Fuzzy Supervisory Control Research Articles

Design of an Advanced Optimal Fuzzy Controller For a Binary Distillation Column

The most common control philosophy followed in the chemical process industries is the SISO system using the conventional PID controller algorithms. One drawback is relying on models for both control and design work in Chemical process industries (CPI) is that many problems are very complex and accurate models are difficult, if not impossible to obtain. To overcome these problems, it will be helpful to apply techniques that use human judgment and experience rather than precise mathematical models, which in the major cases deduced from the linearization of the system and simplification hypothesis. The fuzzy logic systems are capable of handling complex, nonlinear systems using simple solutions. However, obtaining an optimal set of fuzzy membership functions is not an easy task. In this chapter a solution based on artificial intelligence is proposed to improve the control of a binary distillation column. The solution is based on the use of the advantages of both fuzzy logic and genetic algorithms. The fuzzy logic is used as a supervisory PI controller that is a simple PI controller that generally used in controlling distillation columns with parameters deduced from the fuzzy supervisor. The membership functions shape is deduced by using research algorithms based on hierarchical genetic algorithms. The results show that the Fuzzy supervisory PI controller provide an excellent tracking toward set point change.

Fuzzy Partitioned Discrete-event System and Its Decentralized Supervisory Control

This work uses the idea of a fuzzy partitioned automaton to examine a fuzzy partitioned discrete-event system and associated decentralized supervisory control theory. We will specifically look at the notion of a fuzzy partitioned automaton and how it may be used to simulate the aforementioned system. In addition, we investigate the decentralized fuzzy supervisory control theory for a fuzzy partitioned discrete-event system and look at the conditions for its existence.

Integrated supervisory fuzzy control framework for biological wastewater treatment plants operation and their effect on GHG emissions

In this research, the effect on the greenhouse gases (GHG) emissions from biological wastewater treatment plants (WWTP) operation by implementing an integrated supervisory control framework on the bench-mark simulation model 2 gas (BSM2G) platform is studied. Ammonia based aeration control is considered for improving the efficiency of the nitrification process and also nitrate control is considered to enhance the performance of the overall plant. Different control strategies are proposed by considering PI control, Model Predictive control and Fuzzy controllers. A notable change is observed in GHG emissions due to bio-treatment because of the impact of control strategies on N2O emissions during denitrification with respect to the nitration where dissolved oxygen (DO) control alone is considered. In addition, when the nitrate loop is considered, a better change is observed in the overall plant performance. On comparison with other approaches, the proposed control strategies resulted in a reduction in the GHG emissions with marginal effect on the other performance indexes effluent quality index (EQI) and operational cost index (OCI).

A fast and structurally simple control scheme based on a fuzzy supervisory approach for wireless power transfer systems

AbstractWireless power transfer (WPT) is a promising technology and has attracted many researchers’ attention due to its inherent advantages such as reliability, safety, flexibility, and low maintenance costs. For WPT systems, the output voltage varies according to the variable loads and the coupling coefficient conditions, affecting system stability and efficiency. In this regard, this study is concerned with the tracking control problem of WPT systems based on fuzzy supervisory proportional–integral (PI) controller and a phase shift modulation technique, which is for the first time proposed by this paper. By introducing appropriate fuzzy rules, the proposed control scheme not only can track the desired output voltage but also has better robust behaviour to deal with the problem of variations of circuit elements and coupling coefficients. That is, the proposed fuzzy supervisory PI control scheme can perform better than the traditional PI controller. Analysis of the designed control method and simulation results validate the superiority of the proposed method over the traditional PI controller in all conditions, which can make it a suitable choice for the applications of electric vehicle charging systems. The analysis is supported by experimental measurements.

