Fuzzy Supervisory Control Research Articles

Improved Image Based Visual Servoing with Parallel Robot

The position regulation problem of an eye-in-hand type of parallel robot based pointing systems (PRBPS) is considered in this paper. A fuzzy logic system is first designed to compensate for the uncertainties of the parallel robot and the uncertainty of the image Jacobian, then a hybrid controller (HC) including the image-based nonlinear controller and the adaptive supervisory fuzzy logic controller (ASFLC) is derived by using the Lyapunov direct method to realize the position regulation (PR). The stability of the closed-loop system in the Lyapunov sense is proven theoretically. The fuzzy scaling matrix is combined with the HC to improve the performance of the control system. The simulation results demonstrate that the PRBPS realizes PR with very good robustness to the parameter uncertainties, and the control input torques and settling time are reduce greatly in the case of large initial feature errors.

Experimental Studies on Pressure Control Using Intelligent PI Fuzzy Supervisory Approach

Fuzzy supervisory control scheme for regulating the tank pressure in a pilot pressure control system has been presented. The controller structure has been designed in such a way as to adjust on-line the parameters of a classical PI controller that will reduce the human involvement in tuning the controller parameters at different operating conditions. The control algorithm for the proposed scheme has been developed using KEIL software and then converted into an executable file before downloading it to an ARM7 (AT91M55800A) based embedded target board. The pipeline execution and RISC architecture of ARM7 target board enhances the computation speed in real time control applications. Experimental study has been carried out to test the performance of the proposed supervisory fuzzy controller and the results were compared with classical control method. Better control performance using supervisory fuzzy controller has been obtained.

Fuzzy Predictive Supervisory Control Based on Genetic Algorithms for Gas Turbines of Combined Cycle Power Plants

This work presents a novel design and development of a fuzzy predictive supervisory controller, based on genetic algorithms (GA), for gas turbines of combined cycle units. The control design is based on an objective function that represents the economic and regulatory performance of a gas turbine by using a dynamic optimal set-point for the regulatory level. A fuzzy model is considered in order to characterize the nonlinear behavior of the gas turbine, which is used in two supervisory control systems. The first fuzzy supervisory control design includes a fuzzy model, where its parameters are held constant for the successive predictions. For the second fuzzy supervisory control design, its parameters are updated in each prediction and its nonlinear optimization problem is solved using GAs. The proposed fuzzy supervisory controllers are compared against a supervisory controller based on linear models and a regulatory controller with constant optimal set-points. Results indicate that the fuzzy GA predictive supervisory controller captures adequately the nonlinearities of the process, which, in turn, provides a promising approach to improve the performance of the combined cycle unit.

FUZZY SUPERVISORY CONTROL OF A DSP‐BASED MAGNETIC LEVITATION SYSTEM

ABSTRACTThis paper proposes a fuzzy supervisory control of a magnetic levitation system which the eZdsp F2812 is utilized as a controller. The discrete‐time model of the magnetic levitation system is derived and the stability is guaranteed by the root locus methodology. A fuzzy supervisory control is used to compensate for system nonlinearities.

Modeling and Simulation of a Fuzzy Supervisory Controller for an Industrial Boiler

In this paper we compare and discuss the performance of a boiler evaporator system when the system is controlled by a traditional PID-type strategy and when the system is enhanced by using fuzzy logic blocks to provide set-points for the system. The strategy used in fuzzy logic controllers (FLCs) is called fuzzy supervisory control and it generates set-points for the conventional controllers. The boiler under test is a VU-60 industrial system that produces 180,000 pounds of steam per hour. The mathematical model of the plant is a scaled version model of that obtained for a thermoelectric unit. The new model simplifies the large-scale thermoelectric boiler model to an industrial small-scale type VU-60 boiler model based upon first principle mass and energy balance equations. The main change consists of representing only the behavior of the drum—evaporator system, having a partial model of the combustion process, with a simplified combustion control system and a three-element boiler feed-water controller. The control system for combustion and boiler feed-water receives a supervisory signal (or set-point tracking signal) that comes from the FLC to improve the performance of the overall control system. The behavior of the supervisory controller brings some advantages to the system performance, compared with the traditional control schemes. The comparison reflects fuel improvements from 2.5% to 6.5% depending upon the steam load ramp regime. The simulations are performed using the SIMULINK® shell running under the MATLAB ® platform.

