Fuzzy Gain Scheduling Research Articles

Trajectory tracking for robotic airships using sliding mode control based on neural network approximation and fuzzy gain scheduling

The robotic airship is a unique and promising light-than-air platform, which has attracted worldwide developing interests for its broad applications. This article addresses the control problem of trajectory tracking for robotic airships. A neural network fuzzy sliding mode controller is proposed to steer a robotic airship along a referenced trajectory precisely. First, the dynamics model of a robotic airship is presented, and the problem of trajectory tracking is formulated. Second, a sliding mode controller is designed to track a time-varying trajectory. The neural network is employed to approximate the uncertain model of the airship, with the tracking error and its derivatives and the trajectory and its derivatives as neural network inputs and the approximation of the uncertain model as neural network output. And a fuzzy logic system is adopted to reduce the chattering results from the sliding mode controller. The control gains are tuned synchronously with the sliding surface according to fuzzy rules, with switching sliding surface as fuzzy logic inputs and control gains as fuzzy logic outputs. The stability and convergence of the closed-loop controller are proven using the Lyapunov stability theorem. Finally, the effectiveness and robustness of the proposed controller are demonstrated via simulation results. Contrasting simulation results indicate that the neural network fuzzy sliding mode controller reduces the chattering effectively and has better performance against the sliding mode controller.

A Certain Investigation on Harmonic Reduction and Design of Fuzzy Gain Scheduler for Shunt Compensation for Power Quality Enhancement

Abstract This article describes a Synchronous reference frame based current control scheme for three phase shunt active power filters (SAPFs) to reduce or to remove harmonics, correct power factor, and balance asymmetrical load is analyzed. This active power filter (APFs) is used to compensate harmonic currents and reactive power generated from the nonlinear loads. In this article, to determine reference compensation currents under balanced source voltages, the voltage source inverter for harmonic filtering of a three phase shunt active filter is used. In this proposed method, a fuzzy gain scheduling controller is used and the response of the DC link voltage is compared with PI controller during transient and steady state. The main objective of this article is to analyze shunt active filters with fuzzy gain scheduling controllers and PI controllers. To carry out this analysis, active and reactive current methods (Id–Iq) are considered. Extensive simulations were carried out. The simulations were performed under balance sinusoidal conditions. The results validate the dynamic behavior of a fuzzy gain scheduling controller over PI controllers. The simulation of the proposed system is evaluated using MATLAB Simulink and the results show that the proposed method is more effective than the existing methods.

Fuzzy Speed Control of Networked Motion Control Systems

This paper proposed an integrated scheme for the modeling and control of induction motors in the networked environment. The networked control system (NCS) is built in hierarchical structure, which consists of a networked speed controller and a local controller. In the networked speed controller, fuzzy gain scheduling is applied to guarantee the robustness against communication constraints. Furthermore, a state predictor is designed to compensate the time delay occurred in data transmission from the sensor to the controller, as a component of the networked speed controller. Simulation and experimental results are given to illustrate the effectiveness of the proposed approach.

Fuzzy Gain Scheduling of PI Plus Derivative Controller to Improve the Transient Response of Boost DC/DC Converter

This paper illustrates a fuzzy gain scheduling of PI plus derivative controller. The proposed control technique improves the transient response of a DC/DC converter. The proposed control system is easy to implement based on an STM32F4 microcontroller. The performance evaluation was done by an experiment through a boost DC/DC converter, with a 24W load, a 12V input voltage, and a 24V output voltage, respectively. The system operation achieves tight output voltage regulation, both for the steady-state and transient responses.

Adaptive Fuzzy Gain Scheduling of PI Controller for Control of the Wind Energy Conversion Systems

In this work, the Wind Energy Conversion Systems (WECS) based on doubly fed induction generator (DFIG) model is built. First, we consider the vector control strategy of the active and reactive powers in order to ensure an optimum operation. The whole system is presented in d-q-synchronous reference frame. After, the design of Adaptive Fuzzy Gain Scheduling of Proportional Integral Controller (AFGPI) for WECS is described, where the optimization by Fuzzy rules is utilized online to adjust the parameters of PI controller based on the error and its first derivative. Finally, the control of the active and reactive power using fuzzy-PI controller is simulated using software Matlab/Simulink, studies on a 1.5 MW DFIG wind generation system compared with conventional proportional integral controller. Performance and robustness results obtained are presented and analyzed.

