Ashwani Kharola

Automated Control and Optimisation of Overhead Cranes

Thisstudyconsidersafuzzybasedcomputingtechniqueforcontrolandoptimisingperformance ofoverheadgantrycrane.Theobjectiveistominimiseloadswingandstabilisecranepositionin the least possible time. The fuzzy controllers were designed using nine gaussian and triangular shapemembershipfunctions.Theresultsclearlyconfirmedtheeffectofshapeofmembershipson performanceoffuzzycontrollers.Performanceofoverheadcranewasmeasuredintermsofsettling timeandovershoot.Thestudyalsodemonstratestheinfluenceofvaryingmassoftheload,massof craneandlengthofcranebaronstabilityofthecrane.Amathematicalmodelofthecranesystemhas beenderivedtodevelopasimulinkmodelofproposedsystemandperformingsimulations. KEywORdS Fuzzy Logic, Gbell Memberships, Mathematical Model, Matlab, Overhead Cranes, Simulation, Simulink, Triangular Memberships

Soft Computing Based Adaptive Error Optimisation for Control of Nonlinear System

Thispaperelaboratesanovelhybridlearningapproachfortrainingerroroptimisationandcontrolof highlydynamictriple-linkinvertedpendulumoncart.Thestudydemonstratesarelationshipbetween shapeandnumberofmembershipfunctions(MFs)ofbothlinearandconstanttypetodetermine training error tolerance of ANFIS controller. The results are plotted which clearly highlighted supremacyofconstanttypethreetriangularshapeMFs.Mathematicalmodelandsimulinkofproposed systemhasalsobeenanalysed.Thelearningabilityanddesigningmethodologyofadaptivenetworks androbustnessofPIDcontrollersarebrieflydescribed.Finally,thestudyillustratesanofflinemode comparisonofPIDbasedANFISandNeuralcontrollersintermsofsettlingtime,steadystateerror andovershoot. KEywORdS ANFIS, Membership Functions, Neural Networks, PID, Simulation, Soft Computing, Training Error, TripleLink Pendulum

Neural Fuzzy Control of Ball and Beam System

This paper presents an offline control of ball and beam system using fuzzy logic. The objective is to control ball position and beam orientation using fuzzy controllers. A Matlab/Simulink model of the proposed system has been designed using Newtons equations of motion. The fuzzy controllers were built using seven gbell membership functions. The performance of proposed controllers was compared in terms of settling time, steady state error and overshoot. The simulation results are shown with the help of graphs and tables which illustrates the effectiveness and robustness of proposed technique.

Dynamic stabilization of one wheel mobile robot (OWMR): a soft-computing approach

Abstract This paper presents application of various Soft-computing control strategies for offline control of one wheel mobile robot (OWMR). The techniques considered for controlling were Fuzzy logic reasoning, Adaptive neuro-fuzzy inference system (ANFIS), and Neural networks (NNs). The study compares the performance of proposed techniques in terms of settling time, maximum overshoot, and steady state error. A Matlab-Simulink model of OWMR system has also been developed. The results obtained from simulation of fuzzy controller were used to train ANFIS and NNs controller. The simulation results showed better performance and learning ability of ANFIS controller as compared to other two controllers. The results are shown with the help of graphs and tables to validate the proposed study.

Position and Tilt Control of Two-Wheeled Robot (TWR): A Neuro-Fuzzy Approach

This paper presents a fuzzy based adaptive control approach for stabilization of Two wheeled robot TWR system. The TWR consists of a robot chassis mounted on two movable wheels. The objective is to stabilize the proposed system within desired time, minimum overshoot and at desired location. The data samples collected from simulation results of fuzzy controllers were used for training, tuning and optimisation of an adaptive neuro fuzzy inference systemANFIS controller. A Matlab Simulink model of the system has been built using Newtons second law of motion. The effect of shape and number of membership functions on training error of ANFIS has also been analysed. The designing of fuzzy rules for both fuzzy and ANFIS controller were carried out using gbell shape memberships. Simulations were performed which compared and validated the performance of both the controllers.

