Controller Parameter Tuning Method Research Articles

Control stability analysis of dynamic model with time delay for quadrotor UAV

For the first-order unstable object with time delay, the PD control parameter tuning method based on the traditional definition lacks consideration of the lower boundary of amplitude margin, resulting in a certain deviation between the result and the reality. In this paper, through analyzing the control loop of quadrotor UAV (QUAV), the integral link is moved from the controlled object to the controller, and the QUAV becomes a first-order unstable object, realizing the conversion of PD to PI in the hovering state. The system stability margin remains unchanged, and the position and attitude control objectives can be realized synchronously. By means of parameter setting method, the upper boundary of stability margin is obtained. Pade approximation is carried out for the time delay link, and then the lower margin of the amplitude margin is deduced according to Routh stability criterion, so as to obtain a more accurate reachable stability margin region. To avoid the high gain feedback of attitude angular velocity of QUAV, a numerical analytical setting formula for PD attitude control is derived to meet the requirements of gain and phase margin. The graph shows that the reachable region obtained by the strict definition of stability margin analysis decreases significantly. The proposed method can not only give the reachable region intuitively, but also give the tuning formula of the control parameters concisely, which has important engineering application value.

A Small-Signal Stability Analysis Method Based on Minimum Characteristic Locus and Its Application in Controller Parameter Tuning

A small-signal stability analysis method based on minimum characteristic locus (MCL) was recently proposed by the authors. The method is found to be particularly useful in analyzing various oscillation modes in a unified way. The present work continues this line of research. After providing a brief review of the basic principle of MCL method, the paper proceeds to show the mathematical relationship between the stability margin obtained using an MCL method, and that obtained by traditional modal analysis method. Next, a sensitivity analysis procedure is proposed to study quantitatively the effects of control parameters on the stability of the system. Third, the properties of the so-called exciter mode are explained in detail, in particular, explicit formulas of exciter mode frequency and power system stabilizer (PSS) critical gain are derived. Finally, taking PSS4B as an example, a controller parameter tuning method is proposed. The effectiveness and practicability of the proposed method are verified by case studies of Kundur's four-machine-two-area system and China's Yunnan power grid.

A Parameter Tuning Method of Adaptive Dynamic PID Controller for Time Delay Integral System

PID controllers are widely used in the control of integral systems. In the practical application of integral systems, the control effect is usually affected by the delay time, and at the same time, the large variation of the controlled integral parameters will also affect the control effect of the controller. This paper proposes an adaptive dynamic PID controller parameter tuning method suitable for time delay integral systems, considering the influence of parameters such as the voltage of the integral system, the variation range of the controlled object, and delay time, making the relationship between the parameters of the PID controller and the control object of the integral system more explicit. At the same time, this paper considers the tuning of the differential filter parameters in practical applications. A non-linear system with a 10-fold variation of the controlled integral parameters is simulated in Simulink, and the dynamic control of the non-linear system is realized using the method proposed in this paper, achieving a better control effect compared to the traditional control with constant PID parameters.

IK-SPSA-Based Performance Optimization Strategy for Steam Generator Level Control System of Nuclear Power Plant

The steam generator (SG) is a critical component of the steam supply system in the nuclear power plant (NPP). Hence, it is necessary to control the SG level well to ensure the stable operation of the NPPs. However, its dynamic level response process has significant nonlinearity (such as the ‘swell and shrinks’ effect) and time-varying properties. As most of the SG level control systems (SGLCS) are constructed based on the Proportional-Integral-Derivative (PID) controllers with fixed parameters, the controller parameters should be optimized to improve the performance of the SGLCS. However, traditional parameters tuning methods are generally experience-based, cumbersome, and time-consuming, and it is difficult to obtain the optimal parameters. To address the challenge, this study adopts a knowledge-informed simultaneous perturbation stochastic approximation (IK-SPSA) based on adjacent iteration points information to improve the performance of the SGLCS. Rather than the traditional controller parameter tuning method, the IK-SPSA method optimizes the control system directly by using measurements of control performance. The method’s efficiency lies in the following aspects. Firstly, with the help of historical information during the optimization process, the IK-SPSA can dynamically sense the current status of the optimization process. Secondly, it can accomplish the iteration step size tuning adaptively according to the optimization process’s current status, reducing the optimization cost. Thirdly, it has the stochastic characteristic of simultaneous perturbation, which gives it high optimization efficiency to optimize high dimensional controller parameters. Fourthly, it incorporates an intelligent termination control mechanism to accomplish optimization progress control. This mechanism could terminate the optimization process intelligently through historical iterative process information, avoiding unnecessary iterations. The optimization method can improve the stability, safety, and economy of SGLCS. The simulation results demonstrated the effectiveness and efficiency of the method.

