Improvement in Controller Performance for Distillation Column Using IMC Technique

Focusing on the passive network, this paper analyzes the discrete mathematical model and its control strategy of the passive terminal of the VSC-HVDC system on the basis of the α-β frame of axes. The method of the functioning-fuzzy-subset inference is introduced into automatic regulation of the proportional integral (PI) parameters. Based on the digital signal processor (DSP), the fuzzy PI control method is adopted and the VSC-HVDC experiment device is constructed. The experiment of constant AC voltage control based on fuzzy adaptive PI controller in transmitting power to passive network via VSC-HVDC system is developed. The experimental results show that the fuzzy controller with adaptive PI parameters for AC voltage control of VSC-HVDC system is effective.

The distillation columns are widely used in various chemical industries. It is basically used for separating the components of a liquid mixture. The More volatile component is vaporized, and the less volatile one is left back in the liquid. The plant of distillation column that is considered in the present work is a SOPDT transfer function model imported from an online source. Due to the impact of present dead time, the stability in the output variable is a challenging task. So, the improved control performance is achieved by the dead time compensation using the Smith predictor. The PI control systems are designed, using MATLAB, for this plant with and without Smith predictor, and their performance is compared.

In order to meet the requirements of high precision and fast response of permanent magnet direct drive (PMDD) wind turbines, this paper proposes a fuzzy proportional integral (PI) controller associated with a new control strategy for wind turbine converters. The purpose of the control strategy is to achieve the global optimization for the quantization factors, ke and kec, and scale factors, kup and kui, of the fuzzy PI controller by an improved particle swarm optimization (PSO) method. Thus the advantages of the rapidity of the improved PSO and the robustness of the fuzzy controller can be fully applied in the control process. By conducting simulations for 2 MW PMDD wind turbines with Matlab/Simulink, the performance of the fuzzy PI controller based on the improved PSO is demonstrated to be obviously better than that of the PI controller or the fuzzy PI controller without using the improved PSO under the situation when the wind speed changes suddenly.

In this paper Fuzzy adaptive proportional-integral (PI) controller is designed to control the level of interacting tank. In the interacting process the load on the first process affects the second process and vice versa. The conventional PI controller cannot reach satisfactory result. The gain parameters of PI control are obtained by Ziegler-Nichols (ZN) tuning method and can be adjusted automatically using fuzzy logic. This paper illustrates the comparative analysis of conventional PI and fuzzy adaptive PI controller.

In this paper mathematical models of Mamdani type fuzzy PI or PD controllers via Center of Gravity (CoG) defuzzification are obtained. These controllers are called the simplest fuzzy PI or PD controllers as minimal number of fuzzy sets (two fuzzy sets on each of the two-input variables and three fuzzy sets on the output variable) are employed. The basic components of these models are L-type and Y-type membership functions on each input variable, trapezoidal or triangular membership functions on output variable, minimum / algebraic product t-norm, maximum OR operator, three linear fuzzy control rules, minimum / algebraic product inference and CoG defuzzification. Mathematical models of fuzzy controller via CoG defuzzification were not reported in the literature and are therefore presented for the first time in this paper.

PMSM servo system has nonlinear, parameter time-varying, load disturbance and other issues. In the face of these problems, traditional PI control strategies often fail to achieve ideal parameter tracking and load immunity, showing a large degree of limitations. Aiming at this kind of problem, this paper designs a fuzzy PI control method combining PI control and fuzzy control, and uses the vector rotation control method of current speed double closed loop to drive the permanent magnet synchronous motor for control. Finally, the traditional PI control system and fuzzy PI control system simulation model are built in Matlab/Simulink environment. The simulations were carried out in three cases of high speed, medium speed and low speed, and the speed and current waveforms of the two control methods were observed. The simulation results show that the fuzzy PI control system can increase the response rate, reduce the overshoot of the speed and reduce the current fluctuation in the full speed range. The robustness of the system is improved, the adaptability of the system to load changes is enhanced, and the correctness of the theoretical analysis is verified.

Typical Takagi–Sugeno PI and PD fuzzy controllers: analytical structures and stability analysis

This paper proposes a new rotor (also called secondary) field oriented control model of linear induction machine (LIM) based on a fuzzy self-adapting PI controller which mainly considers the influence of longitudinal end effect. Because the conventional PI controller's parameters cannot be adjusted by itself, it is not very suitable to ever changeful situations especially influenced by non-linear circuit parameters in LIM, such as mutual inductance and secondary resistance. Depending on the rapidity of response, the fuzzy self-adapting PI controller is able to regulate the parameters of its embedded PI controller through fuzzy control scheme to accustom itself to different new situations. Simulation results indicate that the proposed control model has strong robustness and quick response to new dynamic procedures.

The question addressed is placed in the broad context of fuzzy control, based on fuzzy PI controller, of industrial processes, the equivalence of fuzzy PI controllers with linear PI controllers, tuned with given methods, for the improvement of performance of control systems. The purpose of the study is to analyze the possibility of these implementation and to find the behavior of the resulted fuzzy control systems to compare with the linear control systems. The tuning criteria chosen are the modulus criterion in Kessler’s variant for second-order processes with two time constants, the symmetric criterion in Kessler’s variant, for the second order processes with purely integrator character and Ziegler-Nichols criterion for slow processes. The equivalence of the fuzzy controllers with the linear controllers was made using the input-output transfer characteristics of the fuzzy block and its linearization in the origin to obtain the gain in the origin. The results consist of an equivalence formula for the controller, simulation characteristics for the control systems, comparisons and analyses. The main conclusions are that fuzzy regulators can be designed by equating linear regulators with the presented method and better regulation performances can be obtained.