Proposal of a new hybrid control strategy for dynamic performance improvement of wound field synchronous generator-based wind turbines

This paper presents a simulation study of the control of a variable speed wind energy conversion system (WECS) using a wound field synchronous generator (WFSG). The proposed scheme is based on an RST controller with adaptive fuzzy gain scheduling (AFGS-RST) and regulates the active and reactive powers exchanged between the stator of the WFSG and the grid to ensure a Maximum Power Point Tracking of the WECS. The overall system model was implemented in MATLAB/Simulink with three different power controllers (the polynomial RST, the fuzzy logic controller, and the AFGS-RST controller). The performances of these controllers are evaluated under different operating conditions of the WECS power system. The results demonstrate that AFGS-RST controller is very effective in improving the power flow and very robust against variable transmission line parameters compared to the two other controllers.

GENETIC FUZZY APPROACH TO ADAPTIVE CRANE CONTROL SYSTEM

In automated manufacturing processes the safety, precise and fast transfer of goods realized by automated material handling devices is required to raise efficiency and productivity of manufacturing process. Hence, in those industrial branches where cranes are extensively used the problem of an anti-sway crane control is especially important to speed-up the time of transportation operations and ensures the safe and effective transportation operations. The precise positioning of a cargo requires controlling the speed of crane motion mechanisms to reduce the sway of a payload. Moreover, the anti-sway crane control scheme involves applying the adaptive techniques owing to the nonlinearities of a system that comes especially from stochastic variation of rope length on which a payload is suspended and mass of this payload. The paper provides the design method of an adaptive control system for a planar model of crane. The control system is based on the gain scheduling control scheme created using fuzzy logic controller with Takagi-Sugeno-Kang-type fuzzy implications. The design process of a gain scheduling control system consists in selecting such a suitable set of operating points at which the linear controllers are determined that interpolation control scheme ensures the expected control quality within the known range of nonlinear system parameters changes, when those parameters vary in relation to the exogenous variables: rope length and mass of a payload. The method that is proposed in this paper to solve the problem of designing the fuzzy gain scheduling crane control system for minimum set of operating points is based on the pole placement method and genetic algorithm.

Variable Structure Fuzzy Gain Schedule Based Load Frequency Control of Non-Linear Multi Source Multi Area Hydro Thermal System

The article focuses on the issues of Load Frequency Control (LFC) under non-linear strategies in multi source multi area hydro thermal system. On practical perspective dead band, boiler dynamics, reheat steam turbine along with hydro turbine operating under two different area capacities are considered in the system. When subjected to random load variations in both the areas, the system exhibits higher oscillations. The speed governor matches the generation with the demand. The offset in the area frequencies and tie-line power is removed by using secondary Proportional Integral (PI) controller. The PI controller is tuned using Ziegler Nichols' (ZN) and Fuzzy Gain Scheduling (FGS) method. The influence of high Proportional (P) controller gain during steady state and high Integral (I) controller gain during transient affects the system performance. Variable Structure System (VSS) helps to switch from P to PI controller during transient to steady state based on control error. The concept of VSS is applied to Fuzzy Gain Scheduling (FGS) PI controller. The performance of the optimal Variable Structure Fuzzy Gain Scheduled (VSFGS) controller under non-linear environment is judged and validated using performance indices.

Smart monitoring system based on adaptive current control for superconducting cable test.

A smart monitoring system for superconducting cable test is proposed with an adaptive current control of a superconducting transformer secondary. The design, based on Fuzzy Gain Scheduling, allows the controller parameters to adapt continuously, and finely, to the working variations arising from transformer nonlinear dynamics. The control system is integrated in a fully digital control loop, with all the related benefits, i.e., high noise rejection, ease of implementation/modification, and so on. In particular, an accurate model of the system, controlled by a Fuzzy Gain Scheduler of the superconducting transformer, was achieved by an experimental campaign through the working domain at several current ramp rates. The model performance was characterized by simulation, under all the main operating conditions, in order to guide the controller design. Finally, the proposed monitoring system was experimentally validated at European Organization for Nuclear Research (CERN) in comparison to the state-of-the-art control system [P. Arpaia, L. Bottura, G. Montenero, and S. Le Naour, "Performance improvement of a measurement station for superconducting cable test," Rev. Sci. Instrum. 83, 095111 (2012)] of the Facility for the Research on Superconducting Cables, achieving a significant performance improvement: a reduction in the system overshoot by 50%, with a related attenuation of the corresponding dynamic residual error (both absolute and RMS) up to 52%.

Novel Adaptive Fuzzy Inference Controller for Highly Nonlinear System

Robot manipulators are multi inputs-multi outputs, nonlinear and time variant system, therefore control of this system is the main challenge in robotic science. Control robot arm according to linear methodology are often having lots of problems because robotic systems are always highly nonlinear. Design accurate controller for robot manipulator is difficult because some dynamic parameters such as compliance and friction are not well understood and some robot parameters such as inertia are difficult to measure accurately and caused to variation in dynamic response. Fuzzy logic controllers have been applied in many applications. One of the important methods to solve above challenge is design adaptive controller based on fuzzy logic. In this paper, intelligent control of robot arm using Proportional-Integral-Derivative (PID) Adaptive Fuzzy Gain scheduling (PID-AFGS) is design for two degrees of robot arm.

