Multiloop PID controller design using partial least squares decoupling structure

Chen, Junghui; Cheng, Yuansheng; Yea, Yuezhi

doi:10.1007/bf02701481

Cited by 17 publications

(12 citation statements)

References 28 publications

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“…With these dynamic PLS models (DyPLS), many control schemes are applied. Kaspar and Ray proposed a proportion-integration-differentiation (PID) control scheme under this framework [12], and Chen and Cheng [14] designed multi-loop adaptive PID controllers based on a modified decoupling PLS framework. Hu et al [16,17] proposed a multi-loop internal model controller in the dynamic PLS framework and achieved better performance for disturbance rejection.…”

Section: Dynamic Pls Model Descriptionmentioning

confidence: 99%

Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

2017

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This paper develops an offset free tracking model predictive control based on a dynamic partial least square (PLS) framework. First, state space model is used as the inner model of PLS to describe the dynamic system, where subspace identification method is used to identify the inner model. Based on the obtained model, multiple independent model predictive control (MPC) controllers are designed. Due to the decoupling character of PLS, these controllers are running separately, which is suitable for distributed control framework. In addition, the increment of inner model output is considered in the cost function of MPC, which involves integral action in the controller. Hence, the offset free tracking performance is guaranteed. The results of an industry background simulation demonstrate the effectiveness of proposed method.

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Section: Dynamic Pls Model Descriptionmentioning

confidence: 99%

Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

2017

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“…It is apparent from literature that the single input single output (SISO) PID controller design is valuable for real process plant. The multiloop PID controller is also commonly [19][20][21] used in the multiple input multiple output process.…”

Section: July 2008mentioning

confidence: 99%

“…The following IMC filter is utilized (20) The resulting IMC controller becomes (21) Therefore, the ideal feedback controller is obtained as…”

Section: First Order Delay Integrating Process (Fodip)mentioning

confidence: 99%

PID controller design for integrating processes with time delay

Shamsuzzoha

Lee

2008

Korean J. Chem. Eng.

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A simple IMC-PID controller design technique is proposed on the basis of the IMC principle for two representative integrating processes with time delay. Further, it is extended to integrating processes with negative and positive zero as well. The proposed PID controller design method is mainly focused on the disturbance rejection, which causes the overshoot in the setpoint response, and a two-degree-of-freedom (2DOF) control structure is used to eliminate this overshoot. The simulation results show the superiority of the proposed tuning rule over other existing methods, when the controller is tuned to have the same robustness level by evaluating the peak of the maximum sensitivity (Ms). The closed loop time constant (λ) has only one user-defined tuning parameter in the proposed method. A guideline is suggested for the selection of λ for different robustness levels by evaluating the value of Ms over a wide range of θ/τ ratios.

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“…The results described here are for PIDNNC which tuned for tested at set-point 6 to 7 or 8 and these results are indicative of the results for controller tuned and tested at other set-point. Figure 8 the pH set-point change started at 7,8,7,6, and 7 for the end of pH value. The better to reach for pH output with PIDNNC in the first time pH reference (figure 8a).…”

Section: Set-point Tracking Ph Reference Changementioning

confidence: 99%

“…[2] devised a neural PID controller method in which a neural network is trained on the basis of control errors and tuning parameter of the PID controller can obtain. [3], [4], [5], [6] used self-tuning technique to adjust PID parameters on-line. However this was based on the assumption that process to be controlled is linear.…”

Section: Introductionmentioning

confidence: 99%

Self-Tuning Pid Neural Network Controller to Control Nonlinear Ph Neutralization in Waste Water Treatment

Cordova

Wijaya

2007

JTS

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89AbstractThe conventional PID control (linear) is popular control scheme that is used in almost pH control at waste water treatment process. pH model with respect to titration liquid flow rate has been known to be intrinsically difficult and nonlinear, especially when the process is conducted to non-linear range pH reference (pH 4-8), the settling time reach a long time. Therefore in this paper the nonlinear controller with self-tuning PID scheme is performed handle pH by training a neural-network based on backpropagation error signals. The pH process model is combination between CSTR (Continuous Stirred Tank Reactor) linear dynamic with H 3 PO 4 , HF, HCl, H 2 S flow rate and nonlinear static electro neutrality (wiener process model). The simulation result has a good and suitable performance under several tests (set-point and load change). The simulation result show the pH control system can follow the change of pH set-point and load with the average steady state error 0.00034. The settling time achieved at 50 second faster than conventional PID controller scheme.

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Multiloop PID controller design using partial least squares decoupling structure

Cited by 17 publications

References 28 publications

Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

PID controller design for integrating processes with time delay

Self-Tuning Pid Neural Network Controller to Control Nonlinear Ph Neutralization in Waste Water Treatment

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