Fuzzy Model Reference Learning Control Research Articles

Human-Robot Interaction of a Craniotomy Robot Based on Fuzzy Model Reference Learning Control

Human-Robot Interaction of a Craniotomy Robot Based on Fuzzy Model Reference Learning Control

Fuzzy model reference learning control in surgical robots

This paper proposes to apply the fuzzy model reference learning control method based on the guide control to the collaborative human-machine dragging process of the surgical robot. Variable damping coefficient adjustment parameter rules for fuzzy conductance controllers are trained by an offline learning mechanism in order to achieve the best human-machine interaction control effect. The surgical risk caused by the doctor's misoperation is lowered. Through the simulation experiment results, the speed curve tracking error is reduced by 70%. Trajectory curve tracking error is reduced by 57%. The both results prove that the strategy can reduce the error of the surgical robot and ensure the safety of robotic surgical operation.

Design Adaptive controller and learning controller to control the pacemaker: تصميم متحكم تكيفي ومتحكم متعلم للتحكم بناظم الخطى القلبي

Pacemakers play a vital role in the management of electrical disorders of the heart, as having a controller that presents a signal that generates a correct heart rate without delay or overshoot is important, to provide better quality and long-term patient's life. Many advances in pacemaker control technology have been achieved, making today's pacemakers capable of correcting many electrical heart abnormalities. In this paper, a fuzzy model reference learning control was designed to control the pacemaker, and compared with a controller adopts the adaptive mechanism instead of the learning mechanism, in order to confirm the importance of learning in the controller. The controller automatically controls the heart rate (Heart Rate: HR) to prevent it from falling under a reference value and makes it track preset signal accurately. The learning controller achieved better performance than the adaptive controller and the learning controller achieved when comparing with the similar intelligent ones that proposed to control pacemaker the required response with a small settling time 0.9s, and minimized the overshoot at start-up operation where overshoot was 0.2%

Regeneration Energy for Nonlinear Active Suspension System Using Electromagnetic Actuator

The main purpose of using the suspension system in vehicles is to prevent the road disturbance from being transmitted to the passengers. Therefore, a precise controller should be designed to improve the performances of suspension system. This paper presents a modeling and control of the nonlinear full vehicle active suspension system with passenger seat utilizing Fuzzy Model Reference Learning Control (FMRLC) technique. The components of the suspension system are: damper, spring and actuator, all of those components have nonlinear behavior, so that, nonlinear forces that are generated by those components should be taken into account when designed the control system. The designed controller consumes high power so that when the control system is used, the vehicle will consume high amount of fuel. It notes that, when vehicle is driven on a rough road; there will be a shock between the sprung mass and the unsprung mass. This mechanical power dissipates and converts into heat power by a damper. In this paper, the wasted power has reclaimed in a proper way by using electromagnetic actuator. The electromagnetic actuator converts the mechanical power into electrical power which can be used to drive the control system. Therefore, overall power consumption demand for the vehicle can be reduced. When the electromagnetic actuator is used three main advantages can be obtained: firstly, fuel consumption by the vehicle is decreased, secondly, the harmful emission is decreases, therefore, our environment is protected, and thirdly, the performance of the suspension system is improved as shown in the obtained results.

Battery Monitoring and Control System for Photovoltaic based DC Microgrid

Abstract This paper presents a battery control and monitoring strategy for a DC microgrid feed by a public utility (PU) photovoltaic (PV) including with multi-battery bank (BB). The BBs respond to the changes in a power imbalance between generation and demand within a DC micro-grid, to maintain the micro-grid voltage and reliability enhancement. The fuzzy model reference learning control (FMRLC) controller has been designed to power control of the battery bank. The proposed battery control and monitoring system (BCMS) strategy keep the battery charging and discharging power as per standard charging/discharging characteristics of the battery. The BCMS allows continuous monitoring and intelligent control of distribution system operations such as battery bank energy storage (BBES), the PV system and the electric load of the consumer. A power sharing between the sources has been controlled for smooth supply. A graphical user interface (GUI) in the MATLAB environment has been configured to demonstrate the control and monitoring of the DC micro-grid and the operation performance of the BCMS algorithm for various scenarios (i. e modes). The proposed BCMS shows better operational performance for various operating conditions in terms of monitoring and control of the batteries power in the grid-connected DC micro-grid.