Design of supervisory fuzzy control for enhanced energy saving in a sequencing batch reactor-based wastewater treatment plant

Design of supervisory fuzzy control for enhanced energy saving in a sequencing batch reactor-based wastewater treatment plant

Fuzzy predictive Stanley lateral controller with adaptive prediction horizon

The challenge of trajectory tracking of autonomous vehicles (AVs) is a critical aspect that must be effectively addressed. Recent studies are concerned with maintaining the yaw stability to guarantee the customers’ comfort throughout the journey. Most of the geometrical controllers solve this task by dividing it into consecutive point stabilization problems, limiting the controllers’ ability to handle sudden trajectory changes. One research presented a predictive Stanley lateral controller that uses a discrete prediction model to mimic human behavior by anticipating the vehicle’s future states. That controller is limited in its use, as the parameters must be manually tuned for every change in the maneuver or vehicle velocity. This article presents a novel approach for trajectory tracking in autonomous vehicles, by introducing a fuzzy supervisory controller that automatically adapts to changes in the vehicle’s velocity and maneuver by estimating the prediction horizon’s length and providing different weights for each controller. The proposed method overcomes the limitations of traditional controllers that require manual tuning of parameters, making it ready for real-world experiments. This is the main contribution of the research in this paper. The suggested technique demonstrated an advantage over the Basic Stanley controller and the manually tuned predictive Stanley controller in terms of the total lateral error and the model predictive control (MPC) in terms of computational time. The performance is determined by performing various simulations on V-Rep and hardware-in-the-loop (HIL) experiments on an E-CAR golf bus. A broad selection of velocities is used to validate the behavior of the vehicle while working on different maneuvers (double lane change, hook road, S road, and curved road).

Battery-Supercapacitor Current Control Using Fuzzy Supervisory with PI Controller for Electric Vehicle Application

The configuration of the battery-supercapacitor as a hybrid power source (HPS) is one solution to overcome the limitations of battery life, such as in an electric vehicle. To achieve battery energy savings when a supercapacitor is added, a control scheme is needed. This paper proposes a battery-supercapacitor current sharing control scheme using Fuzzy Supervisory Control (FSC). The FSC is designed to determine how much current of the battery and supercapacitor need to discharge based on the current requirement of the load. The output of the FSC will produce a reference current which is then controlled in the main closed-loop control using a Proportional-Integral (PI) controller. The control signal in the form of a duty cycle is then used to regulate the battery-supercapacitor current through a DC/DC converter. This control scheme is tested using a resistive load change scenario both simulation and experimental. The results show that the battery-supercapacitor current can be controlled according to the loading conditions.

Energy-based fuzzy supervisory non integer control for performance improvement of PMSG-Based marine energy system under swell effect and parameter uncertainties

Permanent magnet synchronous generators (PMSG) based marine current system has gained much attention due to several advantages such as high predictability, rapid response and high-power density. The performance and power integration efficiency of the above-mentioned system greatly rely on the applied control system. However, system nonlinearities, parameter variations make the controller design process a challenging task. This paper introduces passivity based fuzzy fractional order proportional integral and derivative (PID) controller for PMSG based marine current energy conversion system under swell effect and parameter uncertainties. The proposed approach combines a new passivity-based current control (PBCC) with a fuzzy fractional order PID control (FFO-PID). The proposed method is tested under the swell effect and parameter uncertainties using MATLAB/Simulink. The proposed controller exhibits robustness against swell effect, parameter changes and shows fast convergence of the system states. Furthermore, the exponential stability and error convergence of the proposed controller is analytically justified.

A supervisory fuzzy logic control scheme to improve effluent quality of a wastewater treatment plant.

The application of control strategies in wastewater treatment plants has increased to improve their performance for treating influent. The fuzzy logic controller plays a vital role in this work and simulation work was carried out in a benchmark simulation model no.1 (BSM1) framework. The attempted work proposes two control schemes with the objectives of improving the effluent quality and minimizing the number of measurements taken from the plant. The design of fuzzy control schemes is based on five inputs and six outputs in order to accomplish the objectives. Experimental results show improvement in the effluent quality and increase in the efficacy of the control system. The proposed design is implemented using MATLAB with the adaptation in 2014a.

Control of an anaerobic bioreactor using a fuzzy supervisory controller

In the present work, a fuzzy supervisory control approach combined with an adaptive model predictive controller (AMPC), has been proposed to maximize the productivity of an anaerobic digestion (AD) process, while keeping the operation stable. In the proposed hierarchal control strategy, the set-point of the inner loop is provided by a supervisory controller. In the inner loop an AMPC has been applied to achieve the desired methane production rate by manipulating the feed flow rate. The AMPC is designed based on the auto-regressive moving average (ARMA) model whose parameters are updated at each sampling time to make the controller more robust against uncertainties and external loads. In the supervisory level, a fuzzy logic system is utilized to adjust the set-point of the lower level controller based on the measurement of total concentration of the volatile fatty acids (VFA). To prevent VFA accumulation, an override control strategy is used to bring the total VFA concentration to its safe level when it exceeds a predetermined level. To simulate the AD process, the well-established Anaerobic Digestion Model No.1 (ADM1) has been used. Through simulation study, the performance of the proposed control strategy has been evaluated. Simulation results indicate that the proposed control strategy could maximize the methane production rate in presence of different external disturbances and model mismatch.