Performance Optimization of a Steam Generator Level Control System via a Revised Simplex Search-Based Data-Driven Optimization Methodology

A Steam generator is a crucial device of a nuclear power plant. Control performance of the steam generator level control system is key to its normal operation. To improve its performance, the control system parameters should be optimized by utilizing a proper optimization method. Furthermore, the method’s efficiency is critical for its operability in the actual plant. However, the steam generator level process is a complex process, with high nonlinearity and time-varying properties. Traditional parameters tuning methods are experience-based, cumbersome, and time-consuming. To address the challenge, a systemic data-driven optimization methodology based on the model-free optimization with a revised simplex search method was proposed. Rather than the traditional controller parameter tuning method, this method optimizes the control system directly by using control performance measurements. To strengthen its efficiency, two critical modifications were incorporated into the traditional simplex search method to form a knowledge-informed simplex search based on historical gradient approximations. Firstly, with the help of the historical gradient approximations, the revised method could sense the optimization direction more accurately and accomplish the iteration step size tuning adaptively, significantly reducing the optimization cost. Secondly, a revised iteration termination control strategy was developed and integrated to monitor the optimization progress, which can promptly terminate the progress to avoid unnecessary iteration costs. The effectiveness and the efficiency of the revised method were demonstrated through simulation experiments.

Data-driven Estimated Iterative Feedback Tuning by One Shot Experimental Data

Iterative Feedback Tuning (IFT) is a simple approach to obtain a controller that achieves the target response. However, it requires many experiments to update the parameters. In this study, we propose a new control parameter tuning method based on IFT. As a main contribution of this study, the proposed method can reduce experiment the number of the iterated experiment for parameter tuning using the data-driven estimation. Finally, we show the usefulness of the proposed method used in experimental validation.

Active Disturbance Rejection Generalized Predictive Control of a Quadrotor UAV via Quantitative Feedback Theory

This paper investigates the design of a robust controller for a quadrotor unmanned aerial vehicle (UAV) system subject to parameter uncertainties and external disturbances. A new double closed-loop active disturbance rejection generalized predictive control (ADRC-GPC) is proposed for the trajectory tracking problem of the UAV. Considering the measurement noise, the ADRC-GPC control strategy can provide better dynamic performance and robustness against uncertainties and external disturbances than active disturbance rejection control (ADRC). Furthermore, a controller parameter tuning method for ADRC-GPC is proposed. In the framework of the classical two-degree-of-freedom equivalent model, the bandwidth of ESO is determined according to the parameter selection criterion. The criterion is a trade-off between the estimation accuracy and the immunity of measurement noise. In addition, the parameters of the ADRC-GPC controller are tuned by quantitative feedback theory to achieve the expected performance specifications. Finally, the proposed method is compared with several traditional control methods in the simulation experiments. The simulation results show that the proposed method has better set-point tracking performance, disturbance rejection performance, and robustness.

