Preface. Automatic Train Operation System by Predictive Fuzzy Control (S. Yasunobu, S. Miyamoto). Application of Fuzzy Reasoning to the Water Purification Process (O. Yagashita, O. Itoh, M. Sugeno). The Application of a Fuzzy Controller to the Control of a Multi-Degree-of-Freedom Robot Arm (E.M. Scharf, N.J. Mandic). Optimizing Control of a Diesel Engine (Y. Murayama et al.). Development of Performance Adaptive Fuzzy Controllers with Application to Continuous Casting Plants (G. Bartolini et al.). A Fuzzy Logic Controller for Aircraft Flight Control (L.I. Larkin). Automobile Speed Control System Using a Fuzzy Logic Controller (S. Murakami, M. Maeda). An Experimental Study on Fuzzy Parking Control Using a Model Car (M. Sugeno, K. Murakami). A Fuzzy Controller in Turning Process Automation (Y. Sakai, K. Ohkusa). Design of Fuzzy Control Algorithms with the Aid of Fuzzy Models (W. Pedrycz). Human Operator's Fuzzy Model in Man-Machine System with a Nonlinear Controlled Object (K. Matsushima, H. Sugiyama). The Influence of Some Parameters on the Accuracy of a Fuzzy Model (J.B. Kiszka, M.E. Kochanska, D.S. Sliwinska). A Microprocessor Based Fuzzy Controller for Industrial Purposes (T. Yamazaki, M. Sugeno). The Application of Fuzzy and Artificial Intelligence Methods in the Building of a Blast Furnace Smelting Process Model (H. Zhao, M. Ma). An Annotated Bibliography of Fuzzy Control (R.M. Tong).

Industrial applications of fuzzy control

https://www.econstor.eu/bitstream/10419/243697/1/1683642686.pdf

Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm

https://www.econstor.eu/bitstream/10419/243676/1/1683616235.pdf

System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm

Genetic algorithm (GA) based PID (proportional integral derivative) controller has been proposed for tuning optimized PID parameters in a continuous stirred tank reactor (CSTR) process using a weighted combination of objective functions, namely, integral square error (ISE), integral absolute error (IAE), and integrated time absolute error (ITAE). Optimization of PID controller parameters is the key goal in chemical and biochemical industries. PID controllers have narrowed down the operating range of processes with dynamic nonlinearity. In our proposed work, globally optimized PID parameters tend to operate the CSTR process in its entire operating range to overcome the limitations of the linear PID controller. The simulation study reveals that the GA based PID controller tuned with fixed PID parameters provides satisfactory performance in terms of set point tracking and disturbance rejection.

/pdf/genetic-algorithm-based-pid-controller-tuning-approach-for-1cchrk0nhg.pdf

Genetic algorithm based PID controller tuning approach for continuous stirred tank reactor

This article presents Fuzzy Particle Swarm Optimization of PID controller PSO-FPIDC used as a Conventional Power System Stabilizer CPSS to improve the dynamic stability performance of generating unit during low frequency oscillations. Speed deviation Δw and acceleration Δẇ of synchronous generator are taken as input to the PSO-FPIDC controller connected to Single Machine Infinite Busbar SMIB system. This controller examined under different perturbation scenarios. The dynamic performance of the PSO-FPIDC is compared with the Fuzzy Teacher Learner Based Optimization PID TLBO-FPIDC, PSO-PID, TLBO-PID and optimal parameters of convectional Power System Stabilizer CPSS. The results show that the performance of PSO-FPIDC has small overshoot/undershoot and damp out lower frequency oscillations very quickly as compared to other controllers.

