Abdul R. Ofoli

Design and Implementation of Parallel Fuzzy PID Controller for High-Performance Brushless Motor Drives: An Integrated Environment for Rapid Control Prototyping

This paper presents an integrated environment for the rapid prototyping of a robust fuzzy proportional-integral-derivative (PID) controller that allows rapid realization of novel designs. Both the design of the fuzzy PID controller and its integration with the classical PID in a global control system are developed. The architecture of the fuzzy PID controller is basically composed of three parallel fuzzy subcontrollers. Then, the parallel subcontrollers are grouped together to form the overall fuzzy PID controller. The fuzzy proportional, integral, and derivative gains are direct output from the parallel fuzzy subcontrollers and are derived in the error domain. Thus, the proposed architecture presents an alternative to control schemes employed so far. The integrated controller is formulated and implemented in real time, using the speed control of a brushless drive system as a test bed. The design, analysis, and implementation stages are carried out entirely using a dSPACE DS1104 digital-signal-processor-based real-time data acquisition control system and MATLAB/Simulink environment. Experimental results show that the proposed hybrid fuzzy PID controller produces superior control performance than the conventional PID controllers, particularly in handling nonlinearities and external disturbances.

DSP-Based Laboratory Implementation of Hybrid Fuzzy-PID Controller Using Genetic Optimization for High-Performance Motor Drives

This paper presents a real-time implementation of a genetic-based hybrid fuzzy-proportional-integral-derivative (PID) controller for industrial motor drives. Both the design of fuzzy-PID (FPID) controller and its integration with the conventional PID in global control system to produce a hybrid design are demonstrated. A genetic optimization technique is used to determine the optimal values of the scaling factors of the output variables of the FPID controller. The objective is to utilize the best attributes of the PID and FPID controllers to provide a controller which will produce better response than either the PID or FPID controller. The principle of the hybrid controller is to use a PID controller, which performs satisfactorily in most cases, while keeping in the background a FPID controller, which is ready to take over the PID controller when severe disturbances occur. The hybrid controller is formulated and implemented in real time, using the speed control of a brushless drive system as a testbed. The design, analysis, and implementation stages are carried out entirely using a dSPACE DS1104 digital-signal-processor-based real-time data acquisition control system and MATLAB/Simulink environment. Experimental results show that the proposed FPID controller-based genetic optimization produces better control performance than the conventional PID controllers, particularly in handling nonlinearities and external disturbances.

DSP-Based Real-Time Implementation of a Hybrid

An embedded hybrid Hinfin adaptive fuzzy control structure is implemented for trajectory tracking control of a brushless servo drive system. The control structure employs a fuzzy logic controller incorporating an Hinfin tracking controller via an acceleration feedback signal. The fuzzy logic controller is equipped with an adaptive-law-based Lyapunov synthesis approach to compensate for system uncertainty and random changes in the external load acting on the drive system. The proposed control structure is experimentally verified on a state-of-the-art dSPACE DS1104 digital signal processor (DSP)-based data acquisition and control system in a laboratory 1-hp brushless drive system. The controllers are first designed in Simulink. Then, the Real-Time Workshop is used to automatically generate optimized C code for real-time applications. Afterward, the interface between MATLAB/Simulink and the dSPACE DS 1104 allows the control algorithm to run on the hardware processor of the DSP. The result is a powerful testbed for the rapid design and implementation of the hybrid tracking controllers for a wide variety of operating conditions. Experimental results are provided to verify the effectiveness of the proposed controller. Considerable improvement in the performance generated by the hybrid controller is compared with the traditional Hinfin controller

H \infty

This paper presents a successful implementation of a fuzzy logic controller structure for switch-mode power-stage dc-dc converters and evaluates experimentally its sensitivity for variable supply voltages and load resistance variations. The optimum topology of the controller structure is determined using experimental tests. An advanced test-bed system is used to evaluate the robustness capacities of the controller under varying loading conditions and input voltage variations. The experiment is performed using a low-cost microcontroller PIC16F877 to verify the design performance over a wide range of operating conditions. The controller shows very interesting tracking features and is able to cope with load changes and input voltage variations. The proposed controller structure is general and can be directly applied to any dc-dc converter topologies. The fuzzy controller structure is compared experimentally with the existing proportional-integral control used in industry. It yields a better dynamic performance without overshoot

Adaptive Fuzzy Tracking Controller for Servo-Motor Drives

This paper presents a complete and integrated environment for rapid prototyping of robust proportional-integral-derivative (PID) fuzzy controller that allows rapid realization of novel designs. The architecture of the proposed controller is basically composed of three independent fuzzy sub-controllers. Then, the three sub-controllers are combined together to form the overall fuzzy PID controller. The design strategy incorporates a fuzzy tuner for realizing a guaranteed-PID performance fuzzy controller that can assist practitioner to achieve the best control design for the entire operating envelop. The design, analysis, and implementation stages are carried out entirely using a dSPACE DS1104 digital signal processor (DSP)-based real-time data acquisition control (DAC) system, and MATLAB/Simulink environment. Experimental results show that the proposed fuzzy PID controller produces superior control performance than the conventional PID controllers, particularly in handling nonlinearities and external disturbances

