Model Reference Adaptive Fuzzy Control Research Articles

Model reference adaptive controller with interpretable fuzzy rules for linear MIMO systems

This paper presents a method for fuzzy model reference adaptive control system design for a partially known multi-input multi-output (MIMO) linear dynamic plant. Differential or convolutional equations can describe the plant to be controlled, and the fuzzy controller is assumed to be from the MIMO sector-bounded nonstationary nonlinearities class. The task is to obtain robust adaptive stabilization. Some absolute stability theorems and integral evaluations of the state and control vectors are applied in the MIMO fuzzy adaptive controller design procedure for the first time. A method for automatically obtaining a near-optimal nonlinear dynamic reference model is described. A practical step-by-step procedure of the MIMO adaptive fuzzy controller design is proposed, resulting in highly interpretable MIMO fuzzy control rules based on triangular fuzzy sets and strong triangular fuzzy partition. An example of the fuzzy controller design according to the proposed procedure is given.

RHYTHMS: Real-time Data-driven Human-machine Synchronization for Proactive Ergonomic Risk Mitigation in the Context of Industry 4.0 and Beyond

Human-machine work system (HMWS) represents a composite system that integrates machine precision and strength with human cognitive abilities, flexibility, and adaptability, aiming to achieve enhanced performance in various tasks aligned with a common goal, which is getting smarter in the era of Industry 4.0 (I4.0) and has received extensive attention from academia and industry. However, the lack of real-time information sharing during HMWS implementation and operation, coupled with uncertainties stemming from human instability and the complexities introduced by smart networking environments in the context of I4.0, poses challenges to the synchronous coordination of this distributed human-centric manufacturing system. Failure in synchronization not only compromises overall system integrity, leading to unrealistic decision-making and potential harm to human health but also goes against the principles of Industry 5.0 (I5.0), which proposes placing human well-being at the center of the production process for human-centric smart manufacturing in the future. In this context, this research presents real-time data-driven hyper objects orchestrating for the human-machine synchronization (RHYTHMS) based on a service-oriented human-to-machine architecture (SOH2M) and a model reference adaptive fuzzy control for real-time information sharing and synchronous coordination of the smart HMWS. Based on these, a real-life assembly case is carried out by a full-scale HMWS prototype to quantitatively analyze the benefits of the proposed approach in proactive ergonomic risk mitigation (PERM), contributing to the human-centric value-oriented I5.0 era.

Impedance Force Control of Manipulator Based on Variable Universe Fuzzy Control

Impedance control is a classic and straightforward control method that finds wide applications in various fields. However, traditional constant impedance control requires prior knowledge of the environment’s stiffness and position information. If the environmental information is unknown, constant impedance control is not capable of handling the task. To address this, this paper proposes a variable universe fuzzy model reference adaptive impedance control method that achieves effective force tracking even in the presence of unknown environmental information. A variable universe fuzzy controller was employed to determine the impedance parameters. The force tracking error and its rate of change were used as two input parameters for the variable universe fuzzy controller, which utilizes fuzzy inference to obtain the incremental values of the impedance parameters. For the introduced model reference controller, a novel adaptive law was employed to obtain the coefficients for contact force and torque. Subsequently, the contact force of the manipulator in Cartesian space was taken as the research object, and a simulation model of a six-joint manipulator was established in MATLAB/Simulink. By comparing it with the constant impedance control method, the feasibility and effectiveness of this control approach were validated.

Self-Regulating Adaptive Controller for Oxygen Support to Severe Respiratory Distress Patients and Human Respiratory System Modeling

Uncontrolled breathing is the most critical and challenging situation for a healthcare person to patients. It may be due to simple cough/cold/critical disease to severe respiratory infection of the patients and resulting directly impacts the lungs and damages the alveoli which leads to shortness of breath and also impairs the oxygen exchange. The prolonged respiratory failure in such patients may cause death. In this condition, supportive care of the patients by medicine and a controlled oxygen supply is only the emergency treatment. In this paper, as a part of emergency support, the intelligent set-point modulated fuzzy PI-based model reference adaptive controller (SFPIMRAC) is delineated to control the oxygen supply to uncomforted breathing or respiratory infected patients. The effectiveness of the model reference adaptive controller (MRAC) is enhanced by assimilating the worthiness of fuzzy-based tuning and set-point modulation strategies. Since then, different conventional and intelligent controllers have attempted to regulate the supply of oxygen to respiratory distress patients. To overcome the limitations of previous techniques, researchers created the set-point modulated fuzzy PI-based model reference adaptive controller, which can react instantly to changes in oxygen demand in patients. Nonlinear mathematical formulations of the respiratory system and the exchange of oxygen with time delay are modeled and simulated for study. The efficacy of the proposed SFPIMRAC is tested, with transport delay and set-point variations in the devised respiratory model.