Dynamic Stability Enhancement Using Fuzzy PID Control Technology for Power System

A sliding mode controller for a non-isolated DC/DC, bidirectional converter is presented and comparative study with PI controller is done along with ISE analysis, in order to do performance analysis. The proposed system can be utilized in many applications such as electrical vehicle, distributed power generation or small grids. Second theorem of Lyapunov is utilized and stability of the closed loop system is mathematically proven. The adopted control strategy achieves effective output voltage regulation and good dynamic stability. Rejection of disturbance is also an inherent characteristic of this technique. Furthermore, it is illustrated that the system can successfully follow changes of load demand and compensates sudden disturbances in operating condition. The design is evaluated and verified using Matlab/Simulink. Results of Matlab simulation are provided to show the feasibility of the proposed system and effectiveness of control method. Simulation results show that this technique can provide a considerable edge over control techniques which are presently available (applied) over this type of converter.

https://ijpeds.iaescore.com/index.php/IJPEDS/article/download/11071/10909

Performance analysis of DC/DC bidirectional converter with sliding mode and pi controller

CSTR plays a vital role in almost all the chemical reactions and is a highly nonlinear system exhibiting stable as well as unstable steady states. The variables which characterize the quality of the final product in CSTR are often difficult to measure in real-time and cannot be directly measured using the feedback configuration [1]. So, a virtual feedback control is implemented to control the state variables using Extended Kalman Filter (EKF) in the feedback path. Since it is hard to determine the optimal or near optimal PID parameters using classical tuning techniques like Ziegler Nichols method, a highly skilled optimization algorithm like Particle Swarm Optimization (PSO) is used. This work is based on the optimal tuning of virtual feedback PID control for a CSTR system using PSO algorithm for minimum Integral Square Error (ISE) condition.

Optimal tuning of virtual feedback PID controller for a Continuous Stirred Tank Reactor (CSTR) using Particle Swarm Optimization (PSO) algorithm

This paper proposes a new active fault-tolerant control (FTC) using fuzzy predictive logic. The FTC approach is based on two steps, fault detection and isolation (FDI) and fault accommodation. The fault detection is performed by a model-based approach using fuzzy modeling and fault isolation uses a fuzzy decision making approach. The information obtained on the FDI step is used to select the model to be used in fault accommodation, in a model predictive control (MPC) scheme. The fault accommodation is performed with one fuzzy model for each identified fault. The FTC scheme is used to accommodate the faults of real-time CSTR level process. The fuzzy FTC scheme proposed in this paper was able to detect, isolate and accommodate correctly the considered faults of the system.

Weighted fuzzy fault tolerant model predictive control

CSTR plays a vital role in almost all the chemical reactions and is a highly nonlinear system exhibiting stable as well as unstable steady states. The variables which characterize the quality of the final product in CSTR are often difficult to measure in real-time and cannot be directly measured using the feedback configuration [1]. So, a virtual feedback control is implemented to control the state variables using Extended Kalman Filter (EKF) in the feedback path. Since it is hard to determine the optimal or near optimal PID parameters using classical tuning techniques like Ziegler Nichols method, a highly skilled optimization algorithm like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) are used. This work is based on the optimal tuning of virtual feedback PID control for a CSTR system using soft computing algorithm for minimum Integral Square Error (ISE) condition.

Soft computing techniques based optimal tuning of virtual feedback PID controller for chemical tank reactor

The inherent characteristics of fuzzy logic theory make it suitable for fault detection and diagnosis (FDI). Fault detection can benefit from nonlinear fuzzy modeling and fault diagnosis can profit from a transparent reasoning system, which can embed operator experience, but also learn from experimental and/or simulation data. Thus, fuzzy logic-based diagnostic is advantageous since it allows the incorporation of a-priori knowledge and lets the user understand the inference of the system. In this paper, the successful use of a fuzzy FDI based system, based on dynamic fuzzy models for fault detection and diagnosis of an industrial two tank system is presented. The plant data is used for the design and validation of the fuzzy FDI system. The validation results show the effectiveness of this approach.

/pdf/takagi-sugeno-fuzzy-expert-model-based-soft-fault-diagnosis-4nn8zbyo6s.pdf

M. Geetha

Papers

Performance analysis of DC/DC bidirectional converter with sliding mode and pi controller

Optimal tuning of virtual feedback PID controller for a Continuous Stirred Tank Reactor (CSTR) using Particle Swarm Optimization (PSO) algorithm

Weighted fuzzy fault tolerant model predictive control

Soft computing techniques based optimal tuning of virtual feedback PID controller for chemical tank reactor

Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system