Fuzzy Supervisory Passivity-Based High Order-Sliding Mode Control Approach for Tidal Turbine-Based Permanent Magnet Synchronous Generator Conversion System

Higher efficiency, predictability, and high-power density are the main advantages of a permanent magnet synchronous generator (PMSG)-based hydro turbine. However, the control of a PMSG is a nontrivial issue, because of its time-varying parameters and nonlinear dynamics. This paper suggests a novel optimal fuzzy supervisor passivity-based high order sliding-mode controller to address problems faced by conventional techniques such as PI controls in the machine side. An inherent advantage of the proposed method is that the nonlinear terms are not canceled but compensated in a damped way. The proposed controller consists of two main parts: the fuzzy gain supervisor-PI controller to design the desired dynamic of the system by controlling the rotor speed, and the fuzzy gain-high order sliding-mode control to compute the controller law. The main objectives are feeding the electrical grid with active power, extracting the maximum tidal power, and regulating the reactive power and DC voltage toward their references, whatever the disturbances caused by the PMSG. The main contribution and novelty of the present work consists in the new robust fuzzy supervisory passivity-based high order sliding-mode controller, which treats the mechanical characteristics of the PMSG as a passive disturbance when designing the controller and compensates it. By doing so, the PMSG tracks the optimal speed, contrary to other controls which only take into account the electrical part. The combined high order sliding-mode controller (HSMC) and passivity-based control (PBC) resulted in a hybrid controller law which attempts to greatly enhance the robustness of the proposed approach regardless of various uncertainties. Moreover, the proposed controller was also validated using a processor in the loop (PIL) experiment using Texas Instruments (TI) Launchpad. The control strategy was tested under parameter variations and its performances were compared to the nonlinear control methods. High robustness and high efficiency were clearly illustrated by the proposed new strategy over compared methods under parameter uncertainties using MATLAB/Simulink and a PIL testing platform.

Loop Control and Tuning in Distributed Control System Using Fuzzy Controller (Dept.E)

In this paper, the designed schemes for two fuzzey controllers, employing the scaling factor tuning, are p roposed. The first fuzzy logic controller is a normalized controller used to control the system. The tuning for the input and output-scaling factors of the first one is done through (le second fuzzy controller (the supervisory controller). This combination is used to ap propriately determine the control signal of the process. The supervisory fuzzy controller tunes the normalized fuzzy controller based on the model reference adaptive control technique. The great advantage of the proposed method is that, a supervisor as a fuzzy controller to tune the scaling factor of a normalized fuzzy controller can be used to supervise many control systems. The simplicity and modular structure of the controller makes it is more suitable to be applied to control most control loops in the distributed control systems (DCS). The normalized fuzzy controller and the supervisory fuzzy controller are organized with specific experience information about the controlled systems. The proposed (fuzzy controllers are applied experimentally to control an experimental p rocess, which simulate an LPG p rocess. The proposed controller is applied to two different control 100ps, temperature and level, where the controller gains are selected based on the process conditions and limitations. A comparison among scaling factor manual tuning, supervisor fuzzy and conventional adaptive fuzzy controller is done to verify the effectiveness of the proposed design. The results show that in the last case the system is forced to follow the desired response.

Particle Swarm Optimization for tuning a Fuzzy Supervisory Controller Parameters (Takagi Seguno and Mamdani Engines)

Steam generation systems are a crucial part of most power plants. Therefore, boiler control is an important problem for power plants that are frequently changing load or subject to sudden load disturbances, which are common in current market driven electricity industry. Hence, the control of such processes needs the design of more effective controllers. Although Fuzzy logic controllers seem more adequate for controlling chemical processes, since they provides solutions to incompletely defined and nonlinear processes industrials does not prefer such type of controllers since the tuning of these controllers requires the adjustment of a large number of parameters, which is tedious. The fact that let the industrials prefer the use of simple PID controllers rather than complicated fuzzy or neural controllers. Hence in this paper a simple PD controller is used to control the steam flow parameters of whereas its parameters are deduced using fuzzy supervisory controller the parameters of the supervisory controller are optimized using particle swarm optimization algorithm, the most known inference Engines SEGUNO and MAMDANI are considered.