Intelligent Control for Human-Robot Cooperation in Orthopedics Surgery.

Cooperation between surgeon and robot is one of the key technologies that limit the robot to be widely used in orthopedic clinics. In this study, the evolution of human-robot cooperation methods and the control strategies for typical human-robot cooperation in robot-assisted orthopedics surgery were reviewed at first. Then an intelligent admittance control method, which combines the fuzzy model reference learning control with the virtual constraint control, is proposed to solve the requirements of intuitive human-robot interaction during orthopedics surgery. That is, a variable damping parameter model of the admittance control based on fuzzy model learning control algorithm is introduced to make the robot move freely by using the reference model of surgeon's motion equation with the minimum jerk trajectory. And the virtual constraint control method based on the principle of virtual fixture is adopted to make the robot move within the pre-defined area so as to perform more safe surgery. The basic principle and its realization of this intelligent control method are described in details. At last, a test platform is built based on our designed 6 DOF articulated robot. Experiments of safety and precision on acrylic model with this method show that the robot has the ability of better intuitive interaction and the high precision. And the pilot experiment of bone tumor resection on sawbone model shows the effectiveness of this method.

Magnetic induction of hyperthermia by a modified self-learning fuzzy temperature controller.

The aim of this study involved developing a temperature controller for magnetic induction hyperthermia (MIH). A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The MIH system generated an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating had few side effects when it was extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with a gain tuning mechanism was implemented to obtain high control performance in a wide range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. In this study, the proposed SLFLC was compared to the classical self-tuning fuzzy logic controller and fuzzy model reference learning control. Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicated that the proposed controller showed greater robustness and excellent adaptability with respect to the temperature control of the MIH system.

A new quadrotor manipulation system: Modeling and point-to-point task space control

This article introduces a novel quadrotor manipulation system that consists of 2-link manipulator with unique topology attached to the bottom of a quadrotor. This new system presents a solution for the limitations found in the current quadrotor manipulation system. Unlike the current system, the proposed system enables the end effector to achieve an arbitrary position and orientation with minimum possible number of actuators/links. System kinematics and dynamics are derived. A new closed form inverse kinematics algorithm is presented such that a task space motion controller can be implemented. To study the feasibility of the proposed system, a quadrotor with high enough payload, to add the 2-link manipulator, is designed and constructed. The system controller is designed based on three control techniques: Feedback Linearization based PID (FL-PID), Direct Fuzzy Logic Control (DFLC), and Fuzzy Model Reference Learning Control (FMRLC). Simulation framework is implemented in MATLAB/SIMULINK based on real system parameters to emulate a realistic setup. These controllers are tested under the effect of picking/placing a payload and changing the operating region. Results enlighten the system feasibility, the superior performance of the FMRLC, and the efficiency of the proposed inverse kinematics algorithm.

SVC control enhancement applying self-learning fuzzy algorithm for islanded microgrid

Maintaining voltage stability, within acceptable levels, for islanded Microgrids (MGs) is a challenge due to limited exchange power between generation and loads. This paper proposes an algorithm to enhance the dynamic performance of islanded MGs in presence of load disturbance using Static VAR Compensator (SVC) with Fuzzy Model Reference Learning Controller (FMRLC). The proposed algorithm compensates MG nonlinearity via fuzzy membership functions and inference mechanism imbedded in both controller and inverse model. Hence, MG keeps the desired performance as required at any operating condition. Furthermore, the self-learning capability of the proposed control algorithm compensates for grid parameter’s variation even with inadequate information about load dynamics. A reference model was designed to reject bus voltage disturbance with achievable performance by the proposed fuzzy controller. Three simulations scenarios have been presented to investigate effectiveness of proposed control algorithm in improving steady-state and transient performance of islanded MGs. The first scenario conducted without SVC, second conducted with SVC using PID controller and third conducted using FMRLC algorithm. A comparison for results shows ability of proposed control algorithm to enhance disturbance rejection due to learning process.