A Comparative Study for Position Regulation and Anti-Swing Control of Highly Non-Linear Double Inverted Pendulum DIP System Using Different Soft Com

This paper presents a comparative analysis for stabilization and control of highly non-linear, complex and multi-variable Double Inverted Pendulum on cart. A Matlab-Simulink model of DIP has been built using governing mathematical equations. The objective is to control both the pendulums at vertical position while cart is free to move in horizontal direction. The control of DIP was achieved using three different soft-computing techniques namely Fuzzy logic reasoning, Neural networks NNs and Adaptive neuro fuzzy inference system ANFIS. The results show that the ANFIS controller is more effective as compared to other two controllers in terms of settling time sec, maximum overshoot degree and steady state error. The regression R and mean square error MSE values obtained after training of Neural network were adequate and the training error obtained in ANFIS was also optimum. All the three controllers were able to stabilize the DIP system but ANFIS control provides better results as illustrated with the help of graphs and tables.

Stabilization and Control of Elastic Inverted Pendulum System (EIPS) Using Adaptive Fuzzy Inference Controllers

ElasticInvertedPendulumsystem(EIP)areverypopularobjectsof theoretical investigationand experimentationinfieldofcontrolengineering.Thesystembecomeshighlynonlinearandcomplex duetotransversedisplacementofelasticpoleorpendulum.Thispaperpresentsacomparisonstudy forcontrolofEIPusingfuzzyandhybridadaptiveneurofuzzyinferencesystem(ANFIS)controllers. Initiallyafuzzycontrollerwasdesigned,whichwasusedfortrainingandtuningofANFIScontroller usinggbellshapemembershipfunctions(MFs).Theperformanceofcompletesystemwasevaluated throughoutputresponsesofsettlingtime,steadystateerrorandmaximumovershoot.Thestudyalso highlightseffectofvaryingnumberofMFsontrainingerrorofANFIS.Theresultsshowedbetter performanceofANFIScontrollercomparedtofuzzycontroller. KEywoRdS ANFIS, EIP, Epochs, Fuzzy, Hybrid, MFs, Training Error

Control Dynamics and Simulation of Inclined Cart and Pendulum System

Thispaperappliesvarioussoft-computingcontrolstrategiesforofflinemodecontrolofhighlynonlinearcartandpendulumsystemmovingonaninclinedsurface.Thesurfaceisconsideredatinclination of12°fromhorizontal.Thestudycomparesperformanceoffourdifferentcontroltechniquesnamely Proportional-integral-derivative(PID),Fuzzylogic,Adaptiveneurofuzzyinferencesystem(ANFIS) andNeuralnetworksforcontrolofproposedsystem.AMatlab-Simulinkmodelofsystemhasbeen developedfrommathematicalequationsderivedusingNewton’ssecondlaw.Thecartandpendulum systemhasbeeninitiallycontrolledusingPIDcontrollersandresultswerefurtherusedtotrainANFIS andneuralcontrollers.TheANFISandfuzzycontrollersweredesignedusingthreeandninegbell shapemembershipfunctions(MFs)respectively.Thecontrollerswerefurthercomparedintermsof settlingtime,overshootandundershoot. KEywoRDS ANFIS, Cart, Fuzzy Logic, Matlab, Neural Network, Pendulum, PID, Simulink

PID control of two-stage inverted pendulum

A Proportional-integral-derivative (PID) control of highly nonlinear and multivariable two stage pendulum is presented. The aim is to settle cart at desired position within 4 seconds while both the pendulums are inclined vertically upwards. Three PID controllers were separately designed for control of each subsystem. Tuning of controller gains were achieved using trial and error approach. A mathematical model of the system was derived which was utilised to construct a simulink. Simulations were performed which elaborated feasibility of proposed approach.

The control of two-wheeled inverted pendulum robot (TWIPR) using fuzzy logic

This paper presents a fuzzy logic control strategy for control and stabilization of TWIPR. The TWIPR considered in the study comprises of a robot chassis mounted on two movable wheels. The research objective is to stabilize the system within desired time, overshoot and steady state error. The Matlab Simulink model of the system was built using mathematical equations derived from Newtons second law of motion. Gbell MFs has been used for designing of fuzzy logic controllers (FLCs) for TWIPR. The simulation results proved the validity of the proposed technique.