GA-fuzzy control of smart base isolated benchmark building using supervisory control technique

The effectiveness of a supervisory fuzzy control technique for reduction of seismic response of a smart base isolation system is investigated in this study. To this end, a first generation, base isolated, benchmark building is employed for numerical simulation. The benchmark structure under consideration has eight stories and an irregular plan. Furthermore it is equipped with low damping elastomeric bearings and magnetorheological (MR) dampers for seismic protection. The proposed control technique employs a hierarchical structure of fuzzy logic controllers (FLC) consisting of two lower-level controllers (sub-FLC) and a higher-level supervisory controller. One sub-FLC has been optimized for near-fault earthquakes and the other sub-FLC is well-suited for far-fault earthquakes. These sub-FLCs are optimized by use of a multi-objective genetic algorithm. Four objectives, i.e. reduction of peak superstructure acceleration, peak isolation system deformation, RMS superstructure acceleration and RMS isolation system deformation are used in a multi-objective optimization process. When an earthquake is applied to the benchmark building, each of the sub-FLCs provides different command voltages for the semi-active controllers and the supervisory fuzzy controller appropriately combines the two command voltages based on a fuzzy inference system in real time. Results from numerical simulations demonstrate that isolation system deformation as well as superstructure responses can be effectively reduced using the proposed supervisory fuzzy control technique in comparison with a sample clipped optimal controller.

Robust Indirect Adaptive Fuzzy Controller for Balancing and Position Control of Inverted Pendulum System

In the paper a robust indirect adaptive fuzzy controller is proposed for balancing and position control of the inverted pendulum system. Because balancing control rules of the pendulum and position control rules of the cart can be opposite, it is difficult to design an adaptive fuzzy controller that satisfy both objectives. To stabilize the pendulum at a specified position, the proposed fuzzy controller consists of a robust indirect adaptive fuzzy controller for balancing and a supervisory fuzzy controller which emulates heuristic control strategy and arbitrate two control objectives. It is proved that the signals in the overall system are bounded. Simulation results are given to verify the proposed adaptive fuzzy control method.

Fuzzy supervisory sliding-mode and neural-network control for robotic manipulators

Highly nonlinear, highly coupled, and time-varying robotic manipulators suffer from structured and unstructured uncertainties. Sliding-mode control (SMC) is effective in overcoming uncertainties and has a fast transient response, while the control effort is discontinuous and creates chattering. The neural network has an inherent ability to learn and approximate a nonlinear function to arbitrary accuracy, which is used in the controllers to model complex processes and compensate for unstructured uncertainties. However, the unavoidable learning procedure degrades its transient performance in the presence of disturbance. A novel approach is presented to overcome their demerits and take advantage of their attractive features of robust and intelligent control. The proposed control scheme combines the SMC and the neural-network control (NNC) with different weights, which are determined by a fuzzy supervisory controller. This novel scheme is named fuzzy supervisory sliding-mode and neural-network control (FSSNC). The convergence and stability of the proposed control system are proved by using Lyapunov's direct method. Simulations for different situations demonstrate its robustness with satisfactory performance.

Supervisory fuzzy control of a base-isolated benchmark building utilizing a neuro-fuzzy model of controllable fluid viscous dampers

A numerical study demonstrating the effectiveness of a supervisory fuzzy controller for seismic protection of a base-isolated structure is described in this paper. The numerical simulations were conducted using the first-generation base isolation benchmark problem. This benchmark problem focuses on the three-dimensional seismic response of an eight-storey base-isolated building wherein lateral–torsional response of the plan irregular structure is explicitly modeled. The isolation system consists of low-damping elastomeric bearings and semi-active controllable fluid viscous dampers. The dynamics of the controllable fluid viscous dampers are accounted for via a neuro-fuzzy model that is developed based on experimental data from testing of small-scale controllable fluid viscous dampers. Results from the simulations demonstrate that accounting for the damper dynamics is important in assessing the realizable improvements in seismic response reduction. Furthermore, the results show that simultaneous reductions in peak isolation system deformation and peak superstructure responses can be achieved for several near-field earthquake ground motions, particularly with respect to the semi-active clipped optimal sample controller provided in the benchmark problem statement Copyright © 2005 John Wiley & Sons, Ltd.

