Predictive PI Controller Research Articles

Predictive PI Control for Maximum Power Point Tracking and DC-Link Voltage Regulation of PMVG-Based Wind Turbine Systems

Predictive PI Control for Maximum Power Point Tracking and DC-Link Voltage Regulation of PMVG-Based Wind Turbine Systems

A temperature control algorithm for lithography machine based on generalized predictive control and BP neural network PI control

Temperature stability is a critical factor affecting the performance of the most subsystems in the lithography system, due to the high precision and sensitivity of system components to temperature variations. The temperature control system of the lithography machine is characterized by its large inertial constant, time delay characteristics, as well as susceptibility to multiple disturbances. The temperature control system of the lithography machine chiefly requires response speed, high accuracy, and stable and constant temperature control. The contribution of this study is not only avoiding complex precision modeling processes based on real-time parameter estimation and neural network self-tuning but also improving the performance of temperature control in real time under external disturbances. A novel adaptive algorithm with a cascade structure based on generalized predictive control (GPC) and backpropagation (BP) neural network proportional-integral (PI) control is successfully proposed for high accuracy temperature control of lithography machine with a large inertial constant, time delay, and multiple disturbances. In this study, firstly, the liquid circulating temperature control system is developed based on heat exchanger and heater. Secondly, an adaptive controller composed of GPC and BP neural network PI control is successfully proposed. A BP neural network is employed to enable the parameters of the PI controller to adjust in real time, and the mathematical model parameters of the control system are identified in real time by the least square method. Also, the performance of the proposed controller is evaluated comparing with conventional PI controller and GPC controller in terms of robustness and quantitative study of error analysis. Finally, the temperature stability and robustness of the temperature control system controlled with the proposed adaptive GPC-PI algorithm has been investigated by the simulation results carried out in different working scenarios. The simulation results show that the steady-state error from the proposed algorithm is less than 0.01°C under the action of disturbance input. It can effectively counteract the influence of environmental interference and time-varying system parameters. The results of the simulation experiment indicate that the proposed adaptive GPC and PI control algorithm exhibits significant advantages in terms of control accuracy, anti-interference ability, and robustness compared to the conventional control method.

Automated approach to design predictive PI control scheme for gain margin specification

Automated approach to design predictive PI control scheme for gain margin specification

An improved anti‐interference predictive PI for first order plus dead time processes

AbstractThe predictive PI control algorithm can handle time delay systems well, but still suffers from poor control and weak anti‐interference ability when facing large inertia and large lag links. This paper starts from the principle of predictive PI controller, equates it to Smith predictive controller, then combines Smith anti‐disturbance structure and proposes an improved predictive PI (IPPI). For the first‐order plus dead time (FOPDT) system simulation experiment, the experimental results show that the proposed controller has significantly improved the response speed and anti‐interference ability.

소둔산세 공정의 사행제어기 개발 및 측정지연 보상에 관한 연구

In this paper, we propose a control technology to prevent and reduce the center position error in the annealing and pickling process. A coupling control is developed to solve the problem of independent control using individual sensors. We design a receding horizon controller (RHC) to reduce the error and consider the limiting conditions of control input and state variables based on actual data. We also design a filtered predictive PI (FPPI) controller structure to reduce the measurement delay problem, disturbance and modeling error at high frequencies. The simulation results demonstrate the successful performance of the command following and the disturbance reduction by the proposed control scheme.

Modelling and Simulation of 1 MW Grid-Connected PV System Regulated by Sliding Mode Control, Model Predictive Control and PI Control

The design and modeling of a 1 MW grid-connected multistage PV system consisting of four equal power rated PV arrays are presented. Two nonlinear control methods, namely sliding mode control and discrete time model predictive control with finite switching states are adopted as the current control scheme of the inverter together with the parameter update for the LCL filter for each control mode. The proposed controllers are based on coordinate transformation of the variables and use decoupling to improve control performance. The dynamic and steady-state performances of these controllers have been thoroughly evaluated and compared with the conventional decoupled PI current control scheme in Matlab Simulink environment under fixed and changing weather conditions. It is observed that the performances of the controller schemes are satisfactory and they give almost similar steady-state performances under given test conditions, although there are little changes observed during system starting.