Design and Implementation of a Four-Quadrant DC-DC Converter Based Adaptive Fuzzy Control for Electric Vehicle Application

Electric vehicles (EVs) provide an excellent opportunity for limiting the emission of a variety of environmentally hazardous gases caused by gasoline and diesel-based vehicles. These propelled vehicles require a forward and backward motion as well as a variable speed operation. Hence, the use of a four-quadrant (4-Q) direct current (DC) converter becomes a necessity. This paper aims to analyse the traction system of an electric automobile along with the improvement of energy efficiency. Inserting a bi-directional DC-DC converter between the battery and the four quadrant-DC chopper assembly allows the power flow from the battery to the motor and the other way around during regenerative braking. Therefore, increasing the limited driving range of the EV. This paper also focused on the application of model reference adaptive fuzzy control (MRAFC) in order to adjust the direct current bus voltage and the DC motor speed. The proposed system has been tested on an experimental bench and the results have been analysed.

Multivariable Model Reference Adaptive Control of an Industrial Power Boiler Using Recurrent RBFN

In industrial steam systems, the process requires a specific pressure, and the maximum permissible operating pressure is different. If the inlet steam pressure to the steam consuming equipment exceeds the operating pressure, it may cause hazards. Therefore, the more precise control of the boiler pressure is important. Since we are dealing with a nonlinear, time-varying, and multivariable system, the control method must be designed to handle this system well. Most of the methods proposed so far are either not physically feasible or the system has considered very simple. Therefore, in this paper, while modeling the boiler and its pressure relations more precisely, we will introduce a recurrent type-2 fuzzy RBFN-based model reference adaptive control system with various uncertainties so that the uncertainty and inaccuracy of the model can be compensated. The experimental results prove the efficiency of the proposed method in boiler control.

Multi-UAV cooperative target tracking via consensus-based guidance vector fields and fuzzy MRAC

Purpose This paper aims to propose a multi-agent approach to adaptive control of fixed-wing unmanned aerial vehicles (UAVs) tracking a moving ground target. The approach implies that the UAVs in a single group must maintain preset phase shift angles while rotating around the target so as to evaluate the target’s movement more accurately. Thus, the controls should ensure that the UAV swarm follows a moving circular path whose center is the target while also attaining and maintaining a circular formation of a specific geometric shape; and the formation control system is capable of self-tuning because the UAV dynamics is uncertain. Design/methodology/approach This paper considers two interaction architectures: an open-chain where each UAV only interacts with its neighbors; and a cooperative leader, where the leading UAV is involved in attaining the formation. The cooperative controllers are self-tuned by fuzzy model reference adaptive control (MRAC). Findings Using open-chain decentralized architecture allows to have an unlimited number of aircraft in a formation, which is in line with the swarm behavior concept. The approach was tested for efficiency and performance in various scenarios using complete nonlinear flying-wing UAV models equipped with configured standard autopilot models. Research limitations/implications Assume the target follows a rectilinear trajectory at a constant speed. The speed is supposed to be known in advance. Another assumption is that the weather is windless. Originality/value In contrast to known studies, this one uses Lyapunov guidance vector fields that are direction- and magnitude-nonuniform. The overall cooperative controller structure is based on a decentralized and centralized consensus.

Estimation of Rotor Temperature of Permanent Magnet Synchronous Motor Based on Model Reference Fuzzy Adaptive Control

For high-speed permanent magnet synchronous motor (PMSM), its efficiency is significantly affected by the performance of permanent magnets (PMs), and the phenomenon of demagnetization will occur with the increase of PM temperature. So, the temperature detection of PMs in rotor is very necessary for the safe operation of PMSM, and direct detection is difficult due to the rotation of rotor. Based on the relationship between permanent magnet flux linkage and its temperature, in this paper, a new temperature estimation method using model reference fuzzy adaptive control (MRFAC) is proposed to estimate PM temperature. In this method, the model reference adaptive system (MRAS) is built to estimate the permanent magnet flux linkage, and the fuzzy control method is introduced into MRAS, which is used to improve the accuracy and applicable speed range of parameters estimated by MRAS. Different permanent magnet flux linkages are estimated in MRFAC based on the variation of stator resistance, which corresponds to different working temperatures measured by thermal resistance, and the PM temperature will be obtained according to the estimated permanent magnet flux linkage. At last, the back electromotive force (BEMF) is measured on the experimental motor, and the flux linkage and PM temperature of the experimental motor are deduced according to the BEMF. Compared with the experimental results, the estimated PM temperature is very close to the actual test value, and the error is less than 5%, which verifies that the proposed method is suitable for the estimation of PM temperature.