퍼지관리제어기법의 강인성능평가

This paper describes the robust performance of fuzzy supervisory control (FSC) approach for the seismic response control of cable-stayed bridges. FSC is a hybrid control method with the hierarchical structure of several sub-controllers and fuzzy supervisor. Each sub-controller is preliminarily designed to reduce the selected response of the structure only, and a fuzzy supervisor is introduced to improve the overall control performance by modulating the predesigned static gains into time-varying dynamic gains. To demonstrate the robust performance of the proposed strategy, the FSC and linear quadratic Gaussian (LQG) controller are optimally designed and their seismic performances are compared according to the variation of the bridge stiffness in the phase I benchmark control problem. The comparative results show that the FSC system can clearly guarantee the robust performance against the uncertainties of the bridge model.

퍼지관리제어기법을 이용한 스마트 면진 벤치마크 건물의 제어

본 논문에서는 스마트 면진장치를 효과적으로 제어하기 위하여 퍼지관리제어기를 개발하였고 그 효율성을 검토하였다. 이를 위하여 1세대 스마트 면진 벤치마크 건물을 이용하여 수치해석을 수행하였다. 대상 벤치마크 구조물은 부정형의 평면을 가지고 있는 8층 건물이고 탄성베어링과 MR 감쇠기로 이루어진 스마트 면진장치가 설치되어 있다. 본 논문에서는 다목적 유전자 알고리즘을 이용하여 원거리 지진과 근거리 지진에 대하여 각각 면진구조물을 효과적으로 제어할 수 있는 하위 퍼지제어기를 개발한다. 최적화과정에서는 구조물의 최대 및 RMS 가속도와 면진층 변위의 저감이 목적으로 사용된다. 벤지마크 건물에 지진하중이 가해지면 두 개의 하위 퍼지제어기에서는 각각 다른 명령전압이 제공되는데 이 명령전압들은 퍼지관리제어기의 추론과정에 기반하여 실시간으로 참여율이 조절되어 하나의 명령전압으로 조합된다. 수치해석을 통하여 제안된 퍼지관리제어기법을 사용함으로써 상부구조물의 응답과 면진층의 변위를 효과적으로 줄일 수 있음을 확인할 수 있다. The effectiveness of fuzzy supervisory control technique for the control of seismic responses of smart base isolation system is investigated in this study. To this end, first generation base isolated building benchmark problem is employed for the numerical simulation. The benchmark structure under consideration is an eight-story base isolated building having irregular plan and is equipped with low-damping elastometric bearings and magnetorheological (MR) dampers for seismic protection. Lower level fuzzy logic controllers (FLC) for far-fault or near-fault earthquakes are developed in order to effectively control base isolated building using multi-objective genetic algorithm. Four objectives, i.e. reduction of peak structural acceleration, peak base drift, RMS structural acceleration and RMS base drift, are used in multi-objective optimization process. When earthquakes are applied to benchmark building, each of low level FLCs provides different command voltage and supervisory fuzzy controller combines two command voltages io one based on fuzzy inference system in real time. Results from the numerical simulations demonstrate that base drift as well as superstructure responses can be effectively reduced using the proposed supervisory fuzzy control technique.

Fuzzy supervisory control of earthquake-excited cable-stayed bridges

A fuzzy supervisory control (FSC) technique is proposed for the seismic response control of cable-stayed bridges. The proposed technique is a hybrid control method, which adopts a hierarchical structure consisting of several sub-controllers and a fuzzy supervisor. The sub-controllers are independently designed in order to reduce the selected responses of a cable-stayed bridge and a fuzzy supervisor is introduced to modulate the pre-designed static gains into time-varying dynamic gains to improve seismic performance. In the design of the sub-controllers, shear forces and bending moments at the base of the towers, longitudinal displacements at the top of the towers, relative displacements between the deck and the tower, and tensions in the stay cables were selected as control variables. To demonstrate the effectiveness of the proposed strategy, example designs and numerical simulations were performed using a benchmark cable-stayed bridge subject to three historically recorded earthquakes. For comparison of the seismic performance, an optimally designed LQG system is also simulated for the benchmark bridge. The simulated results show that the FSC system can efficiently reduce the seismic responses of the cable-stayed bridge while maintaining lower control efforts.