Optimal Energy Management Scheme of Battery Supercapacitor-Based Bidirectional Converter for DC Microgrid Applications

Because of the splendid front of sustainable energy reassets in a DC Microgrid, it is profoundly willing to variances in energy age. A hybrid energy storage system (HESS) which includes a battery and a supercapacitor (SC) is used to decrease in-built fluctuations. The two different characteristics of the battery and supercapacitor make it a great match for HESS applications. The HESS is connected to the DC Microgrid through a bidirectional converter, which allows energy to be exchanged between the battery and supercapacitor. This paper discusses a converter presenting an approach for a double-input bidirectional converter. Related to this, a regulator was designed for use as a voltage regulation in a DC Microgrid. The designed controllers accelerated PV generation and load disturbance DC link voltage restoration, in addition to effective power balancing among the battery and the SC. The conventional PI, proposed PI, and predictive PI control techniques are effectively validated using MATLAB Simulink. Experimental findings with low power have been used to validate the operation of the predictive PI control technique. The DC grid voltage profile showed substantial improvement while using the predictive PI control in comparison with the proposed and conventional PI control techniques in terms of setting time and maximum peak overshoot.

Lion swarm optimisation‐based tuning method for generalised predictive fractional‐order PI to control the speed of brushless direct current motor

In order to enhance the speed tracking performance of brushless direct current motor (BLDCM), a new generalised predictive fractional-order PI (GPFOPI) control algorithm ground on lion swarm optimisation (LSO-GPFOPI) is proposed in this paper. First, GPFOPI combines robust generalised predictive control with scalable fractional-order PI, whose optimal control law is obtained through the model parameters and the predicted speed reference values. Next, the lion swarm algorithm is utilised to optimally tune the three parameters K P , K I , λ of GPFOPI, so as to make them be optimised. Finally, the optimal control signal is input into the system of BLDCM to control its speed. To verify the effectiveness of LSO-GPFOPI, its performance is compared with other algorithms. The results show that LSO-GPFOPI has better response speed, speed tracking ability, anti-interference ability, robustness, and self-adaptability than other algorithms. Moreover, an experimental platform of the BLDCM drive system is built to verify the feasibility of the LSO-GPOPI algorithm, and the test results prove that LSO-GPOPI can guarantee the smooth and accurate operation of BLDCM.

Research on Cascaded Single Phase PFC Based on Predictive PI Control

In order to improve the charging speed and reduce the occupied volume of an electric vehicle charger, a single-phase boost power factor corrector (PFC) system with cascade CHB (cascaded H-bridge) topology was adopted. Due to the periodic fluctuation of single-phase AC input, there is a large double power frequency ripple component in the output voltage of an AC-DC converter. When capacitor voltage is used as output for feedback control, the control system has the characteristics of a non-minimum phase system. In light of these factors that affect the dynamic stability of the system, a control method is proposed to improve the dynamic characteristics of the system without affecting its steady-state performance. The predictive PI control strategy was adopted to predict the error input signal of the lag process to attenuate the jitter in the control system and improve the dynamic performance and anti-interference of the system. Finally, the feasibility of the scheme was verified by experiments.

PI Controller for Hybrid Biomass- Solar Photovoltaic- Wind in Microgrid: A Case Study of Mersing, Malaysia

Renewable energy has become an alternative energy to replace fossil fuels in electric power generation systems to alleviate environmental problems in the future. Alongside the shift to renewable energy, microgrid technology has also evolved to integrate renewable energy into the power generation and distribution system. In addition, the issue of voltage stability is also a vital factor in sustaining microgrids with renewable energy integration to ensure system stability. Reports in earlier studies have included three voltage control methods such as Model Predictive Control (MPC), PI controller, and negative feed-forward voltage control, implemented to ensure voltage stability of hybrid renewable energy microgrids. However, very a few research reports apply voltage control in hybrid biomass (BM)-solar photovoltaic (PV)-wind microgrid, choosing only to focus on energy management, economic analysis, and best sizing. For this reason, this main objective of this paper is integration of hybrid Biomass-Solar Photovoltaic-Wind off-grid microgrid comprising a PI controller as the voltage control of the system based on an actual input database at a location in a small rural town named Mersing in Malaysia. Additionally, this paper also intends to illustrate the implemented efforts to support the voltage stability of the system for the Mersing case study. A PI controller with harmonic filter voltage controller is implemented in this study to reduce the Total Harmonic Distortion (THD) percentage. The average power output for solar, wind, and biomass energy sources were 60kW, 20kW, and 750kW, respectively. Concurrently the measured THD voltage and current at each renewable energy and distribution line show a percentage below 10%. It is thus shown that a hybrid BM-Solar PV-wind microgrid is stable, especially for distribution lines, according to harmonic standards in the electricity supply application book by the local electricity provider Tenaga Nasional Berhad (TNB). Indeed, a PI controller with a harmonic filter has proven as an effective method for controlling the voltage instability in this study.