In this work, a new adaptive extreme learning machine (ELM) neural network-based fuzzy controller is designed and simulated for implementing speed regulation in a permanent magnet synchronous motor. ELM is a neural model wherein the number of hidden neurons to be placed in the hidden layer is tuned during the process of neural network training itself. A new adaptive ELM model is developed for placing the number of hidden neurons in the hidden layer, and this new adaptive ELM is tuned with artificial bee colony (ABC) algorithm for optimizing its weight parameters and also the number of hidden neurons. Fuzzy proportional–integral (PI) controller is developed in this work in order to eliminate the steady-state error. The new adaptive ELM neural model optimized with ABC algorithm is applied to tune the input parameters of the fuzzy PI controller and also on optimizing the rules and fuzzy membership functions. The optimized adaptive ELM neural network-based fuzzy PI controller is utilized to investigate the speed regulation of permanent magnet synchronous motor (PMSM) in this work. The developed new PI controller with the PMSM is tested for its performance characteristics and to prove its validity is compared with the traditional controller and other heuristic controllers proposed in earlier literature works.

The asynchronous machine has qualities that make it a preferred candidate for variable speed drives, but this simplicity is accompanied by great physical complexity due to the electromagnetic interactions between the stator and the rotor. This paper presented a comparison between fuzzy PI logic and PI traditional controllers based for the speed control of the asynchronous machine (ASM); the analysis was achieved in MATLAB/SIMULINK environment. At first, it described the ASM indirect vector control, which consists in eliminating the coupling between the armature and the inductor to reduce its operation comparable to that of a DC machine. In addition, it described the PI controller; the fuzzy logic controller that does not deal with a well-defined mathematical relationship, but uses inferences with multiple rules control, then, compared their performance. The results of all the controllers have been compared. The machine started at t = 0.1 s from zero speed to t = 0.4 s at no load, and at t = 5 s, a load torque of T = 10 N m was applied to observe the controllers’ performance. As for the comparison of the controllers’ speed response, it can be observed that the PI controller has the highest and that the fuzzy PI has the lowest overshoot of (rad/s). Besides, the settling time for the fuzzy PI is lower than that of PI. The results showed the effectiveness and robustness of the fuzzy control system compared to PI controller for indirect vector control of asynchronous machine.KeywordsAsynchronous machine (ASM)Indirect field-oriented control (IFOC)PI controllerFuzzy logic controllerVector control

Since a Superconducting Magnetic Energy Storage (SMES) unit with a self–commutated converter is capable of controlling both the active and reactive powers simultaneously and quickly, increasing attention has been focused recently on power system stabilization by SMES control. This paper presents a novel control method of SMES with a self-tuned Fuzzy Proportional Integral (FPI) controller associated with the Automatic Generation Control (AGC) for improving Load Frequency Control (LFC) in a single area power system. Boiler dynamics and nonlinearities such as governor dead band (DB) and generator rate constraints (GRC) are considered in the developed comprehensive mathematical model of a single area isolated power system. The effects of the self–tuning configuration of FPI controller in AGC on SMES control is compared with that of fixed gain PI controlled AGC. It is seen that with addition of FPI controller, SMES can perform a more effective primary frequency control for single area power system. Key words: Load frequency control; single area power system; automatic generation control; superconducting magnetic energy storage unit; fuzzy proportional integral controller

Power conditioning system (PCS) is an interface between distributed generation and utility grid. It regulates voltage, current and power transmitted from the hybrid direct fuel-cell/turbine (DFC/T) power plant to utility grid. This paper presents a self-adaptive fuzzy PI controller of three phase inverter in PCS for the DFC/T power plant. One of the main tasks of PCS for distributed generations is power flow control. Traditionally, the control scheme for grid connected inverters is PI controller. However, PI control scheme would lead to large overshoot and long response time when the load increases sharply. Thus, a hybrid fuzzy PI control scheme is proposed in this paper in order to improve the power flow control. The fuzzy controllers can adjust the PI parameters according to the voltage error and the derivative of voltage error. The overall self-adaptive tuning fuzzy PI controller can update the PI parameters in real time, which makes the power flow control much more accurate than conventional PI controllers. Analysis and design methodologies of hybrid fuzzy PI controllers are presented in this paper. The model was developed in Matlab/Simulink environment. Simulation results show that the proposed control scheme can effectively reduce the overshoot and response time compared to the conventional PI controllers.

In this paper, we propose the use of a Particle Swarm Optimization (PSO) for tuning decentralized typical fuzzy PI (FPI) controllers applied for the stabilization of a helicopter model. The method is used such as to minimize a square error (SE) fitness function which quantifies the performance of the whole control system. The proposed PSO method exploits all the available knowledge about the system under control since, the considered typical FPI controllers are characterized by simple and interpretable structure, which can reduces the tuning time.

This paper presents a fuzzy PI controller as a power system stabilizer. The fuzzy PI controller has been designed in order to prepare an auxiliary signal for the excitation system of the synchronous generator. The proposed controller utilizes a combination of a fuzzy logic controller (FLC) and a PI controller, which are the most recent researches in the problem at hand. In comparison with the conventional fuzzy controllers, the proposed fuzzy PI controller takes advantage of both FLC and conventional PI controller to overcome all kind of disturbances. The damping of oscillations local modes in a power system will substantially be controlled by employing this controller as a replacement for conventional PSS. Furthermore, the presented paper considers the conventional PSS and compares its performance with respect to three proposed types of fuzzy PI controller. Correspondingly, the effects of the supplementary signals in damping oscillation have been shown. Finally, to scrutinize the effectiveness of the proposed controller in damping oscillations, a three-phase short circuit condition is studied in order to clarify the application of the developed methodology. The achieved outcomes show that the proposed controller for stabilizing power system can provide very good damping characteristic.