In this paper, we formulate an optimization problem of establishing a fuzzy neural network model (FNNM) for efficiently tuning PID controllers of various test plants. An existing indirect, two-stage approach used a dominant pole assignment method with P=198 to find the corresponding PID controllers. Consequently, an adaptive neuro-fuzzy inference system (ANFIS) is used to independently train th...

Numerical Control, NC, machine tool feed servo systems are time-varying, but with the poor anti-interference of conventional PID control it is difficult to achieve satisfactory setting effect. Thus this paper puts forward an online adaptive fuzzy PID setting strategy. Further, in order to improve the control performance of fuzzy PID controllers, we use a genetic algorithm to set the initial PID...

This paper will first describe predictor controllers when the proportional-integral-derivative (PID) controllers are inactive for procedures that have large delay time (LDT) in transfer stage. Therefore in those states, the predictor controllers are better than the PID controllers, then compares three types of predictor controllers. The value of these controller’s parameters are obtained by tri...

Parallel robots exhibit good performance in terms of rigidity, accuracy, and dynamic characteristics. However, parallel robots have complex configurations and their dynamic model is highly nonlinear, and conventional PID controllers are not sufficiently robust for their motion control. In this paper, we have investigated the intelligent control of a hydraulically driven parallel robot based on ...

One of the most basic functions of control engineers is tuning of controllers. There are always several process loops in the plant necessitate of tuning. The auto tuned Proportional Integral Derivative (PID) Controllers are designed for applications where large load changes are expected or the need for extreme accuracy and fast response time exists. The algorithm presented in this paper is used...

Due to simple functioning of PID controllers it is widely used in control systems. But tuning the parameters of controller is still a formidable challenge. Also, once the parameters are well tuned satisfactory performance cannot be guaranteed due to process changes and aging effect, so a regular tuning is needed which takes considerable time and may not be practically effective. Most of the con...

The modified second-order sliding mode algorithm is used for controller tuning. Namely the modified suboptimal algorithm based test (modified SOT) and non-parametric tuning rules for proportional-integral-derivative (PID) controllers are presented in the paper. In the developed method of test and tuning, the idea of coordinated selection of the test parameters and the controller tuning paramete...

the paper is devoted to solution of some problems in nuclear power station generating unit intellectual control systems using genetic algorithms on the basis of control system model development, optimizations methods of their direct quality indices and improved integral quadratic estimates. some mathematical vector models were obtained for control system multicriterion quality indices with due ...

This paper presents a novel approach to control the speed of BLDC motor by using PID, Fuzzy, Neural Network and Anti-windup Controllers. The PID controllers is set to optimize the motor parameters such as rise time, peak time, Maximum peak overshoot and Settling time. The fuzzy controller adopts fuzzy logic to retune the PID parameters. Based on the mathematical model of BLDC Motor, novel adapt...

Frequency deviation is a one of major technical issue of a interconnected power system. In this paper an optimized Proportional-Integral-Derivative (PID) controller ios proposed for controlling the frequency deviation. To optimize the gain values of controller, the Adaptive Particle Swarm Optimization (APSO) algorithm is utilized. Tuning of controllers is done in order to get the gain values or...