Closed-loop Automatic Tuning Research Articles

Implementation and test of a new autotuning method for PID controllers of TITO processes

In this paper we present a new closed-loop automatic tuning methodology for decentralised proportional–integral–derivative (PID) controllers applied to two-inputs–two-outputs (TITO) non-singular processes. The main feature of the technique is the estimation of the process parameters by evaluating two closed-loop set-point step responses with two roughly tuned PID controllers already in place. Then, once a process model has been obtained, the PID controllers can be retuned by using any rule available in the literature and suitable for the application. Simulation examples, comparative results with other works and a real application on a neonatal incubator prototype are given to illustrate the methodology and to show its effectiveness.

Implementation of a new iterative learning control algorithm on real data

In this paper, a newly presented approach is proposed for closed-loop automatic tuning of a proportional integral derivative (PID) controller based on iterative learning control (ILC) algorithm. A modified ILC scheme iteratively changes the control signal by adjusting it. Once a satisfactory performance is achieved, a linear compensator is identified in the ILC behavior using casual relationship between the closed loop signals. This compensator is approximated by a PD controller which is used to tune the original PID controller. Results of implementing this approach presented on the experimental data of Damavand tokamak and are consistent with simulation outcome.

Closed-Loop Automatic Tuning Technique for an Event-Based PI Controller

A methodology for the closed-loop automatic tuning of an event-based PI controller is proposed in this article. In particular, the technique consists of performing two relay feedback experiments with a symmetric send-on-delta (possibly roughly tuned) PI controller already in place. The evaluation of the process variable allows for the determination of the parameters of a first-order-plus-dead-time process transfer function that is eventually employed to tune the controller parameters. Simulation and experimental results demonstrate the effectiveness of the methodology.

Neural PID Control Strategy for Networked Process Control

A new method with a two-layer hierarchy is presented based on a neural proportional-integral-derivative (PID) iterative learning method over the communication network for the closed-loop automatic tuning of a PID controller. It can enhance the performance of the well-known simple PID feedback control loop in the local field when real networked process control applied to systems with uncertain factors, such as external disturbance or randomly delayed measurements. The proposed PID iterative learning method is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed-loop systems. The convergence in the mean square sense is analysed for closed-loop networked control systems. To demonstrate the potential applications of the proposed strategies, a pressure-tank experiment is provided to show the usefulness and effectiveness of the proposed design method in network process control systems.

Simultaneous closed-loop automatic tuning method for cascade controllers

A new automatic tuning method for cascade control systems is presented. The technique consists in estimating the parameters of the primary and secondary process simultaneously by evaluating a closed-loop set-point step response. Then, based on the estimated model, the two proportional-integral-derivative controllers are tuned by applying an internal model control strategy. A performance assessment strategy can also be implemented in this context. Both self-regulating and non-self-regulating processes are considered. Simulation results show the effectiveness of the methodology.

Online automatic tuning of a proportional integral derivative controller based on an iterative learning control approach

A new approach is proposed for closed-loop automatic tuning of a proportional integral derivative (PID) controller based on an iterative learning control (ILC) approach. The method does not require the control loop to be detached for tuning, but it requires the input of a periodic reference signal. Such a reference signal can be the natural reference signal of the control system when it is used to execute a repetitive sequence, or it can be an excitation signal purely for tuning the PID controller. A modified ILC scheme iteratively changes the control signal by adjusting the reference signal only. Once a satisfactory performance is achieved, the PID controller is then tuned by fitting the controller to yield close input and output characteristics of the ILC component. Simulation and experimental results are furnished to illustrate the effectiveness of the proposed tuning method.

Iterative Learning Approach Toward Closed-Loop Automatic Tuning of PID Controllers

This paper proposes a new method for closed-loop automatic tuning of a proportional−integral−derivative (PID) controller based on a new iterative learning control (ILC) approach. The proposed approach is applicable to process control applications where there is usually a time-delay/lag phenomenon and where nonrepetitive step changes in the reference signal are far more common than repetitive ones assumed in most literature on ILC. The method does not require the control loop to be detached for tuning, but it requires the input of a periodic reference signal which can be specified by the user or derived from a relay feedback experiment. A modified ILC scheme iteratively changes the control signal by adjusting the reference signal only. The learning gain can be selected to satisfy a necessary and sufficient condition derived in the paper, based on the information available from the oscillations induced. Once a satisfactory performance is achieved, the PID controller is then tuned by fitting the controller to yield a fitting input and output characteristic of the ILC component. Simulation and experimental results are furnished to illustrate the effectiveness of the proposed tuning method.

Closed-loop automatic tuning of PID controller for nonlinear systems

In this paper, a robust control system is first proposed which is suitable for the control of a class of nonlinear systems. A parallel connection of a relay to a proportional integral derivative (PID) controller collectively forms the robust controller. The relay ensures robust control by providing a high feedback gain, but it also induces a control chattering phenomenon. Instead of viewing chattering as an undesirable yet inevitable feature, the chattering signals are used as natural excitation signals for identifying an equivalent PID controller using the recursive least squares algorithm. No other explicit input signal is required. Analysis is provided on the stability properties of the control scheme. Simulation results for the level control of fluid in a spherical tank using the scheme are presented.

A method for closed loop automatic tuning of PID controllers

Abstract A simple method for the automatic tuning of PID controllers in closed loop is proposed. A limit cycle is generated through a nonlinear feedback path from the process output to the controller reference signal. The frequency of this oscillation is above the crossover frequency and below the critical frequency of the loop transfer function. The amplitude and frequency of the oscillation are estimated and the control parameters are adjusted iteratively such that the closed loop transfer function from the controller reference to the process output attains a specified amplitude at the oscillation frequency.