A parameter space approach to constrained variance PID controller design

Dickinson, Paul; Shenton, A.T.

doi:10.1016/j.automatica.2008.10.030

Cited by 12 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section investigates formulations (11) and (15) to derive the controller parameters. The quality of the PID parameters obtained with SQP/IIPM algorithm was compared with that obtained using the classical gain and GPM and ZN method.…”

Section: Simulation Of Optimal Pid Tuningmentioning

confidence: 99%

A novel PID controller tuning method based on optimization technique

Liang

Ayad

2010

J. Cent. South Univ. Technol.

View full text Add to dashboard Cite

An approach for parameter estimation of proportional-integral-derivative (PID) control system using a new nonlinear programming (NLP) algorithm was proposed. SQP/IIPM algorithm is a sequential quadratic programming (SQP) based algorithm that derives its search directions by solving quadratic programming (QP) subproblems via an infeasible interior point method (IIPM) and evaluates step length adaptively via a simple line search and/or a quadratic search algorithm depending on the termination of the IIPM solver. The task of tuning PI/PID parameters for the first-and second-order systems was modeled as constrained NLP problem. SQP/IIPM algorithm was applied to determining the optimum parameters for the PI/PID control systems. To assess the performance of the proposed method, a Matlab simulation of PID controller tuning was conducted to compare the proposed SQP/IIPM algorithm with the gain and phase margin (GPM) method and Ziegler-Nichols (ZN) method. The results reveal that, for both step and impulse response tests, the PI/PID controller using SQP/IIPM optimization algorithm consistently reduce rise time, settling-time and remarkably lower overshoot compared to GPM and ZN methods, and the proposed method improves the robustness and effectiveness of numerical optimization of PID control systems.

show abstract

Section: Simulation Of Optimal Pid Tuningmentioning

confidence: 99%

A novel PID controller tuning method based on optimization technique

Liang

Ayad

2010

J. Cent. South Univ. Technol.

View full text Add to dashboard Cite

show abstract

“…The PID design for multivariable systems used in this paper is not unique. It is possible to use other techniques such as Dickinson and Shenton (2009); Soylemez et al (2003); Toscano and Lyonnet (2009) to derive a robust PID gain before applying it into MPC.…”

Section: Pid Controllermentioning

confidence: 99%

From Parametric Model-based Optimization to robust PID Gain Scheduling

Nguyen¹,

Tan²

2013

Preprint

View full text Add to dashboard Cite

In chemical process applications, model predictive control (MPC) effectively deals with input and state constraints during transient operations. However, industrial PID controllers directly manipulates the actuators, so they play the key role in small perturbation robustness. This paper considers the problem of augmenting the commonplace PID with the constraint handling and optimization functionalities of MPC. First, we review the MPC framework, which employs a linear feedback gain in its unconstrained region. This linear gain can be any preexisting multi-loop PID design, or based on the two stabilizing PI/PID designs for multivariable systems proposed in the paper. The resulting controller is a feedforward PID mapping, a straightforward form without the need of tuning PID to fit an optimal input. The parametrized solution of MPC under constraints further leverages a familiar PID gain scheduling structure. Steady state robustness is achieved along with the PID design so that additional robustness analysis is avoided.

show abstract

“…With the development of modern control theory, previous researchers tried to combine PID with advanced control algorithms to overcome the abovementioned defciencies of traditional PID controller and achieve better control performance for diesel engine. Tese applications included fuzzy-PID controller, genetic-PID controller, NN-PID controller, and so on [13,14]. As shown in Figure 1, fuzzy-PID controller designed by Xiong et al [15], whose PID parameters were adjusted by fuzzy logic to improve control performance, was applied on engine speed control.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Control with Active Disturbance Rejection Algorithm for a Diesel Engine

Ding

Tu³

et al. 2023

Complexity

View full text Add to dashboard Cite

To improve the speed control performance of a diesel engine, the active disturbance rejection controller (ADRC) is designed by adding an arranging transition process to the traditional PID controller, extracting differential signals reasonably, and adopting nonlinear combination process to error signals. The ADRC is composed of a tracking differentiator (TD), an extended state observer (ESO), and a nonlinear state error feedback (NLSEF). Such constructed ADRC can adapt to the strong nonlinear and complicate working conditions of diesel engine for power generation. A simulation model of the diesel engine is built to verify the proposed ADRC, and simulation results are in good accordance with the experimental results. To further validate the advantages of the designed ADRC, the traditional PID is improved by TD. Meanwhile, performance comparisons are carried out between traditional PID controller, improved PID controller, and ADRC. Results present that the improved PID controller can achieve a better control performance than that of the traditional PID controller both in steady state and transient state. However, the ADRC can further improve the speed control performance evidently on the basis of the improved PID controller.

show abstract

A parameter space approach to constrained variance PID controller design

Cited by 12 publications

References 8 publications

A novel PID controller tuning method based on optimization technique

A novel PID controller tuning method based on optimization technique

From Parametric Model-based Optimization to robust PID Gain Scheduling

Model-Based Control with Active Disturbance Rejection Algorithm for a Diesel Engine

Contact Info

Product

Resources

About