A Proposed System of Ship Trajectory Control Using Particle Swarm Optimization

This paper discussed the operation of Particle Swarm Optimization (PSO) to optimize a Fuzzy Model Reference Learning Controller (FMRLC) for ship. FMRLC is developed by synthesizing several basic ideas from fuzzy set and control theory. It can achieve the heading regulation of ship exposed to plant changes and disturbances by adjusting the rules in a direct fuzzy controller so that the overall system behaves like a “reference model”. It is shown that PSO can provide a very promising technique for the design of FMRLC for its simplicity and ease of use. The promising results from the experiment provide direct evidence for the feasibility and effectiveness of PSO for the optimization of FMRL controller for ship heading regulation.

Adaptive Fuzzy Autopilot for a Scale Ship Model

This paper discusses the implementation of an adaptive fuzzy autopilot for a scale ship model using a Fuzzy Model Reference Learning Controller (FMRLC). Initially, a fuzzy singleton model of the scale ship model is obtained from experimental input and output data gathered from the turning circle manoeuvre. After that, the paper describes how to design a Fuzzy Model Reference Learning Controller (FMRLC) for controlling the directional heading of the scale ship model. At the end, results of the implementation of the adaptive ship autopilot are showed.

Fuzzy fault tolerant control for smart lights

This paper investigates the implementation of adaptive and nonadaptive controls for a smart lights experimental testbed. The objective is to accurately regulate the light level across the experimental testbed to a desired value, and to test the performance of the controllers under cross-illumination effects and bulb and sensor failures. As an initial approach, a decentralized (i.e., no communication between controllers) nonadaptive is implemented and applied to the smart lights. This approach is convenient for this type of experimental testbed where a mathematical model of the plant is not available and heuristic information about how to control the system is sufficient. The nonadaptive controller, when properly tuned, is able to achieve uniform lighting across the entire testbed floor in most of the tested situations but it fails whenever an on/off light bulb failure is introduced. In order to attain uniform lighting in the presence of failures, a fuzzy model reference learning controller (i.e., adaptive controller) is implemented for the smart lights, and this algorithm proves to be able to successfully adapt to uncertainties such as disturbances and failures.

Fuzzy model reference control with adaptation of input fuzzy sets

An improved adaptation mechanism to fuzzy model reference learning control (FMRLC) will be introduced in this paper. The main idea of the presented approach consists in including the controller input fuzzy sets into the adaptation process. In comparison with other FMRLC modifications the proposed method can be started with smaller number of input membership functions resulting in better reference signal tracking. Performance of the proposed procedure is demonstrated on control of a nonlinear laboratory system.

Fuzzy model reference control with adaptation mechanism

An improved approach to adaptation in fuzzy model reference learning control (FMRLC) will be introduced in this paper. The main idea of the presented method consists in including into adaptation process the input membership functions in the fuzzy controller. In comparison with original FMRLC algorithm the proposed method can be started with smaller number of input membership functions and reduces amount of penalization after few steps that results in convergent rule base and better and more reliable behavior of the closed loop that is shown on an simulation example of control of a nonlinear time-varying system.

Fuzzy Control of a Helio-Crane

In this paper we present a comparison of two fuzzy-control approaches that were developed for use on a non-linear single-input single-output (SISO) system. The first method is Fuzzy Model Reference Learning Control (FMRLC) with a modified adaptation mechanism that tunes the fuzzy inverse model. The basic idea of this method is based on shifting the output membership functions in the fuzzy controller and in the fuzzy inverse model. The second approach is a 2 degrees-of-freedom (2 DOF) control that is based on the Takagi-Sugeno fuzzy model. The T-S fuzzy model is obtained by identification of evolving fuzzy model and then used in the feed-forward and feedback parts of the controller. An error-model predictive-control approach is used for the design of the feedback loop. The controllers were compared on a non-linear second-order SISO system named the helio-crane. We compared the performance of the reference tracking in a simulation environment and on a real system. Both methods provided acceptable tracking performance during the simulation, but on the real system the 2 DOF FMPC gave better results than the FMRLC.

