Fuzzy Logic Supervisory Control Research Articles

Servo and Regulatory Response of an Industrial Fluid Catalytic Cracking (FCC) Unit under Fuzzy Logic Supervisory Control

A self-tuning hierarchical controller in which a Fuzzy logic controller supervises the control actions of a conventional PID has been proposed, implemented and presented in this paper. The controller has been applied to a control study of Fluid Catalytic Cracking unit (FCCU) riser temperature, and regenerator temperature respectively. Comparison between the performance of the proposed Fuzzy-PID controller and the conventional PID was made in simulation studies of regulatory and servo performances of the two controller types. Six performance measures: Percent overshoot (OS), settling time (ST), integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE) were employed as the tools for performance comparison between the conventional PID and the Fuzzy-PID controller. For the tracking of riser temperature with a set point at 524oC, the performance indicators under PID control gave the following results overshoot (14.5%); settling time (40 seconds) Integral absolute error (8.246), integral square error (3.3); integral time absolute error(1762);integral time square error (43.77) while for the same indicators under Fuzzy-PID control the following values: overshoot (3.3%); settling time (40 seconds) ;Integral absolute error (8.811); integral square error (14.5); integral time absolute error(280),;integral time square error (31.11) .The results allude to the superiority of the fuzzy-PID scheme over the PID scheme in tracking the optimal set point of riser temperature. More so, for tracking the regenerator set point temperature of 746oC, comparative study of step response under the two schemes gave the following results in six performance indicators: overshoot (PID (12.6%) / Fuzzy-PID (6%)); settling time (PID (80 seconds) / Fuzzy-PID (20seconds)); Integral absolute error (PID (14.29) / Fuzzy-PID (8.63)); integral square error (PID(6.713). Fuzzy-PID (4.506)); integral time absolute error(PID(2858)/Fuzzy-PID(305.9)), integral time square error (PID(77.55)/Fuzzy-PID(33.05)) . Moreover, the fuzzy-PID controller also showed superior performance over the conventional PID controller in terms disturbance rejection (regulatory response) of both riser and regenerator temperature. The results from this study suggest that the application of fuzzy-PID scheme to temperature control offers good promise of improved fluid catalytic cracking unit (FCCU) operations.

Fuzzy logic supervisory controller for multi-input non-isolated DC to DC converter connected to DC grid

Multi-Input DC-DC Converter (MIDC) which integrates different renewable sources is an evolving technology. Researches in this field gave several topologies and configurations of Multi-Input DC to DC converters, for integrating multiple sources. In this work, a topology is identified first and a controller with power management is explored. A non-isolated series MIDC with three input sources is selected to inject power into the DC grid. This Unidirectional topology with less no of switches is selected to integrate renewable sources such as Photovoltaic Cells, Wind, Fuel Cells etc. to the grid. The topology acts like a boost converter to integrate low voltage sources to high voltage grid. Power management and control of MIDC, connected to grid is a challenging task, as viable techniques are still not explored. Fuzzy Logic Controller is proposed as a supervisory control for Power Management. Fuzzy Logic Supervisory control (FLSC) decides the quantum of power, which each source has to supply, depending upon the availability of power. Controller used for voltage and current control is simple PI controller. The closed loop simulation studies are done using MATLAB/SIMULINK and experimental results obtained using OPAL-RT is used to validate the identified configuration for power management. The main advantages of this converter are less component count, compact structure, and efficient energy utilization.

Modeling and Control of 5DOF Robot Arm Using Fuzzy Logic Supervisory Control

Modeling and Control of 5DOF Robot Arm Using Fuzzy Logic Supervisory Control

A supervisory control system for optimising nitrogen removal and aeration energy consumption in wastewater treatment plants

A fuzzy logic supervisory control system for optimising nitrogen removal and aeration energy consumption has been developed. This control system allows optimising and controlling the dissolved oxygen (DO) concentration in the aerobic reactors, the blowers discharge pressure and the effluent ammonia and nitrate concentrations. DO is controlled by adjusting control valve opening and blower discharge pressure is controlled by modifying rotational speed of the blowers. Optimum nitrification/denitrification is achieved by modifying the DO set point in the last aerobic reactor and the internal recirculation. This system has been tested by simulation in a Bardenpho process using the Activated Sludge Model No2. A significant improvement in stability on the activated sludge process is achieved. Moreover, significant energy saving has been achieved with this control strategy.

Application of fuzzy logic control in industry

In this paper an overview is given of the various ways fuzzy logic can be used to improve industrial control. The application of fuzzy logic in control is illustrated by two case studies. The first example shows how fuzzy logic, incorporated in the hardware of an industrial controller, helps to finetune a PID controller, without the operator having any a priori knowledge of the system to be controlled. The second example is from process industry. Here, fuzzy logic supervisory control is implemented in software and enhances the operation of a sintering oven through a subtle combination of priority management and deviation-controlled timing. Finally the key areas of research in fuzzy logic control are discussed: First the paper discusses how fuzzy logic control can be combined with other methods. By properly separating the a priori model knowledge of the process under control, a hierarchy of nonlinear control methods is established. After a short discussion of how to optimize an intuitive fuzzy rule base, we show that it is possible to derive conditions for asymptotic stability and robustness of fuzzy logic controllers, using classical nonlinear analysis.