyzed with each PWM control strategy. The effects of load perturbation on the profile of phase, line voltage and current in MLI are also described. A comparative study is presented in terms of THD, peak value of fundamental component of voltage and current under different load conditions.

Identification and control using MLP, Elman, NARXSP and radial basis function networks: a comparative analysis

This paper describes four neural networks multilayer perceptron (MLP) network, Elman network, NARXSP network and radial basis function (RBF) network. Neural networks are applied for identification and control of DC servo motor and benchmark nonlinear system. Number of epochs required and time taken to train the controller are shown in the form of bar plots for four neural networks. Levenberg-Marquardt algorithm is used for training the controller using neural network toolbox in MATLAB. Each neural network controller is run ten times. Their performances are compared for each run in terms of number of epochs required and time taken to train each controller for tracking a reference trajectory.

Simulation, output power optimization and comparative study of silicon and thin film solar cell modules

This paper presents a performance comparison between the polycrystalline thin film Photovoltaic (PV) module with mono crystalline silicon module in terms of their simulated current-voltage characteristics, output power and energy conversion efficiencies in standard test conditions. An accurate PV module electrical model is derived based on the Shockley diode equation. The output power of the modules has been maximised, with temperature and irradiation data as variable inputs, using maximum power point tracking (MPPT) methods to ensure optimum utilization of PV module. Simulation studies using three MPPT techniques have been presented. Fixed Reference Voltage method, Perturb and Observe method and Incremental Conductance method for MPPT are presented in details. The simulated results of all the three techniques for Silicon and Thin Film cells show excellent correspondence to the manufacturers data.

Particle swarm optimization based neural-network model for hydro power plant dynamics

This paper addresses the modeling of hydro power plant dynamics using neural network approach. The cost function as root mean square error is optimized by particle swarm optimization technique. The identification performance is compared with fuzzy models based on GK clustering algorithm in application to study hydro power plant dynamics. It is found that the response obtained from the NN model is comparable to those determined by fuzzy model with much significance to nature of input-output variables used for modeling.

Takagi-Sugeno Fuzzy System based Stable Direct Adaptive Control of Nonlinear Systems

paper proposes a novel idea for stable direct adaptive control of nonlinear systems using Lyapunov function with fuzzy approach. Stable direct adaptive control law consists of an ideal control, and a sliding mode control. Sliding mode controller is used to ensure the stability of Lyapunov function. Stability of direct adaptive control law is tested on two nonlinear systems. Non linear systems analyzed are ball beam system and cart pole system. A computer simulation is performed on nonlinear systems by using MATLAB. Keywordssystem, Lyapunov function, adaptive control, ball beam system and cart pole system.

Adaptive Control of Nonlinear Systems Using Bacterial Foraging Algorithm

 Abstract—In this paper bacterial foraging algorithm (BFA) have been implemented for indirect adaptive control of two nonlinear systems. The nonlinear systems considered in present analysis are liquid level control of surge tank and armature controlled DC motor speed control system. Simulation has been carried out in MATLAB. Using bacterial foraging algorithm plant nonlinearities component alpha and beta have been estimated and compared with actual plant nonlinearities for both the nonlinear systems. Liquid level estimated by BFA adaptive controller and actual liquid level has been compared in the liquid level nonlinear control system. Angular speed estimated by BFA adaptive controller and actual trajectory has been compared for DC motor speed control system. Fitness for the best member in the population of bacteria has been observed for both the nonlinear systems. In DC motor speed tracking obtained using indirect BFA adaptive control have been obtained and compared with reference one. Also liquid level set by indirect BFA adaptive controller have been obtained and compared with reference trajectory of level. Error plots show that error between desired and actual trajectory reduces after ten seconds of system operation in both nonlinear systems. Simulations studies have been also carried out through genetic algorithm (GA) for both nonlinear systems. A comparison of BFA results with GA shows that BFA based indirect adaptive controller are more effective in tracking the desired trajectory in both the nonlinear systems.

Development of supervisory control for distributed drives system

Continuous monitoring and control of motors and drives have become a need of process industries, which require operation of various drives in a pre-designated sequence. The programmable logic controllers (PLCs), an intelligent device, can realise automated operation. Distributed control system has partially autonomous local computational capability, interconnected through a digital communication link and coordinated by a supervisory control and data acquisition (SCADA) system. The resulting system has the advantages of local as well as centralized control. This paper describes the methodology, implementation, operation, monitoring and control of distributed drives with PLC and SCADA system. The performance of the drives are studied and analyzed for starting and load perturbation.