Performance Assessment of Tuning Methods for PID Controller Parameter used for Position Control of DC Motor

In this paper, a comparison is made for tuning methods of PID controller to improve the performance of the position control system of DC motor. Firstly, the PID controller is tuned using conventional Ziegler –Nichols method and then by using Simulink Response Optimization tool box available in MATLAB software. Further, Fuzzy gain scheduling of PID controller is investigated in which fuzzy rules are utilized on-line to adapt the PID controller parameters based on the error and its first time derivative. The result shows that the gain scheduling scheme for DC motor position control system have presented superior performance compared to other methods.

Current‐sharing bus and fuzzy gain scheduling of proportional–integral controller to control a parallel‐connected AC/DC converter

In this study, a current-sharing bus and fuzzy gain scheduling of proportional–integral (PI) controller to control a parallel-connected AC/DC converter are proposed. A simple current-sharing circuit is applied to divide the load current to calculate the peak input current of each converter module. Fuzzy gain scheduling of PI controller is used to compensate for the error of the output voltage. The dsPIC30F6010 microcontroller is applied in the voltage loop control. In addition, a hysteresis control circuit is used to control the current loop. The experimental performance evaluation is conducted on a prototype, with three converter modules that are parallel-connected, has 250 W/module and has a −48 V DC output voltage. The system offers the following advantages: the tight output voltage regulates both the steady-state operation and the transient response. The current sharing is quite good, and it is simpler to operate in the redundant mode.

A novel hybrid PSO-PS optimized fuzzy PI controller for AGC in multi area interconnected power systems

Abstract In this paper, a novel hybrid Particle Swarm Optimization (PSO) and Pattern Search (PS) optimized fuzzy PI controller is proposed for Automatic Generation Control (AGC) of multi area power systems. Initially a two area non-reheat thermal system is used and the gains of the fuzzy PI controller are optimized employing a hybrid PSO and PS (hPSO-PS) optimization technique. The superiority of the proposed fuzzy PI controller has been shown by comparing the results with Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), conventional Ziegler Nichols (ZN), Differential Evolution (DE) and hybrid BFOA and PSO based PI controllers for the same interconnected power system. Additionally, the proposed approach is further extended to multi source multi area hydro thermal power system with/without HVDC link. The superiority of the proposed approach is shown by comparing the results with some recently published approaches such as ZN tuned PI, Variable Structure System (VSS) based ZN tuned PI, GA tuned PI, VSS based GA tuned PI, Fuzzy Gain Scheduling (FGS) and VSS based FGS for the identical power systems. Further, sensitivity analysis is carried out which demonstrates the ability of the proposed approach to wide changes in system parameters, size and position of step load perturbation The proposed approach is also extended to a non-linear power system model by considering the effect of governor dead band non-linearity and the superiority of the proposed approach is shown by comparing the results of hybrid BFO-PSO and craziness based PSO approach for the identical interconnected power system. Finally, the study is extended to a three area system considering both thermal and hydro units with different controllers in each area and the results are compared with hybrid BFO-PSO and ANFIS approaches.

Load frequency control using Bat inspired algorithm based dual mode gain scheduling of PI controllers for interconnected power system

Abstract This paper highlights the load frequency control using dual mode Bat algorithm based scheduling of PI controllers for interconnected power systems. The bat inspired algorithm based on the echolocation of bats has been developed in 2010. In this study, the bat inspired algorithm based dual mode PI controller is applied to the multi-area interconnected thermal power system in order to tune the parameter PI controllers. The proposed controller is simple in structure and easy for implementation. The proposed controller was compared with those from conventional the PI controllers and Fuzzy gain scheduling of PI controllers. The simulation results show the point that the proposed bat inspired algorithm, based dual mode gain scheduling of PI controllers (BIDPI), provides better transient as well as steady state of response. It is also found that the proposed controller is less sensitive to the changes in system parameters.

