Explicit Model-Based Real PID Tuning for Efficient Load Disturbance Rejection

Leva, Alberto; Papadopoulos, Alessandro Vittorio; Seva, Silvano; Cimino, Chiara

doi:10.1021/acs.iecr.9b04198

Cited by 4 publications

(6 citation statements)

References 49 publications

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“…32 OV of a control loop in the CPA problem is also a direct measurement of the disturbance rejection performance and when the disturbance is a zero mean white noise, the result calculated by eq 8 is consistent with the magnitude of the nominal disturbance-to-output frequency response. 32 However, few studies have designed objective functions taking into account it. Here, a new multiobjective function considering both OV and IAE is designed to achieve the trade-off between the disturbance rejection and setpoint tracking performance, which is described as follows…”

Section: Pid Tuning Based On a New Multiobjective Functionmentioning

confidence: 55%

“…Many measurement criteria have been proposed for this performance in literature to tune the PID controller, such as RDR, the magnitude of the nominal disturbance-to-output frequency response . OV of a control loop in the CPA problem is also a direct measurement of the disturbance rejection performance and when the disturbance is a zero mean white noise, the result calculated by eq is consistent with the magnitude of the nominal disturbance-to-output frequency response . However, few studies have designed objective functions taking into account it.…”

Section: Achievable Performance and Pid Tuningmentioning

confidence: 99%

“…Shamsuzzoha and Lee designed optimum IMC filter structures for the PID controller and then tuned it with the IMC–PID method to improve the disturbance rejection capability. Leva et al tuned the PID controller to minimize the magnitude of the nominal disturbance-to-output frequency response to obtain excellent disturbance rejection performance, and, in the meantime, to avoid the peak and plateau. Vrančić et al proposed the disturbance rejection magnitude optimum method based on the magnitude optimum (MO) method to enhance the disturbance rejection capability of the PI controller and the PID controller, and a two-degrees-of-freedom (2DOF) structure with a setpoint filter is used to retain the tracking performance.…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective Tuning and Performance Assessment of PID Using Teaching–Learning-Based Optimization

Zhang

Dong

et al. 2021

ACS Omega

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There have been many studies on the optimal tuning and control performance assessment (CPA) of the PID controller. In the optimal tuning, the trade-off between the setpoint tracking and the disturbance rejection performance is a challenge. Minimum output variance (MOV) is very widely used as a benchmark for CPA of PID, but it is difficult to be observed due to the non-convex optimization problem. In this paper, a new multiobjective function, considering both the OV in the CPA problem and integral of absolute error, is proposed to tune PID for this trade-off. The CPA-related non-convex problem and tuning-related multiobjective problem are solved by teaching–learning-based optimization, which guarantees a tighter lower bound for MOV due to the excellent capability of local optima avoidance and has higher computational efficiency due to the low complexity. The numerical examples of CPA problems show that the algorithm can generate better MOV than existing methods with less calculation time. The relationship between the weight of the multiobjective function and the performance, including setpoint tracking, stochastic and step disturbance rejection, is revealed by simulation results of the tuning method applied to two temperature control systems. The proper adjustment of the weight with a multistage strategy can achieve the trade-off to obtain excellent setpoint tracking performance in the initial stage and satisfying disturbance rejection performance in the steady stage.

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Section: Pid Tuning Based On a New Multiobjective Functionmentioning

confidence: 55%

Section: Achievable Performance and Pid Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiobjective Tuning and Performance Assessment of PID Using Teaching–Learning-Based Optimization

Zhang

Dong

et al. 2021

ACS Omega

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“…Internal Model Control (IMC) has been used as an alternative for designing and tuning PID controllers since 1980s [30]. It has resulted to be of particular interest in industry together with the PID algorithm [32], since the equations for the controller's parameters can be obtained from the transfer function of the process and the desired behavior of the closed-loop response; in most cases, only the closed-loop time constant is required as the user-defined tuning parameter, considering an appropriate trade-off between performance and robustness [20,[33][34][35][36][37]. Additional works, regarding IMC, that have been developed more recently can be found in [38][39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

PID Tuning Method Based on IMC for Inverse-Response Second-Order Plus Dead Time Processes

et al. 2020

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This work addresses a set of tuning rules for PID controllers based on Internal Model Control (IMC) for inverse-response second-order systems with dead time. The transfer function, and some time-response characteristics for such systems are first described. Then, the IMC-based methodology is developed by using an optimization objective function that mixes performance and robustness. A correlation that minimizes the objective function and that allows the user to compute the controller’s tuning parameter is found. The obtained expressions are mathematically simple, which facilitate their application in a ten-step systematic methodology. Finally, the proposed tuning method is compared to other well-known tuning rules that have been reported in literature, for a wide range of parameters of the process. The performance achieved with the proposed method is very good not only for disturbance rejection but for set-point tracking, when considering a wide-range of parameters of the process’ transfer function.

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“…Further, Khodja used particle swarm optimization to tune PID attitude stabilization of a quadrotor [25]. Leva raised the explicit model-based real PID tuning for efficient load disturbance rejection [26]. A mean for PID tuning based on the neutrosophic similarity measure was introduced by Can [27].…”

Section: Introductionmentioning

confidence: 99%

Global Asymptotic Nonlinear PID Control With a New Generalized Saturation Function

Niu

Qu²

2020

IEEE Access

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In order to solve the problem of the strict condition of traditional saturation function, a new generalized saturation function was proposed and applied in nonlinear PID (Proportion-Integration-Differentiation) control laws, which consisted of linear D + nonlinear PI and linear PD + nonlinear PI. The new generalized saturation function has powerful reaction near the equilibrium point, and has the capability to make the control converge to the equilibrium point swiftly. The global asymptotic stability condition of nonlinear PID control laws were derived by employing Lyapunov's method and LaSalle's invariance principle. In order to improve the accuracy of nonlinear PID control laws, time integration of the absolute value of position tracking error and time integration of the absolute value of torque error were chosen as the objective functions. Global asymptotic stability conditions and rated driving torque of each motor were set as the constraint conditions. Nonlinear PID controller parameters were tuned by employing multi-objective genetic algorithm, non-dominated sorted genetic algorithm-II (NSGA-II). Compared with the optimization results of nonlinear PID with traditional saturation function, the accuracy of position tracking using the proposed method was improved by nearly one order of magnitude. The new generalized saturation functions with minimum time integration of position tracking error were selected to study the robustness of the nonlinear PID controller in modeling uncertainty, input torque disturbance, and noise. The position tracking accuracy of the proposed method compared to those of the traditional PID controller and nonlinear PID controller with traditional saturation function was improved by nearly two orders of magnitude and one order of magnitude, respectively. The introduced saturation function significantly improves position tracking accuracy and robustness of the nonlinear PID controller.

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Explicit Model-Based Real PID Tuning for Efficient Load Disturbance Rejection

Cited by 4 publications

References 49 publications

Multiobjective Tuning and Performance Assessment of PID Using Teaching–Learning-Based Optimization

Multiobjective Tuning and Performance Assessment of PID Using Teaching–Learning-Based Optimization

PID Tuning Method Based on IMC for Inverse-Response Second-Order Plus Dead Time Processes

Global Asymptotic Nonlinear PID Control With a New Generalized Saturation Function

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