Adaptive control of a time-varying rotary servo system using a fuzzy model reference learning controller with variable adaptation gain

The constant parameters in a conventional fuzzy controller lead to a poor performance for time-varying systems. In this study, a fuzzy model reference learning controller (FMRLC) with a newly defined variable adaptation gain is designed and implemented in the adaptive fuzzy control of a time-varying rotary servo (TVRS) system. In the design of the FMRLC, a knowledge-base modification algorithm with variable adaptation gain is used instead of a fuzzy relation table. Hence, it is provided that the learning and adaptation mechanism continuously updates the knowledge base of the adaptive fuzzy controller against any parameter variations, such as changing loads. By means of the learning and adaptation mechanism, the TVRS system behaves as a defined reference model in the desired performance in time. The initial parameters of the FMRLC are easily determined by trial and error because of the variable adaptation gain. Using the designed controller, the adaptive fuzzy control of the TVRS system performs successfully in the simulation and practical implementation. The simulation of the system is executed in a MATLAB-Simulink environment and the practical application is implemented in a Quanser Q3 experimental servo module based on MATLAB-Simulink. The simulation and experimental results are given to demonstrate the effectiveness of the proposed control structure.

An advanced FMRL controller for FACTS devices to enhance dynamic performance of power systems

The parameters of power system slowly change with time due to environmental effects or may change rapidly due to faults. It is preferable that the control technique in this system possesses robustness for various fault conditions and disturbances. The used flexible alternating current transmission system (FACTS) in this paper is an advanced super-conducting magnetic energy storage (ASMES). Many control techniques that use ASMES to improve power system stability have been proposed. While fuzzy controller has proven its value in some applications, the researches applying fuzzy controller with ASMES have been actively reported. However, it is sometimes very difficult to specify the rule base for some plants, when the parameters change. To solve this problem, a fuzzy model reference learning controller (FMRLC) is proposed in this paper, which investigates multi-input multi-output FMRLC for time-variant nonlinear system. This control method provides the motivation for adaptive fuzzy control, where the focus is on the automatic online synthesis and tuning of fuzzy controller parameters (i.e., using online data to continually learn the fuzzy controller that will ensure that the performance objectives are met). Simulation results show that the proposed robust controller is able to work with nonlinear and nonstationary power system (i.e., single machine-infinite bus (SMIB) system), under various fault conditions and disturbances.

Fuzzy Model Reference Learning Control with Convergent Rule Base

Abstract New approach to adaptation ensuring convergence of the controller rule base in fuzzy model reference learning control (FMRLC) method will be introduced in this paper. The proposed method guarantees for a time-invariant plant that after few steps of adaptation the controller becomes fixed and needs not to be further tuned that results in faster response in comparison to the original FMRLC method. The main idea consists in using an additional information about the controlled system. The advantages of the proposed modification are presented on control of magnetic suspension system.

A fuzzy learning—Sliding mode controller for direct field-oriented induction machines

This paper presents a direct field-oriented induction motor drive system with two distinct features: a sliding mode voltage mode flux observer (SMVMFO) and a fuzzy model reference learning controller (FMRLC). The speed of the induction motor is regulated using an FMRLC, which does not require rigorous tuning of the controller's parameters. The rotor flux estimation for the direct field orientation (DFO) is realized by a newly proposed SMVMFO, which is insensitive to the stator resistance variation. The complete drive system is shown to have very good speed tracking performance, accurate flux estimation and field orientation, and high robustness to stator resistance variation, which has been a long-standing problem in the area of motor drives. Extensive simulations are presented for performance evaluation and validation of the proposed control scheme.

A new adaptive controller of facts-based FMRLC aimed at improving power system stability

Various control techniques using Advanced Super-conducting Magnetic Energy Storage (ASMES) aimed at improving power system stability have been proposed. As fuzzy controller has proved its value in some applications, the number of investigations employing fuzzy controller with ASMES has been greatly increased over recent period. Nevertheless, it is sometimes difficult to specify the rule base for some plants, or the need can arise for tuning the rule-base parameters if the plant changes. In order to solve such problems the Fuzzy Model Reference Learning Controller (FMRLC) is proposed. This paper investigates multi-inputs/multi-outputs FMRLC for time-variant nonlinear system. This provides the motivation for adaptive fuzzy control, whereby the focus is placed on the automatic on-line synthesis and tuning of fuzzy controller parameters (i.e., the use of on-line data for continuous learning of the fuzzy controller which ensures that the performance objectives are met). The simulation results show that the proposed robust controller is able to work with nonlinear power system (i.e., single machine connected at infinite bus), under various fault conditions and significant disturbances.