English

English

Sliding Mode-Based Fuzzy Model Reference Adaptive Control Technique for an Unstable System

A comparative performance analysis of two adaptive control techniques for an unstable and disturbed system is presented. The first technique is model reference adaptive control (MRAC) that is based on Lyapunov theory, and second strategy is a combination of sliding mode (SM) and fuzzy logic theories and is known as sliding mode fuzzy model reference adaptive control (SM-FMRAC). In these schemes, asymptotic tracking of three reference models with different settling times and the different damping conditions is achieved. The expected advantage of the proposed SM-FMRAC is to improve the performance specifications over the MRAC technique.

Mobile Robot Control and Navigation: A Global Overview

The aim of this paper is to provide a global overview of mobile robot control and navigation methodologies developed over the last decades. Mobile robots have been a substantial contributor to the welfare of modern society over the years, including the industrial, service, medical, and socialization sectors. The paper starts with a list of books on autonomous mobile robots and an overview of survey papers that cover a wide range of decision, control and navigation areas. The organization of the material follows the structure of the author’s recent book on mobile robot control. Thus, the following aspects of wheeled mobile robots are considered: kinematic modeling, dynamic modeling, conventional control, affine model-based control, invariant manifold-based control, model reference adaptive control, sliding-mode control, fuzzy and neural control, vision-based control, path and motion planning, localization and mapping, and control and software architectures.

Application of FMRAC to fault‐tolerant cooperative control of a wind farm with decreased power generation due to blade erosion/debris buildup

SummaryWind energy has shown a remarkable potential for fulfilling the increasing world's energy demand in a clean and sustainable way. The wind energy industry has installed increasingly sophisticated and larger wind turbines particularly in offshore regions to capture such energy as efficiently and cost effectively as possible. The rapid growth in size and capacity of wind turbines together with harsh climate conditions and limited accessibility in offshore regions all result in higher failure rates and increased maintenance requirements and costs. Such difficulties motivate the use of advanced fault detection and diagnosis and fault‐tolerant control schemes in wind farms to improve their reliability and availability. Given the importance of this issue, this paper uses a fuzzy model reference adaptive control approach in a cooperative framework that is oriented to the design and development of a novel fault‐tolerant cooperative control scheme in a wind farm. This scheme handles decreased power generation faults in a wind farm caused by turbine blade erosion and debris buildup on the blades over time. The effectiveness and performance of the proposed scheme is demonstrated by a series of simulations on an advanced large offshore wind farm benchmark model in the presence of wind turbulence, measurement noise, load variations, and realistic fault scenarios.

Fuzzy Model Reference Adaptive Controller for DC Motor

In this work, a Fuzzy Model Reference Adaptive Controller FMRAC is presented for the speed control problem of a DC motor. The proposed controller is designed in two phases. In the first phase, the model reference input-output data is used to obtain the fuzzy rules. Then the effective rules are chosen to be used in the second phase. In the second phase, the obtained controller is applied in two conditions; the non fuzzy rules or adjusting the center of output membership functions. The simulation results shows a good speed motor tracking to the model reference in the word of the step response coefficients.

Passive Fault-tolerant Cooperative Control in an Offshore Wind Farm

The rapid growth in size and capacity of wind turbines together with harsh climate conditions and limited accessibility in offshore regions all result in higher failure rates and increased maintenance requirements and costs. Such difficulties motivate the use of advanced fault detection and diagnosis as well as fault-tolerant control schemes in wind farms to improve their reliability and availability. Given the importance of this issue, the current paper employs a fuzzy model reference adaptive control approach in a cooperative framework that is oriented to the design and development of a novel passive fault-tolerant cooperative control scheme in a wind farm. This scheme handles decreased power generation faults in a wind farm caused by turbine blade erosion and debris buildup on the blades over time. The effectiveness and performance of the proposed scheme is demonstrated by a series of simulations on an advanced large off-shore wind farm benchmark model.

A Model Reference Adaptive Control (MRAC) System for the Pneumatic Valve of the Bottle Washer in Beverages Using Simulink

One of the most important processes in beverage manufacturing companies is the bottle washing process. When the bottles are returned from the customers they are first cleaned in the bottle washer before they can be reused. The pneumatic valve of the bottle washing machine which controls the discharge of clean bottles sometimes sticks or fails which results in significant loss of production in the plant since all the other processes which follow depend on the bottle washing machine. The main causes of failure are temperatures and pressure outside the required ranges, and also minor contributions by moisture and abrasive particles. These parameters were controlled by designing a Fuzzy Logic Controller using MATLAB and it was found that pressure and temperature should be maintained between 0.5 - 2.99 bars and 50 - 85˚C, respectively, with moisture levels at 2.5% and dust levels at 0.5%. This is an artificial intelligent (AI) technique whereby the system will be able to plan, sense, reason, act and learn in a human intelligent way. Two solutions were developed to control the operation of the valve in cases of disturbances and non-linearity, namely; the Model Reference Adaptive Fuzzy Controller (MRAFC) and MRAC with PID controllers. Simulations were carried out and the MRAFC design showed excellent tracking results as compared to MRAC PID controller.