Computer Network Dynamic Balance Flow Distribution Based on Closed-Loop Particle Swarm Feedback Model

Based on the closed-loop particle swarm feedback model, this paper proposes a graphical method to analyze the stability of the computer network dynamic balance system. First, based on the second-order time delay system model of congestion control, the stability of the system is described by characteristic pseudopolynomials. Secondly, based on the inverse line, the stability of the system is verified by graphical analysis methods, and the PID controller parameter range that guarantees the stability of the system is obtained, and the relationship between the controller proportional gain boundary and the network characteristic parameters is analyzed. Then, based on the analysis of the basic particle swarm optimization algorithm, the particle swarm evolution formula is divided into two parts, its own factors and social factors, and the influence of each part on the evolution speed and position of the particle swarm is analyzed, and an improved particle swarm is proposed. Finally, according to the above analysis, we find the corresponding equation from the appropriate solution in turn, thereby designing a class of particle swarm optimization algorithm with fewer intermediate variables. In view of the system involved in the classical PID control parameter tuning method, the improved particle swarm algorithm is applied to the parameter tuning and optimization of the PID controller. During the experiment, the improved PSO-PID controller optimization algorithm was used in the random early detection algorithm of active queue management, the process of the improved algorithm was researched and designed, and the relevant performance of the improved algorithm was verified through simulation experiments.

Pre‐Filter Design for Iterative Controller Parameter Tuning Using Data‐Driven Minimum Variance Regulatory Controllers

Data‐driven minimum variance regulatory controllers directly tunes controller parameters using regulatory control data for improving disturbance attenuation properties. The approach is originally derived for one‐shot controller parameter tuning. That is followed by iterative methods by way of just repeating one‐shot tuning method. The present work considers such iterative controller parameter tuning method, and proposes a gradient‐based pre‐filter that makes the gradient vector of the data‐driven cost criterion have almost the same direction as one of the original model‐based cost criterion while the global minimizer of the cost criterion remains unchanged. The pre‐filter includes the feedback invariant polynomial in the denominator, which can be estimated from time‐series analysis of the closed‐output and the information of the input delay time. Finally, the effectiveness of the pre‐filter design is shown through a numerical example. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

High-performance state feedback controller for permanent magnet synchronous motor

Due to the outstanding characteristics of permanent magnet synchronous motor (PMSM), such as fast response speed, high torque and power density, it has been widely used in the automation industry. However, it remains a challenge to obtain high control performance caused by its dynamic complexity. In order to achieve favorable control performance, a novel state feedback control algorithm and parameter tuning method for permanent magnet synchronous motor is proposed in this paper. The development of the presented control method starts with the analyses of current state–space equation in rotor reference frame and then provides the design procedure of state feedback controller from the first-order to third-order system. An enhanced Proportional–Integral (PI) plus state feedback controller is designed, which includes the information of current, the error of current and the integral of the current error. The stability and convergence of the proposed control approach, as the extension of the conventional PI regulator, is mathematically justified in state feedback theory. The simulation and experimental results compared with the classical state feedback control method illustrate that the proposed PI plus state feedback control scheme can obtain better control performance in the presence of parameter changes and disturbance.

Double-loop control and intelligent parameter tuning for the temperature control system of a DBR semiconductor laser.

Aiming at lower startup power consumption, stronger thermal load adaptability, easier parameters adjustment, and higher parameter tuning efficiency for the temperature control system of a distributed Bragg reflector (DBR) semiconductor laser, this paper employs the double-loop control and intelligent parameter tuning methods. First, the thermal equivalent circuit model is established for the laser temperature control system, which has stronger thermal load adaptability than the traditional transfer function model. In order to improve the modeling speed and accuracy, a mean impact value (MIV) quantum particle swarm optimization (QPSO) intelligent algorithm is proposed to tune the model parameters. A double-loop temperature control system is set up on this basis. Then, the MIV-QPSO intelligent algorithm is used to tune the control parameters, which shortens the settling time, increases the tuning efficiency, and improves the temperature control effect. The feasibility and effectiveness of the proposed methods are verified through the MATLAB/Simulink simulation of the laser temperature control process.

