There are several methods for tuning a PID controller. This paper takes a qualitative look at three common methods, with comparisons of accuracy and effectiveness. These three methods include a guided Trial and Error method, the Ziegler-Nichols method, and the Cohen-Coon method. For an exceptionally responsive system the Trial and Error method is often used after the Ziegler-Nichols or Cohen-Co...

Identification based PID tuning is studied. The proposed approach consists of the identification of linear or nonlinear process model and model based control design. The identification test can be performed in both open loop and closed-loop. The so-called ASYM method is used to solve the identification problem. The method identifies a low order process model with a quantification of model error...

The proportional-integral-derivative (PID) control structures have been widely used in industrial applications due to their design/structure simplicity and inexpensive cost. The success of the PID controllers depends on the appropriate choice of their parameters. In practice, tuning the PID parameters/gains is usually realized by classical, trial-and-error approaches, and experienced human expe...

A new algorithm of PID controller tuning is presented in this paper. It is well known that there have been introduced many techniques for PID controller tuning, both theoretical and experimental ones. However, this algorithm is suitable especially for highly nonlinear processes. It uses a model of the controlled process in the shape of piecewise-linear neural network which is linearized continu...

This paper present a new method to determine the optimal tuning of the PID controller parameter of an AVR system using Modified particle swarm optimization (MPSO) algorithm. the AVR is not robust to variations of the power system parameters therefore, it is necessary to use PID controller To increase the stability and performance of the AVR system. Fast tuning of optimum PID controller paramete...

Since fuzzy controllers are nonlinear, it is more difficult to set the controller gains compared to proportional-integral-derivative (PID) controllers. This research paper proposes a design procedure and a tuning procedure that carries tuning rules from the PID domain over to fuzzy single-loop controllers. The idea is to start with a tuned, conventional PID controller, replace it with an equiva...

In the paper, a new multivariable self-tuning PID controller design scheme is proposed which has a static matrix precompensator to reduce the interaction terms of the process transfer function matrix adjusted by an online estimator. The p £ p precompensated multivariate system is then controlled via ‘p’ univariate self-tuning PID controllers whose parameters are adjusted by a second identifier ...

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 ...

A Proportional-Integral-Derivative (PID) control is the most commonly used algorithm in industrial control systems. With its three term functionality providing regulation to both transient and steady state responses, PID control offers the simplest and most efficient solution to many industrial control problems. Tuning of PID gain parameters continuous to be important as these parameters has gr...

This chapter presents the design, development and implementation of a novel proposed online-tuning Gain Scheduling Dynamic Neural PID (DNN-PID) Controller using neural network suitable for real-time manipulator control applications. The unique feature of the novel DNN-PID controller is that it has highly simple and dynamic self-organizing structure, fast online-tuning speed, good generalization...