Brushless DC motor tracking control using self-tuning fuzzy PID control and model reference adaptive control

Abstract This paper compares the performance of two different control techniques applied to high performance brushless DC motor. The first scheme is self-tuning fuzzy PID controller and the second scheme is model reference adaptive control (MRAC) with PID compensator. The purpose of the control algorithm was to force the rotor speed to follow the desired reference speed with good accuracy all time. This objective should be achieved for different speed/time tracks regardless of load disturbance and parameter variations. The simulation results presented show that the second control scheme has better performance.

Adaptive Control of Active Magnetic Bearing against Milling Dynamics

For improving the defects in milling processes caused by traditional spindle bearings, e.g., the dimensional discrepancy of a finished workpiece due to bearing wear or oil pollution by lubricant, a novel embedded cylindrical-array magnetic actuator (ECAMA) is designed for milling applications. Since ECAMA is a non-contact type actuator, a control strategy named fuzzy model-reference adaptive control (FMRAC) is synthesized to account for the nonlinearities of milling dynamics and magnetic force. In order to ensure the superior performance of spindle position regulation, the employed models in FMRAC are all constructed by experiments. Based on the experimental results, the magnetic force by ECAMA is much stronger than that by the traditional active magnetic bearing (AMB) design under the same test conditions and identical overall size. The efficacy of ECAMA to suppress the spindle position deviation with the aid of FMRAC has been verified as well via numerical simulations and practical metal cutting.

Wind Turbine Fault Diagnosis and Fault-Tolerant Torque Load Control Against Actuator Faults

Wind turbines are designed to generate electrical energy as efficiently and reliably as possible. Advanced fault detection, diagnosis, and accommodation schemes are necessary to realize the required levels of reliability and availability in modern wind turbines. This paper presents two novel approaches oriented to the design of fault-tolerant control (FTC) schemes for reliable regulation of generator torque in a wind turbine that can be affected by both model uncertainties and actuator faults in its generator/converter. The first approach is based on fuzzy model reference adaptive control in which a fuzzy inference mechanism is used for parameter adaptation without any explicit knowledge of the potential faults in the system. The second approach exploits fuzzy modeling and identification method to develop an integrated model-based fault detection and diagnosis, and automatic signal correction mechanism to accommodate potential faults in the system based on online diagnostic information. Finally, the effectiveness of the proposed FTC schemes is illustrated and compared by a series of simulations on a well-known large offshore wind turbine benchmark in the presence of wind turbulences, measurement noises, and realistic fault scenarios in the generator/converter torque actuator.

Model reference adaptive fuzzy control of a shunt active power filter

In this paper, model reference adaptive fuzzy control (MRAFC) is proposed to improve the power dynamic performance such as current tracking and total harmonic distortion (THD) for a shunt active power filter (APF). T-S fuzzy strategy is utilized on the approximation of nonlinear model of APF caused by nonlinear characteristics, variable parameters and external disturbances. Adaptive fuzzy control of the APF system consists of T-S fuzzy control item and sliding mode control item, which can enhance the robust performance of the system. Simulation results confirm the effectiveness of the proposed controller and comparisons with APF system using model reference adaptive control (MRAC) demonstrate that MRAFC method is capable of reducing the modeling error and tracking reference current signal accurately. Moreover, APF using the proposed method shows the dynamic and static control performance of stronger robustness, better suitability and higher accuracy than the one using MRAC modus.

Direct Model Reference Adaptive Fuzzy Control of Networked SISO Nonlinear Systems

This study presents a novel direct model reference fuzzy controller as applied to the control of a nonlinear system over a network subject to variable network induced time delay. The proposed method uses Pade approximation to cope with this condition. Unlike most approaches seen in the literature, which are mostly model based and necessitate the solution of a set of linear matrix inequalities, the proposed approach is online and can be applied to nonlinear systems with a little knowledge about the structure of the system and the values of its parameters. The stability of the proposed method is proved using an appropriate Lyapunov function. The approach is implemented and tested on a dc motor with nonlinear characteristics and nonlinear state-dependent disturbance. It is shown that it is capable of controlling the system over a network subject to variable network-induced time delay with bounded tracking error. In addition, the effect of packet losses is considered in the implementation part and it is seen that the system can be controlled under these conditions too.