Showing papers on "PID controller published in 1970"
01 Jan 1970
TL;DR: Comparison of the conventional tuning method with the performance of tuning method by using genetic algorithm can be seen and population size of 40 delivers the fastest rise time and settling time.
Abstract: Controller tuning is one of the important aspect in industry. With a good tuning method, it can ensure the quality of the process and product produce. Apart from that, it can protect the environment and help the company to reduce the cost. Genetic algorithm is one of the tuning method that increase usage and awareness in industry. Thus, the objective of this research is to compare the performance of the conventional tuning method with the performance of tuning method by using genetic algorithm can be seen. Optimization was done on stripping section of distillation column by using genetic algorithm with population size of 20, 40, 60 and 80 and comparing the result with previous optimization using Ziegler-Nichols method. The result obtain showed large improvement in the process response especially on rise time from 1.33 s to 1.31s and settling time from 4.56 to 4.46. Finally, population size of 40 deliver the fastest rise time and settling time.
8 citations
TL;DR: The proposed Neural Network -based Model Reference Adaptive Controller (NN-MRAC) can significantly improve the system behavior and force the system to follow the reference model and minimize the error between the model and plant output.
Abstract: The aim of this paper is to design a neural network based intelligent adaptive controller. It consists of an online multilayer back propagation neural network structure along with a conventional Model Reference Adaptive Control (MRAC).The training patterns for the Neural Network (NN) are obtained from the conventional PI controller. In the conventional model reference adaptive control (MRAC) scheme, the controller is designed to realize plant output converges to reference model output based on the plant which is linear. The NN is used to compensate the nonlinearity of the plant that is not taken into consideration in the conventional MRAC. The control input to the plant is given by the sum of the output of conventional MRAC and the output of NN. The proposed Neural Network -based Model Reference Adaptive Controller (NN-MRAC) can significantly improve the system behavior and force the system to follow the reference model and minimize the error between the model and plant output. The effectiveness of the proposal control scheme is demonstrated by simulations.
8 citations
01 Jan 1970
TL;DR: This paper presents hybrid stepper motor (is a type of stepping motor) modelling and simulation which is widely used a kind of motor in industrial applications and it was observed that Fuzzy Logic controller’s response is better than PID's.
Abstract: This paper presents hybrid stepper motor (is a type of stepping motor) modelling and simulation which is widely used a kind of motor in industrial applications. In this study, the stepper motor was modelled using bond graph technique and simulation for desired position was executed on LabVIEWgraphical interface. Then, firstly a convenient PID controller was designed for position, speed and current and PID close loopresponse was obtained for position control. Then, PID parameters for each controller were arranged separately to obtain good response Secondly, Fuzzy Logic controller applied to the system and its response was obtained. Finally, both responses are compared. According to comparison, it was observed that Fuzzy Logic controller’s response is better than PID’s. (In this paper, all shown responses were observed for 120 degree desired position)
6 citations
01 Jan 1970
TL;DR: In this paper, a three-phase brushless DC motor has been simulated and controlled using LabVIEW program, the system has been controlled by classical PID controller and all the results were fulfilled using labVIEW program.
Abstract: This paper aims to simulate and control a three-phase Brushless DC Motor. Bond Graph method has been used to obtain fast and simple dynamic model. The system has been controlled by classical PID controller. All the paper results were fulfilled using LabVIEW program.
4 citations
01 Jan 1970
TL;DR: Simulation result shows that the performance of PID controller using the proposed Nelder Mead based PID design method is better than traditional methods and resistant to disturbance.
Abstract: This paper presents a design of PID controller for furnace temperature control system with disturbance. Currently, PID controller has been used to operate in electric furnace temperature control system because its structure is simpler compared to others. However, the issue of tuning and designing PID controller adaptively and efficiently is still open. This paper presents an improved PID controller efficiency from tuning by Nelder Mead method. The parameters of PID controller shall be obtained from the Nelder Mead optimization procedure. Errors between desired magnitude response and actual magnitude response are calculated by using the Integral of Absolute Error (IAE). The proposed Nelder Mead based PID design method is simpler, more efficient and effective than the existing traditional methods included Ziegler Nichols, Cohen-Coon and Direct Synthesis. Simulation result shows that the performance of PID controller using this proposed method is better than traditional methods and resistant to disturbance.
3 citations
TL;DR: This paper demonstrates the possibility of applying artificial neural networks (ANNs) in the self-tuning PID control of the hot-spot-temperature in a fixed-bed catalytic reactor system by applying the values of the ANN weights, that are continually updated by the algorithm, to give the relevant PID controller parameters.
Abstract: This paper demonstrates the possibility of applying artificial neural networks (ANNs) in the self-tuning PID control of the hot-spot-temperature in a fixed-bed catalytic reactor system. In this reactor system sulfur dioxide is oxidized using vanadium pentoxide catalyst. Unlike the conventional self-tuning PID control algorithm, the ANN applied to the self-tuning PID (NNW-PID) philosophy is an inherent nonlinear estimator and therefore identifies a nonlinear system directly from historical data supplied by the plant. In the majority of control applications, the ANN is employed as a predictor of future outputs within an established predictive control algorithm [1,2]. In this paper we propose a scheme where an ANN is employed on-line with a non predictive PID controller to give adaptive control of the reactor. This is accomplished by applying the values of the ANN weights, that are continually updated by the algorithm, to give the relevant PID controller parameters. Since real plant data is used to train the network, the algorithm can be applied to similar real-world problems.
1 citations
TL;DR: It is demonstrated both by simulation and experimental implementation on a prototype system that fuzzy logic control can provide better control, does not require mathematical modelling of the plant and yields better disturbance rejection properties.
Abstract: High performance position control can be obtained with dc servomotors and actuators by using efficient control strategies. For precise control of position, the control strategy employed should result in fast control of the output, with minimum overshoot and least steady state error. The advancement of control theory over the last 30 years resulted in a huge choice of control schemes, given a plant or a system to be controlled. Despite this, traditional PID (proportional integral and derivative) control scheme is the popular choice for implementation in industrial environment, as this scheme is simple and easy to implement and its design does not require an exact knowledge of the controlled plant dynamics. Fuzzy logic is recently finding wide popularity in various applications that include management, economics, medicine and process control systems. This paper presents the simulation and experimental results of the adaptive fuzzy logic control scheme proposed for a position control system, with cost-effective, real time implementation as the main objective. Traditional PID control and more recent fuzzy logic control schemes have been used for the studies. It is demonstrated both by simulation and experimental implementation on a prototype system that fuzzy logic control can provide better control, does not require mathematical modelling of the plant and yields better disturbance rejection properties.