퍼지관리제어기법을 이용한 사장교의 지진응답제어

사장교의 지진응답 제어를 위한 퍼지관리제어기법에 대하여 연구하였다. 제시하는 방법은 복합제어방법의 하나로서, 여러 개의 최적제어기로 이루어진 하부제어기와 퍼지관리자로 구성되는 계층적인 구조를 가진다. 하위제어기들은 사장교의 주요 지진응답들을 저감시키도록 각각 독립적으로 설계되며, 퍼지관리자는 설계된 하위제어기들의 참여율을 조절함으로써 향상된 제진성능을 확보한다. 이는 하위제어기의 정적 제어이득을 퍼지추론과정에 기반하여 실시간으로 변화하는 동적 제어이득으로 변화함으로써 이루어진다. 제안하는 방법으리 적용성을 평가하기 위하여 Dyke 등이 제안한 사장교의 벤치마크 제어문제를 설계 예로 고려하였으며, 사장교 지진응답제어를 위한 제어변수로는 주탑하부의 전단력과 휨모멘트, 주탑상부의 수평변위 및 테크 주탑간 상대변위, 그리고 케이블의 장력을 선정하였다. 벤치마크 사장교에 대하여 제안한 퍼지관리제어기 및 최적제어이론에 기반한 LQG 제어기의 제어성능 비교로부터 제시하는 기법의 효율성을 검증하였다. Fuzzy supervisory control technique for the seismic response control of cable-stayed bridges subject to earthquakes is studied. The proposed technique is a hybrid control method, which adopts a hierarchical structure consisting of several sub-controllers and a fuzzy supervisor. Sub-controllers are independently designed to reduced the responses to be controlled of a cable-stayed bridge, and a fuzzy supervisor achieves improved seismic control performance by tuning the pre-designed sub-controllers. It is realized by converting static gains of the sub-controllers into time-varying dynamic gains through the fuzzy inference mechanism. To evaluate the feasibility of the proposed technique, the benchmark control problem of cable-stayed bridge proposed by Dyke et al. is adopted. The control variables for the seismic response control of the cable-stayed bridge are determined to be t도 shear forces and bending moments at the base of the towers, the longitudinal displacements at the top of the towers, the relative displacements between the deck and the tower, and the tensions in the stay cables. Comparative results between the fuzzy supervisory controller and LQG controller demonstrate the effectiveness of the proposed control technique.

Fuzzy Supervisory Control of Manufacturing Systems

A supervisory controller is derived for scheduling (single/multiple-part-type, reentrant) production networks. The supervisory controller is used to tune a set of lower level distributed fuzzy control modules that reduce work-in-process (WIP) and synchronize the production system's operation. The overall production-control system is viewed as a two-level surplus-based system with the overall control objective to keep the WIP and cycle time as low as possible, while maintaining quality of service by keeping backlog to acceptable levels. The production rate in each production stage is controlled to satisfy demand, avoid overloading, and eliminate machine starvation or blocking. The system's improvement is demonstrated using a set of performance measures. Extensive simulation results show that the supervisory controller, when compared with the single-level distributed fuzzy controllers reduces WIP and cycle time while keeping backlog to acceptable levels.

An add-on type fuzzy controller for control system retrofit

The present paper advocates the concept of a supervisory fuzzy controller. The proposed FLC algorithm employs a parallel architecture where an additional PD-type FLC can be employed either in conjunction with a conventional PID controller or a static fuzzy controller to improve system performance. The additional FLC implements a different rule base compared to a standard PD-type FLC, when employed alone. The effectiveness of the proposed scheme has been demonstrated by implementing it in simulation for two linear plant models of marine diesel engines and a nonlinear process plant. Its performance has been compared vis-` a-vis a standard PD-type FLC, employed in simulation, for the same processes. The system performance is evaluated under both transient and steady state load disturbance conditions and also under noisy measurement environment.