Modified Smith Predictor Based on H 2 and Predictive PI Control Strategy

In this paper, a new type of modified Smith predictor based on the H 2 and predictive PI control strategy is proposed. The modified Smith predictor not only has H 2 robust performance but also has a similar predictive PI control structure. By introducing a time delay term, the modified Smith predictor controller overcomes the shortcoming that the conventional control algorithm can only use the low-order approximation of time delay term to design the control algorithm. The modified Smith predictor controller’s output is related to the current system error and related to the output in a period before the controller. Simultaneously, the modified Smith predictor controller is applied to conventional process systems based on dynamic optimization estimation in the case study to show absolute superiority over the nonpredictive control method (such as the classical PID control method).

Data-Driven Model Predictive Control of Cz Silicon Single Crystal Growth Process With V/G Value Soft Measurement Model

The growth process of Czochralski (Cz) silicon single crystal is a dynamic time-varying system with nonlinearity, strong coupling, large hysteresis, and uncertain model. Traditional model-based control methods are difficult to achieve satisfactory crystal growth control effects, and it is difficult to ensure that the crystal quality meets the actual process requirements. Therefore, from the perspective of data-driven modeling and control, this paper proposes a model predictive control method for the crystal growth process with a V/G soft-sensing model for measuring crystal quality. First, because the V/G value to measure crystal quality is difficult to obtain directly, a hybrid variable weighted stacked autoencoder random forest (HVW-SAE-RF) soft-sensing model based on data-driven V/G value is established. Here, the HVW-SAE is used to extract the deep quality-related features of the measurable process data, and the RF is used for the regression prediction of the SAE output layer; Second, using the dual closed-loop control strategy, the inner loop is based on the HVW-SAE-RF soft-sensing model of the V/G value, and the predictive PI control method is used to control the V/G value closely related to the crystal quality within a reasonable range, and the outer loop based on real-time estimation of V/G values to achieve nonlinear model predictive control (NMPC) of crystal diameter; finally, the effectiveness of the proposed method is verified based on the industrial production process data of silicon single crystal.

Tuning Generalized Predictive PI controllers for process control applications

Predictive PI (PPI) controllers have demonstrated to exceed traditional PID controllers when they are applied to systems with long delays. This work proposes a new controller structure and tuning that we call Generalized Predictive PI (GPPI) controller which provides greater design flexibility than PI and PPI strategies. To realize a fair comparison, the design and tuning rules for discrete PI and PPI controllers were developed using optimal arguments based on the root-locus, for critically damped response before a step change in the reference. Experimental results, using industrial equipment, have illustrated the tuning methodology and the performance of the proposed controller under real conditions. Flow and water level process in a laboratory flume were considered. For these processes, First Order Plus Time Delay (FOPTD) models are used. The GPPI control results are encouraging, reducing the settling time plus a very small overshoot before step change in the reference regarding the PI and PPI strategies, up to 41.03% for the flow control loop and up to 54.21% for the level control loop. The discrete analysis of the strategies in the Z plane was performed, allowing for a direct translation to recursive equations that can then be programmed into a Programmable Logic Controller (PLC), other industrial controllers such as Distributed Control Systems (DSC), or microcontrollers, such as Arduino, Raspberry or FPGA. This is an important result, since it demonstrates that the increased complexity of the proposed controller does not hamper its implementation in industrial controller systems. In this work, we used a Rockwell ControlLogix \\protect \\relax \\special {t4ht=®} PLC with Structured Text programming language.

Design and experiential test of a model predictive path following control with adaptive preview for autonomous buses

This paper presents a hierarchical path following control framework for a two-axle autonomous bus, under which the active safety controller, the adaptive preview regulator as well as the predictive trajectory tracker are integrated as a whole system with highly effective performance. The framework is developed with two layers. The upper layer is designed to prevent the vehicle from sideslip and rollover by restricting the speed with an active safety controller. As the high computational load of optimal algorithm poses a great challenge for the real-time application in vehicle system, an adaptive preview regulator is, therefore, developed with an active combination of the PSO and SVM aiming for a more practical implementation. Back to the lower layer, the predictive trajectory controller and PI controller are proposed to acquire the steering angle with stability constrains to follow the reference path. The whole system with the proposed method is verified with both the simulation and experiments. A combined comparison simulation with four cases was carried out based on TruckMaker-Simulink joint platform using a highly-fidelity and full-car model in different scenarios. Compared with the case without preview and that with fixed preview, the tracking accuracy of the proposed control is improved by 44.97% and 36.12% respectively. The proposed method is tested in a real autonomous bus with acceptable errors compared with the simulation. In addition, the effectiveness of the proposed layered control framework and its real-time control capability is proved in real autonomous bus with satisfying performance under parameter uncertainties and external disturbances.

