DESIGN AND EXPERIMENTAL EVALUATION OF A DATA-DRIVEN PID CONTROLLER

Evolutionary tuning of PID parameters

Since most processes have nonlinearities, controller design schemes to deal with such systems are required. On the other hand, PID controllers have been widely used for process systems. Therefore, A new design scheme of PID controllers based on a memory-based (MB) modeling is proposed for nonlinear systems. According to the MB modeling method, some local models are automatically generated based on input/output data pairs of the controlled object stored in the database. The proposed scheme generates PID parameters using stored input/output data in the database. This scheme can adjust the PID parameters in an on-line manner even if the system has nonlinear properties. Finally, the effectiveness of the newly proposed control scheme is numerically evaluated on a simulation example.

Design of a Data-Driven PID Controller using Operating Data

Aiming at the nonlinear system, the method on PID controller optimization based on the data-driven technique is designed by combining the data-driven theory with the PID control algorithm. Firstly, a clustering method which preferentially determines the clustering center is used to cluster the collected data. And then on the basis of the euclidean distance, add the angle relationship. According to the current system input information, find the same class and similar groups of information. Secondly, the PID parameter values are obtained according to the local linear model. Finally, optimize the PID parameters and update the database. Through the simulation experiment, we can verify the effectiveness of the algorithm, while the system has better tracking performance and higher control accuracy.

In this paper, a new design scheme of performance-driven PID controllers whose PID parameters are adjusted based on a control performance criterion. Although a majority of studies have been focused on the derivation of the CPM index, the control parameter tuning method based on the CPM has been hardly studied. Conventional self-tuning controllers are tuned based on the variance of control errors and/or modeling errors. Few adaptive schemes use performance indice as tuning signals, which should be the main driving force in maintaining optimal operation, This paper develops a strategy for the tuning of an adaptive PID controller that is an approximation of a generalized minimum variance controller. The main driving signal for adaptive tuning is the degradation of the controller performance criterion. The effectiveness of the proposed method is numerically evaluated on two simulation examples.

Design of data-driven PID controllers with adaptive updating rules

PID control has been applied in many real systems. The control performance strongly depends on PID parameters. Although, some schemes for tuning PID parameters have been proposed, these schemes require system parameters which are estimated by system identification in order to calculate PID parameters. Moreover, most process systems are multivaliable systems which have interference of each respective inputs. On the other hand, data-oriented controller design schemes represented by VRFT or FRIT have received much attention in the last few years. These methods can calculate control parameters using closed loop data, and are expected to reduce computational costs. However, these methods have not been extended for MIMO systems. In this paper, a type of data-oriented multivariable PID controller using closed loop data is proposed. According to the proposed method, full matrices PID parameters which suppress mutual interference are calculated based on the implicit GMVC. Moreover, the control performance can be suitably adjusted by user-specified parameters. The effectiveness of the proposed method is numerically and experimentally evaluated.

Pneumatic Muscle Actuator (PMA) has a broad application prospect in soft robotics. However, PMA has highly nonlinear and hysteretic properties among force, displacement, and pressure, which lead to difficulty in accurate position control. A phenomenological model is developed to portray the hysteretic behavior of PMA. This phenomenological model consists of linear component and hysteretic component force. The latter component is described by Duhem model. An experimental apparatus is built up and sets of experimental data are acquired. Based on the experimental data, parameters of the model are identified. Validation of the model is performed. Then a novel cascade position PID controller is devised for a 1-DOF manipulator actuated by PMA. The outer loop of the controller is to cope with position control whilst the inner loop deals with pressure dynamics within PMA. To enhance the adaptability of the PID algorithm to the high nonlinearities of the manipulator, PID parameters are tuned online using RBF Neural Network. Experiments are performed and comparison between position response of RBF Neural Network based PID controller and that of classic PID controller demonstrates the effectiveness of the novel adaptive controller on the manipulator.

During the past few years, finite control set predictive current control (PCC) method has attracted more and more attention in research and industry applications. However, PCC method could be improved by considering two points. First, the current reference used in the cost function of PCC control scheme is usually produced by a proportional and integration speed controller. Confront with load torque and time-varying system parameters, it is a better solution to design a disturbance estimation based feed-forward compensated controller. In this way, the current reference could be generated faster and more accurate. Second, the PCC method is model-based method which means the accuracy of the model parameters are essential. In real system, time-varying parameters existed almost always. This paper investigates a generalized proportional integral observer based PCC approach for dealing with load torque disturbance, time-varying parameter uncertainties. The effectiveness of the proposed method has also been confirmed by simulation and a lab-constructed experimental prototype.

Adjusting the traditional PID controller parameters requires the expert’s experience. Sometimes, the traditional linear PID controller can't balance the dynamic and static performance of the system, and it can’t effectively control the object when the object parameters change. We propose a nonlinear PID controller, which uses state transition algorithm (STA) to tune the parameters of nonlinear PID controller, and uses data-driven (DD) to enable the system to identify different object parameters and adopt the corresponding controller. Simulation results show that the proposed controller effectively improves the control effect of the above problems.

This paper proposes a new offline learning algorithm for a data-driven proportional-integral-derivative (DD-PID) controller based on an extended fictitious reference iterative tuning (E-FRIT). PID controllers have been used in many process systems. However, it is difficult to maintain a good control performance using PID controllers with fixed control parameters because of the nonlinearity of the systems. The DD-PID controller has been proposed as an effective control system for nonlinear systems. This controller can tune its PID parameters adaptively at each equilibrium point of the system output. In the conventional DD-PID controller, the PID parameters are learned so as to minimize a criterion of the FRIT method. However, the FRIT method is based on minimization of the error in system output, and therefore, the criterion is insufficient for systems, such as chemical process systems, for which the stability of a closed loop system is essential. In order to solve this problem, the E-FRIT method has been proposed; a penalty for input variation is incorporated in the criterion for this method. In the present study, an offline learning rule of PID parameters was derived based on the E-FRIT criterion. According to the rule, the PID parameters that are taken into stability can be calculated. The effectiveness of the proposed method was evaluated by simulations of a polystyrene reactor system. The simulation confirmed that the proposed DD-PID controller yields better control result than the conventional learning method.

Design of process and control scheme for cyclohexanol production from cyclohexene using reactive distillation

For the stability problem of the active magnetic levitation ball, the application of the PID controller on the system is determined. Firstly, the linearized mathematical model of the active magnetic levitation ball system is established. Then the PID parameters are adjusted by the trial method. The influence of three PID parameters on the system is introduced, and a PID controller is designed for a set of actual systems. The performance is simulated and experimentally studied. The research results show that the active electromagnetic suspension ball system based on PID controller has high anti-interference performance and stability.

A Design of Self-Tuning PID Controller with a Time-Delay Compensator

Design and Experimental Evaluation of Self-Tuning PID Controller Using Evolutionary Computation