Adaptive Voltage Feedback Controllers on Nonsalient Permanent Magnet Synchronous Machine

This article investigates the nonlinear behavior of the conventional voltage feedback flux-weakening control, i.e., the voltage magnitude feedback control on the nonsalient permanent magnet synchronous machine, and proposes an adaptive control parameter tuning method for the voltage feedback controller. Due to less voltage margin in the flux-weakening region, the current dynamic performance is degraded. This issue is more serious when the system operates in the overmodulation region and could affect the system's stability. Based on the designed voltage feedback controller, the system performance in the flux-weakening and overmodulation regions is further improved by utilizing the current and voltage reference modifiers. Consequently, the system can operate well in both linear and overmodulation regions with the specified scaling factor, which is beneficial to general-purpose applications. The viability of the proposed method is demonstrated by the experimental results.

Signal Synthesis Model Reference Adaptive Controller with Genetic Algorithm for a Control of Chemical Tank Reactor

Abstract Modelling and controlling of Continuous Stirred Tank Reactor (CSTR) is one of the significant problems in the process industry. Chemical reactions inside the CSTR depend on the reference value of the temperature. Design and implementation of suitable control device for such system is a challenge for researchers. This paper proposes the Model Reference Adaptive Control (MRAC) based control strategy as a solution to control problem of CSTR. An enhancement of Signal Synthesis MRAC scheme has been proposed in this study to improve the steady-state and transient-state performance of CSTR. Genetic Algorithm (GA) based controller parameter tuning method is employed to obtain the optimal performance of the controller. This paper presents the design and implementation of conventional Proportional–Integral–Derivative (PID) tuned with Ziegler–Nichols (ZN) tuning method, PID tuned with GA, MRAC, and GA-MRAC for CSTR. Detailed comparison based on simulation studies is also presented to show the improved transient and steady state response with GA-based improved MRAC scheme.

A New Design Method for PI‐PD Control of Unstable Fractional‐Order System with Time Delay

In this paper, a practical PI‐PD controller parameter tuning method is proposed, which uses the incenter of the triangle and the Fermat point of the convex polygon to optimize the PI‐PD controller. Combined with the stability boundary locus method, the PI‐PD controller parameters that can ensure stability for the unstable fractional‐order system with time delay are obtained. Firstly, the parameters of the inner‐loop PD controller are determined by the centre coordinates of the CSR in the kd − kf plane. Secondly, a new graphical method is used to calculate the parameters of the PI controller, in which Fermat points in the CSR of (kp − ki) plane are selected. Furthermore, the method is extended to uncertain systems, and the PI‐PD controller parameters are obtained by using the proposed method through common stable region of all stable regions. The proposed graphical method not only ensures the stability of the closed‐loop system but also avoids the complicated optimization calculations. The superior control performance of this method is illustrated by simulation.

Design of Rated Power Control Strategy of Wind Turbine Based on Particle Swarm Optimization

In view of the reduction of wind turbine operating efficiency caused by wind speed measurement errors, this paper proposes a rated power control strategy for wind turbines for practical applications; in order to solve the problem that the pitch PI controller parameters of wind turbines are not easy to calculate and tune in the process of design and optimization, this paper proposes a complete set of methods for parameter tuning of pitch control PI controllers for wind turbines. This paper establishes a Bladed-Matlab co-simulation platform, uses Bladed software to build a complete model of a 2MW wind turbine, and designs a rated power control strategy for wind turbines. After the wind turbine model is linearized at different wind speed points by applying Bladed software, the linearized model for PI parameter tuning is obtained, and then the optimal PI parameters for each wind speed point are set by using particle swarm algorithm. According to the variation law between the PI parameters and the pitch angle obtained before, the PI parameters are adjusted adaptively by variable gain PI control. The simulation results show the superiority of the control strategy and parameter tuning method used in this paper.