Energy and exergy co-optimization of IGCC with lower emissions based on fuzzy supervisory predictive control

This paper presents an energy and exergy co-optimization method of integrated gasification combined cycle (IGCC) based on Fuzzy Supervisory Predictive Control (FSPC). Firstly, a green IGCC process is proposed which contains three principle couplings: air separation unit (ASU), heat recovery steam generator (HRSG) and CO2 capture/storage unit (CCS). From law of thermodynamics, using substance thermophysical parameters, the energy efficiency and exergy efficiency of IGCC are successively defined. The IGCC power station has features such as closed coupling, large time lag and non-linearity, however, faster response speed and lower overshoot are always the unremitting pursuits. Therefore, the Fuzzy Supervisory Predictive Control (FSPC) method is proposed to implement robust control under complex disturbances by pre-considering unmeasurable disturbance and measurable disturbance. The fuzzy rules extracted from historical bigdata are employed in supervisory layer to make the precise control decisions. Finally, the energy and exergy co-optimization model is built and solved for higher efficiency and economic effectiveness. Taking the large-scale (300MW) IGCC for example, after using FSPC, the efficiency of water recovery is increased from 40.7% to 62.1% with the ratio of 52.6% because of waste water recovery (WWR) system. The net efficiency of proposed IGCC system is increased from 37.6% to 41.7% with the ratio of 10.9%. The exergy efficiency of IGCC system is increased from 36.5% to 39.2% with the ratio of 7.4%. The proposed method has great significance for the energy-saving and Near-zero emissions (NZEC) IGCC with high safety and robust control under supercritical (SC) or ultra-super critical (USC) state.

Multi-Mode Switching Control of the EPS With Hybrid Power Supply

Electric power steering system (EPS) with traditional power supply can't provide enough power for heavy-duty vehicle to turn at low speed. To solve the problem, a novel EPS with hybrid power supply was constructed. In this paper, firstly, mathematical model of the EPS with hybrid power supply was established including model of hybrid power supply system and basic model of EPS. Then the power mode recognition was obtained according to the certain threshold of steering resistance torque, state of charge (SOC) of super-capacitor and output voltage of the vehicle power supply, and the corresponding finite automaton model was built. The EPS system with hybrid power supply was regarded as a hybrid system due to continuous event in a single mode and discrete event during mode switching process, thus the multi-mode switching control strategy was proposed. Taken the different characteristics of hybrid power supply in different power modes into account, the corresponding local controllers were designed which were consisted of fuzzy-PID controller, active disturbance rejection controller (ADRC) and sliding mode controller. Besides, the fuzzy supervisory controller was also designed to ensure the stability of the hybrid system during mode switching process. The simulation results show that the local controllers can achieve fast and accurate track of target current in each power mode, and the fuzzy supervisor can effectively ensure the stability of the switching process, which demonstrates the feasibility of the control strategy.

Indirect adaptive fuzzy control of non-linear systems using fuzzy supervisory term

This paper focuses on the indirect adaptive fuzzy control of Single Input Single Output (SISO) non-linear systems with unknown linearities. The proposed adaptive fuzzy controller is based on feedback linearisation. Its parameters are updated online according to some adaptive laws such as tracking error-based method, composite tracking and modelling error-based approach as well as filtered composite tracking and modelling error-based approach. However, the approximation error introduced into feedback loop increases the difficulty to guarantee the stability of the closed-loop control system. To solve this problem a supervisory term should be added to the control law. In this paper, a fuzzy supervisory control is proposed to overcome the problem of chattering phenomena introduced by the classical supervisory term. Theoretical and simulation results prove the effectiveness of the proposed approaches. Faster and improved tracking is obtained by using second and third aforementioned adaptive laws combined with fuzzy supervisory control.