Design Adaptive Fuzzy Inference Controller for Robot Arm

Design robust controller for uncertain nonlinear systems most of time can be a challenging work. One of the most active research areas in this field is control of the robot arm. The control strategies for robotics are classified in two main groups: classical and non-classical methods, where the classical methods use the conventional control theory and non- classical methods use the artificial intelligence theory such as fuzzy logic, neural networks and/or neuro-fuzzy. Control robot arm using classical controllers are often having lots of problems because robotic systems are always highly nonlinear. Accurate robot manipulator is difficult because some dynamic parameters such as compliance and friction are not well understood and some robot parameters such as inertia are difficult to measure accurately. Artificial control such as Fuzzy logic, neural network, genetic algorithm, and neurofuzzy control have been applied in many applications. Therefore, stable control of a nonlinear dynamic system such as a robot arm is challenging because of some mentioned issues. In this paper the intelligent control of robot arm using Adaptive Fuzzy Gain scheduling (AFGS) and comparison to fuzzy logic controller (FLC) and various performance indices like the RMS error, and Steady state error are used for test the controller performance.

Load Frequency Control of Multi-source Multi-area Hydro Thermal System Using Flexible Alternating Current Transmission System Devices

This article describes the load frequency control of a multi-area system. Each control area contains both a hydro and thermal power plant to form a multi-source multi-area hydro thermal system. The secondary proportional-integral controller has been tuned using Ziegler–Nichols, genetic algorithm, and fuzzy gain scheduling methods. On comparing the controller performance based on various performance indices, it is found that a fuzzy gain scheduling tuned proportional-integral controller is suitable for a multi-source multi-area hydro thermal system. Further improvement on the load frequency dynamics has been achieved by connecting superconducting magnetic energy storage unit in each control area and a static synchronous series compensator unit on a tie-line.

Fuzzy Gain Scheduling of PID Controller for a MIMO Process

paper describes the development of a fuzzy gain scheduling scheme of PID controllers for three tank process. This paper presents the controllers for three tank multi loop system using fuzzy gain scheduling. The application of fuzzy logic controller (FLC) appears to be encouraging in the sense that it is robust in disturbance rejection under various conditions. The controller designed by FLC technique is based on the choice of Fuzzy rules and Reasoning is used to determine the controller parameters based on the error signal and its first difference. Simulation results show that better control performance can be achieved in comparison with conventional-PI controllers. The simulation result of the process is carried out by using MATLAB simulink software.

Alleviation of power fluctuation in a microgrid by electrolyzer based on optimal fuzzy gain scheduling PID control

AbstractThis paper presents an application of the electrolyzer (EZ) to alleviate the power fluctuation in a microgrid with hybrid power generations from wind, photovoltaic array, fuel cell, and diesel engine. In this microgrid, the intermittent power generations from wind and photovoltaic arrays cause severe power fluctuation. With the fast response of EZ, the power absorbed by EZ can be controlled to compensate for the power fluctuation, in addition to the hydrogen production for fuel cell. The structure of the active and reactive power controllers of EZ is the fuzzy gain scheduling of a proportional‐integral‐derivative (FGS‐PID) controller. Without trial and error, the scale factors, membership functions, and control rules of the FGS‐PID controller are automatically optimized by bee colony optimization. A simulation study confirms that the proposed EZ with optimal FGS‐PID controller is much superior to the optimal PID controller in terms of damping effect, robustness against disturbances, and hydrogen production. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A MIMO Sampling Rate Dependent Controller

A sampling-rate-dependent (SRD) controller is proposed for the uniform output tracking problem of multiinput multioutput discrete time-varying linear systems. A proportional-integral-derivative (PID) controller is a special case of the proposed controller. The SRD controller aims at achieving uniform output tracking in the sense of attaining arbitrary small steady-state errors as well as arbitrary small settling time. The SRD controller makes use of higher sampling rates for larger variations in the desired trajectories. An analytic approach for finding the parameters of the SRD controller that guarantees reference output trajectory tracking is presented. Four different numerical examples and two different experimental applications are provided in order to illustrate the output tracking capability of the proposed SRD controller. Performance is compared to a PID controller with fuzzy gain scheduling, four multivariable PID controllers, an H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sup> optimal controller, and an iterative-learning-control algorithm.

Fuzzy Based Intelligent Approach to AGC of Multi-Area Multi-Unit Power System with SMES under Random Load Disturbances

In reality, load variations in power systems are random in nature. Therefore, the automatic generation control (AGC) performance of the power system needs to be investigated under random load disturbances so as to have a realistic evaluation of the control strategy. This paper reports results for one such investigation. The intelligent control strategy, based on fuzzy gain scheduling of a proportional-integral (PI) controller, is developed and implemented for a multi-area multi-unit thermal power system with reheat nonlinearity. The paper also investigates the effect of superconducting magnetic energy storage (SMES) system on the AGC performance. For the sake of comparison, the behavior of the system for the same load disturbance is also investigated with a conventional PI controller. Simulation studies indicate that the proposed intelligent control strategy is very effective under random load disturbances and provides significant improvement over the conventional PI controller.