Design of a Data-Driven Controller for a Spiral Heat Exchanger

A data-driven proportional-integral-derivative (DD-PID) controller has been proposed as an effective controller for nonlinear systems. The DD-PID controller can tune the PID parameters adaptively at each equilibrium point. In order to train the PID parameters in a database, an offline learning algorithm based on a fictitious reference iterative tuning (FRIT) method was established. This method can compute the PID parameters by using a set of operating data. However, the FRIT method is a control parameter tuning method that is only based on the minimization of the system output in its criterion; therefore, the criterion is insufficient for systems in which the stability of a closed-loop system is important such as chemical process systems because sometimes the sensitivity of an obtained controller becomes high. In order to solve this problem, an extended FRIT (E-FRIT) method that penalizes the input variation in its criterion has been proposed. In this method, the PID parameters that are taken into stability can be calculated. The effectiveness of the proposed method is evaluated by an experimental result of a spiral heat exchanger.

CARMAモデルにおける未知な外乱特性に対するデータ駆動型一般化最小分散制御

This paper proposes a data-driven control parameter tuning method based on generalized minimum variance (GMV) evaluation in regulatory control. The proposed method can perform with no need for plant characteristics nor disturbance ones. For GMV control, this paper introduces a data-driven variance criterion which consists of the input and output data generated by stochastic disturbance. The control parameters are derived by using system parameters as optimization variables. Advantages of the method include applicability to routine operating data, which brings that additive experiments are not required. The efficiency of the method is demonstrated through numerical examples.

Control of the 2-degree-of-freedom servo system with iterative feedback tuning

In this article, the controller parameter tuning method of a large inertia 2-degree-of-freedom motion mechanism is presented. The proportional–integral controller is adopted to implement the system control. For the reason of load coupling of the two freedoms, fixed proportional–integral parameters cannot always get satisfied control performance. Based on this problem, the iterative feedback tuning method is applied to tune the controller parameters. This method makes the system output error decrease along the negative gradient direction, and the system cost function will reach local minimum after a few iterations. In order to improve the convergence speed of the iterative process, a gold ratio–based method is proposed to speed up the convergence of the iteration process. The simulation and comparison experiments are performed on the azimuth freedom, and the results show that the iterative feedback tuning method can achieve much higher control performance compared to classical tuning method, and it has the similar effect to the adaptive robust method.

The fractional order PI control for an energy saving electro-hydraulic system

In an electro-hydraulic system (EHS), the throttling phenomenon of the hydraulic valve leads to the problem of low utilization efficiency of hydraulic energy and severe increases in temperature. To alleviate this problem, this paper presents a type of one-chamber-controlled hydraulic circuit. In some applications where elastic load is dominant, this hydraulic circuit can achieve a significant energy-saving effect. For a valve-controlled system, the orifice non-linearity and the slowly varying parameter significantly influence the control performance of the electro-hydraulic system. With this aim in mind, the orifice compensation method is proposed to deal with the orifice non-linearity. Based on the compensation, the fractional order proportional–integral (FOPI) controller is adopted to deal with the problem of fluid parameter variation. In the controller designing process, this paper proposes a controller parameter tuning method based on system frequency characteristic data. Simulation and experiment results show that the strategy presented in this paper can reduce the energy losses dramatically and, at the same time, the control performance of electro-hydraulic system can be guaranteed.

Parameters Tuning via Simplex-Search based Model-Free Optimization for the Steam Generator Level Control

Control performance is critical to a control system. To improve the performance of the steam generator level control system, the control system parameters need to be optimized. Traditional parameters tuning methods, such as trial and error and Design of Experiments etc., are usually experience-based, cumbersome and time-consuming. To address the above inefficiencies, in this paper, the simplex-search based Model-Free Optimization (MFO) has been proposed to search for the optimal control system parameters. The optimized parameters will be gained to maximize the system’s control performance. Rather than traditional controller parameter tuning method, this method optimizes the control system by directly using measurements of control performance. An example of the PID parameters tuning for the steam generator level control was illustrated. The efficiency and the effectiveness of the Simplex-search based Model-Free Optimization – based control parameters tuning methodology has been verified through simulation experiments.