A parallel fuzzy-controlled flexible manipulator using optical tip feedback

Investigation and development of a fuzzy-controlled highly non-linear two-axis manipulator with a single-flexible link using a novel patented optical tip displacement feedback is described. The controller comprises a parallel fuzzy supervisor that is used to alter the derivative term of a linear classical PD controller, which is updated in relation to the measured tip error and error rate. Implementation of the supervisory fuzzy controller is described using both serial and parallel operation on transputers. The design of the fuzzy rules was made with a modified closed-loop phase-plane method. The design approach results in a controller implementation that uses only 14 rules and is suitable for cheaper CPU-constrained and memory-challenged embedded processors. The benefits introduced by this procedure include a method to decide where and when the action takes effect in the controller and a greatly reduced rule base. The parallel operation achieved rise times of 0.033 s and settling times of 0.064 s for a payload of 0.7 kg considerably better than other workers did. An 128% increase in payload, 73.5% faster settling time and a reduction of steady-state error of over 50% were achieved using fuzzy control over its classical counterpart.

Application of Cooperative Control to Petroleum Plants Using Fuzzy Supervisory Control and Model Predictive Multi-variable Control

This paper describes hierarchical control with fuzzy supervisory control and model predictive multivariable control (MPC) in a petroleum plant. MPC is effective in time delay, interference, and handling constraints. Fuzzy logic controllers are effective for plants with large time delay and non-linearity. Our proposed hierarchical control combines their advantages. Fuzzy supervisory control, which determines set points for MPC, consists of an estimation block and a compensation block. We use a statistical model with multi-regression analysis for the estimation block to estimate parameters of plant operation, and fuzzy logic for the compensation block to correct output of the statistical model. Hierarchical control has been applied to an actual plant in an oil refinery, and showed satisfactory performance.

Novel Design Method for the Ball Mill Pulverizing System Based on Fuzzy Reasoning and Auto-Tuning PID Control

The pulverizing process of a ball mill is rather complex. It is difficult to obtain the optimal control by the conventional control methods, so a new fuzzy self-optimizing control method is presented. The method divides the controller into two parts: the fuzzy adaptive PID control and the supervisory fuzzy control. The self-optimizing inference sub-system of supervisory fuzzy control is proposed for solving imprecise constrains optimization problems, and fuzzy self-optimizing interval and fuzzy self-optimizing step are applied to speed the rate of self-optimizing and improve the accuracy of self-optimizing. The fuzzy adaptive PID controller combines fuzzy self-adjusting PID control with fuzzy rough-tuning mechanism. The operation result shows that it is quite suitable for the control of mill and a large amount of electric energy is saved.

Expert control of DO in the aerobic reactor of an activated sludge process

An expert control structure is proposed for the control of dissolved oxygen (DO) in a Nitrification Denitrification Biological Excess Phosphorus Removal (NDBEPR) plant to account for the several processes that are influenced by the DO concentration in the aerator. In the scheme a supervisory fuzzy controller determines the set point of an inner DO control loop where an Adaptive Robust Generic Model Control (ARGMC) controller is used. The fuzzy supervisory control has a hierarchical structure. Off-line measurements of biological parameters of influent and effluent streams can be used to periodically update the set points of the fuzzy controllers. The complete control scheme has been designed for an actual plant and tested by a simulation programme based on the n. 2 IAWQ model for the biological reactors and on separate models of the clarification and thickening phases for the settler. Simulation and experimental results show that good operation conditions can be obtained.

Multivariable fuzzy supervisory control for the laminar cooling process of hot rolled slab

A multivariable fuzzy system is presented for higher level supervision of industrial process. With the help of a statistical process controller, this fuzzy supervisory control system can replace the human operator in the laminar cooling process. Since a feedforward compensator is designed to suppress the external disturbance, the proposed system can automatically find the proper operating points for the cooling process under the variation of boundary conditions. The values of control variables at the operating point are taken as the setpoint of a distributed control system in the relevant cooling mode. The simulation and practical experiment show the robust performance of the suggested system.