Supervisory control using fuzzy logic for fault ride-through capability of a hybrid system in grid supporting mode

This paper demonstrates a novel fuzzy-supervisory control algorithm for power extraction and management in a grid-tied low concentration photovoltaic (LCPV) system with battery energy storage system (BESS). The novelty of this paper lies in avoiding dump load (DL) at the dc link, to regulate dc link voltage (Vdc) under all conditions, with an increase in power dispatch. This increases system efficiency and lessen power extraction from BESS. The supervisory controller directs all converters and maintains power balance at the dc link. Enhanced reactive power (Q) support of the grid inverter during a grid fault is controlled by using the magnitude of grid voltage sag. This control prevents islanding of the dc micro-grid as per the Indian grid code. Variation in duty cycle of the converter for optimum power extraction from LCPV generator is done by using fuzzy logic. Power references of converters depend on different operational modes and grid condition.

Design of a supervisory fuzzy logic controller for monitoring the inflow and purging of gas through lift bags for a safe and viable salvaging operation

This paper presents a mathematical model and numerical time-domain approach to simulate the dynamics of a sunken ship/vessel being raised from seafloor by buoyancy (gas-inflating) systems in a form which is suitable for integrating control techniques to ensure hydrodynamic stability for a safe and viable salvaging operation. According to the two-degree-of-freedom equations of rigid-body vessel motion in diving plane, a conventional sliding mode controller is designed as the primary controller to regulate flow rate of filling gas inside the lift bags and a PID controller is designed as the secondary controller for regulating the purging of gas through the valves fitted on lift bags. Then a supervisory fuzzy logic controller is designed to monitor or switch between the primary and secondary controllers based on the buoyancy requirement. From the simulation studies, it is found that the supervisory fuzzy logic controller is capable to maintain hydrodynamic stability by suitably defining the linguistic fuzzy rules, which is created based on the author's experience in conducting numerical simulation using primary and secondary controllers.

A supervisory on-line tuned fuzzy logic based sliding mode control for robotics: An application to surgical robots

Ever since digital technology entered in the operating theater (OT) surgery has moved out through one of the big transformation on account of medical world and now we are foreseeing the era of digital OT. Robotic assisted surgery (RAS) is a way of technological development in the medical environment that uses robotic structure to assist in surgical measures. RAS was designed to beat the limitations of Minimal Invasive Surgery (MIS) and to improve the ability of medical doctor during surgery. The paper depicts the control methodology for surgical robots based on the combination of fuzzy logic control (FLC) with sliding mode control (SMC). The appreciable features of SMC like simplicity in design and high degree of robustness motivate researchers to employ this methodology in robotics. Nevertheless, the destructive chattering phenomenon is circumvented by espousing FLC in SMC. Additionally, the output gain of fuzzy sliding mode control (FSMC) is online tuned by a supervisory fuzzy logic control (SFLC), which results in chattering diminution. System stability is investigated using Lyapunov theorem. All numerical simulations have been carried out using MATLAB tool for 2 DOF surgical robot manipulator formulated for high speed trajectory tracking and for the typical condition during surgery. Moreover, the performance has been validated in real-time using Opal-RT Lab simulator show the efficacy of the proposed methodology. The simulation as well as real time digital simulator (OP-4500) results of FSMC and supervisory fuzzy logic based sliding mode control (SFSMC) are compared with the conventional SMC which represents the improvement of control law for attainment of optimized results by rejecting perturbations and achieving the desired system performance within a specific band of operation.

A plant-scale validated MATLAB-based fuzzy expert system to control SAG mill circuits

This article presents the basis of a supervisory fuzzy expert controller for semi-autogenous grinding mill circuits. Stable feeding regimen to the mill, enhanced throughout, energy saving and human operator training are the most important objectives of this advanced control system. The fuzzy system calculates optimum set points for plant distributed control loops, causing them to tune semi-autogenous grinding mill performance to new operating set points. Although leading companies have their own commercial control packages, this supervisory controller is coded in MATLAB® and is able to connect to plant lower level controller. The controller has been tested and verified in Sungun copper concentrator semi-autogenous grinding circuit. Results proved the ability of proposed supervisory control system to increase the throughput of the mill by 3.26% and decrease the specific energy consumption by 6.29% at the same time. On the other hand, smooth set points calculated by the fuzzy control system, decrease the fluctuations in mill operation which finally results in a more stable operation of the grinding circuit.