On Fractional Order Predictive PI Controller Design for a MIMO Reactive Distillation Process

Abstract In this paper, fractional-order predictive PID control scheme is applied in composition control of boiling fractions of a reactive distillation plant set up for the esterification reaction between acetic acid and ethanol. The controller shares similar structural features with Model-based Predictive Controller (MPC) as many attractive benefits of Dynamic Matrix Control (DMC) are retained such as constraint handling capability. However, it optimizes a different objective function formulated to achieve a more robust control action. These robust properties make it attractive to multivariable process control applications. Process’s state space model is assumed to be available and the model is augmented for prediction of future outputs. Thereafter, a structured cost function is defined which retains the design objective of fractional-order predictive PI controller. Optimisation of this cost function results in realising a near-optimal MIMO controller with reduced input control efforts. Simulation studies using Giwa’s reactive distillation column demonstrates better control performance over dynamic matrix control of the same system. It also rejects disturbances, both measured and unknown disturbances, better than Dynamic Matrix Control system under similar conditions. A major contribution of this paper is the development of a MIMO fractional order predictive PI controller for multivariable process control applications such as composition control of a reactive distillation column.

Fractional-Order Predictive PI Controller for Dead-Time Processes With Set-Point and Noise Filtering

In most of the industrial process plants, PI/PID controllers have been widely used because of its simple design, easy tuning, and operational advantages. However, the performance of these controllers degrades for the processes with long dead-time and variation in set-point. Up next, a PPI controller is designed based on the Smith predictor to handle dead-time processes by compensation technique, but it failed to achieve adequate performance in the presence of external noise, large disturbances, and higher-order systems. Furthermore, the model-based controllers structure is complex in nature and requires the exact model of the process with more tunable parameters. Therefore, in this research, a fractional-order predictive PI controller has been proposed for dead-time processes with added filtering abilities. The controller uses the dead-time compensation characteristics of the Smith predictor and the fractional-order controller’s robustness nature. For the high peak overshoot, external noise, and disturbance problems, a new set-point and noise filtering technique is proposed, and later it is compared with different conventional methods. In servo and regulatory operations, the proposed controller and filtering techniques produced optimal performance. Multiple real-time industrial process models are simulated with long dead-time to evaluate the proposed technique’s flexibility, set-point tracking, disturbance rejection, signal smoothing, and dead-time compensation capabilities.

Research on Variable Structure High Voltage Power Supply Control Strategy Based on Predictive PI control algorithm

The research and verification of a variable structure high voltage power supply unit and its control strategy are introduced. According to the mathematical modeling method, the variable structure system is linearized equivalent analysis and closed-loop system design. The actual parameter verification and theoretical verification of the system are carried out by means of experimental identification, and the research of subsequent control strategies is guided. According to the analysis results and the dynamic parameters of the power supply, a control strategy based on Predictive PI is proposed and simulated. In addition, in order to realize the actual verification of the power supply control algorithm, a power controller based on the ARM and FPGA architecture is designed to be applied to the system. Experiments show that the control strategy can solve the complex variation of the nonlinear and variable structure of the power supply under dynamic conditions, and ensure that the system completes the established parameter indicators.

Design of a Data-driven Predictive-PI Controller

Design of a Data-driven Predictive-PI Controller

Modular modeling of air-conditioning system with state-space method and graph theory

To improve the modeling efficiency of HVAC system, the modular models for HVAC components are to be established by using the state-space method in this study. The component modular models are used to establish a dynamic system model for a VAV (variable-air-volume) system, and the dynamic system model has been validated by transient response experiments. As an example of model application, the ARIMA-based predictive PI controller for room temperature has been investigated with the system model, which is shown to have the better control effect than the traditional PI controller, and this has been validated by experiments as well. The work in this study will be of significance to the dynamic modeling and control algorithm study of air conditioning systems.

Online Validation of Relay based Identification and Controller Design with an Anti-Reset Windup for a Binary Distillation Column

Sequential auto-tuning based model identification is a closed loop approach, which has an extensive benefit compared to conventional methods because of user defined manipulated gain to the process within the verge of instability. Model parameters estimation of multi input multi output process is challenging because of existence of multivariable interaction among the variables. In this paper ideal relay is used as a sequential basis for binary distillation column in real time. Obtaining sustained oscillations in conventional methods is based on trial and error, benefit of relay is that oscillations can be generated as scaling of user defined gains. Predictive PI control algorithm is implemented. Results depicts the efficiency of methodology and importance of anti-reset term in the algorithm.