Real-Time Implementation of a Fuzzy Logic Controller for Switch-Mode Power-Stage DC–DC Converters

A novel control topology of adaptive network-based fuzzy inference system (ANFIS) for control of the dc-dc converter is developed and presented in this paper. It essentially consists of combining fuzzy inference system and neural networks and implementing within the framework of adaptive networks. The architecture of the ANFIS along with the learning rule, which is used to give an adaptive and learning structure to a fuzzy controller, is also described. The emphasis here is on fuzzy-neural-network control philosophies in designing an intelligent controller for the dc-dc converter that allows the benefits of neural network structure to be realized without sacrificing the intuitive nature of the fuzzy system. Specifically, it permits this type of setup to simultaneously share the benefits of both fuzzy control and neural network capabilities. An experimental test bed is designed and built. The components are tested individually and in various combinations of hardware and software segments. Two categories of tests, namely, load regulation and line regulation, are carried out to evaluate the performance of the proposed control system. Experimental results demonstrate the advantages and flexibilities of ANFIS for the dc-dc converter.

dSPACE DSP-Based Rapid Prototyping of Fuzzy PID Controls for High Performance Brushless Servo Drives

This paper presents a real-time implementation of a genetic-based hybrid fuzzy-PID controller for industrial motor drives. Both the design of fuzzy-PID controller and its integration with the conventional PID in global control system to produce a hybrid design is demonstrated. A genetic optimization technique is used to determine the optimal values of the scaling factors of the output variables of the fuzzy-PID controller. The objective is to utilize the best attributes of the PID and fuzzy-PID controllers to provide a controller, which will produce better response than either the PID or the fuzzy-PID controller. The principle of the hybrid controller is to use a PID controller, which performs satisfactorily in most cases, while keeping in the background, a fuzzy-PID controller, which, is ready to take over the PID controller when severe disturbs occur. The hybrid controller is formulated and implemented in real-time, using the speed control of a brushless drive system as a testbed. The design, analysis, and implementation stages are carried out entirely using a dSPACE DS1104 digital signal processor (DSP)-based real-time data acquisition control (DAC) system, and MATLAB/Simulink environment. Experimental results show that the proposed fuzzy- PID controller-based genetic optimization produces better control performance than the conventional PID controllers, particularly in handling nonlinearities and external disturbances.

Hardware implementation of an adaptive network-based fuzzy controller for DC-DC converters

This paper presents a successful implementation of a fuzzy logic controller structure for dc-dc switching converters and evaluates experimentally its sensitivity for variable supply voltages and load resistance variations. The optimum topology of the controller structure is determined using experimental tests. An advanced test bed system is used to evaluate the robustness capacities of the controller under varying loading conditions and input voltage variations. The experiment is performed using a low cost micro-controller PIC16F877 in order to verify the design performance over a wide range of operating conditions. Experimental results are obtained using appropriate scaling factors associated with the input variables of the fuzzy controller. The controller show very interesting tracking features and is able to cope with load changes and input voltage variations. The proposed controller structure is general and can be directly applied to any dc-dc converter topologies. The fuzzy controller structure is compared experimentally with the existing PI control used in industry. It yields a better dynamic performance without overshoot.

DSP-Based Implementation of Fuzzy-PID Controller Using Genetic Optimization for High Performance Motor Drives

In this paper, a multi-layer fuzzy logic controller (MLFC) is proposed for the transient stability enhancement of the electric power system making use of a dynamic braking strategy. The proposed controller has two layers: the first layer termed the supervisory layer specifies the region of operation of the sub-controllers within the second layer called the execution layer. The outputs of the sub-controllers are then fuzzily combined to achieve the overall objective of the system. The MLFC was found to be very robust to any changes in the network configuration. The scheme was tested on a nine bus system with three generators and three loads and also on the IEEE four generator test system. The simulation results provided shows the effectiveness of the MLFC and thus the proposed fuzzy strategy provides a simple and effective method of transient stability enhancement.

Real-time implementation of a fuzzy logic controller for dc-dc switching converters

Notwithstanding massive fire safety campaigns being carried out, incidents of fire outbreaks continue to increase annually. The alarming rate of these fire outbreaks requires an engineered solution system that detects fire in its early stages and contributes to the firefighting effort. Current research efforts have been directed towards the development of multi-sensor fire detection algorithms to increase the sensitivity of fire detection devices and reduce nuisance alarms. This paper presents the design and implementation of a multi-sensor fire detection and notification system using fuzzy logic. The microcontroller processes data from an MQ2 smoke sensor, a TMP102 temperature sensor and a DFRobot flame sensor using a fuzzy logic algorithm to determine the fire status.