Generalized Predictive Control Research Articles

A New Grey Wolf Optimizer Tuned Extended Generalized Predictive Control for Distillation Process.

The distillation process plays an essential role in the petrochemical industry. However, the high-purity distillation column has complicated dynamic characteristics such as strong coupling and large time delay. To control the distillation column accurately, we proposed an extended generalized predictive control (EGPC) method inspired by the principles of extended state observer and proportional-integral-type generalized predictive control method; the proposed EGPC can adaptively compensate the system for the effects of coupling and model mismatch online and performs well in controlling time-delay systems. The strong coupling of the distillation column needs fast control, and the large time delay requires soft control. To balance the requirement for fast and soft control at the same time, a grey wolf optimizer with reverse learning and adaptive leaders number strategies (RAGWO) was proposed to tune the parameters of EGPC, and these strategies enable RAGWO to have a better initial population and improve its exploitation and exploration ability. The benchmark test results indicate that the RAGWO outperforms the existing optimizers for most of the selected benchmark functions. Extensive simulations show that the proposed method in terms of fluctuation and response time is superior to other methods for controlling the distillation process.

Generalized predictive control application scheme for nonlinear hydro-turbine regulation system: Based on a precise novel control structure

Generalized predictive control (GPC), which has excellent control performance and robustness, is only applicable to the control object that can be expressed in the transfer function and is difficult to be directly applied to the hydraulic turbine regulation system (HTRS) that contain extremely complex nonlinearity. Therefore, considering the practical application of GPC in a hydropower station containing the Francis turbine, the double models, real-time feedback linearization module, and a nonlinear characteristic compensator are designed, and a generalized predictive control strategy combining these modules (GPC-DM-RFLM-NCC) is proposed. Firstly, the salp swarm algorithm (SSA) is improved based on the chaotic mapping and Levy flight, an accurate nonlinear hydraulic turbine model is constructed based on the backpropagation neural network (BPNN) and improved SSA (ISSA), and an HTRS simulation platform with nonlinear characteristics is built by combining modules such as water diversion system, servo system, and generator. Then, the irrationality of the commonly used linear hydraulic turbine models in HTRS dynamic process simulation is verified through quantitative calculation. Furthermore, by combining the real-time feedback linearization method and ISSA-based nonlinear characteristic compensator, the GPC-DM-RFLM-NCC is proposed. Finally, the HTRS simulation platform, which consists of GPC, double models, real-time feedback linearization module, and nonlinear characteristic compensator, is built, and the applicability, reliability, and excellent robustness of the proposed GPC-DM-RFLM-NCC are verified by the real data of a hydropower station.

Driving an asynchronous motor powered by PV‐MPPT using equivalent RST controller of fractional order PI and IP, based on generalized predictive control

AbstractThe article introduces a new model of a Reference Signal Tracking (RST) controller for the fractional order quantities. The controller is applied to an indirect field‐oriented control (IFOC) system used for the speed control of an asynchronous motor powered by a photovoltaic (PV) generator with a maximum power point tracking (MPPT) algorithm. This model utilizes two fractional order controllers: the fractional order proportional‐integral (FOPI) regulator and the fractional‐order integral‐proportional (FOIP) regulator. These controllers are used with the generalized predictive control (GPC) technique. The first step in the approach is to derive the equivalent digital RST controller's model from the FOPI and FOIP controller's transfer functions. The GPC technique converts the continuous‐time FOPI (and FOIP) controller into a discrete‐time version. This conversion ensures a fast response and effective disturbance rejection. Simulation tests are conducted to analyze the rotor speed and stator current ripples to evaluate the performance of the proposed method. The results demonstrate the effectiveness of the introduced scheme in achieving improved control performance in terms of response speed and disturbance rejection. The article presents a modified RST controller model based on the fractional order approach applied to an IFOC system for motor speed control driven by a photovoltaic generator. Using FOPI, FOIP controllers, and GPC contributes to enhanced control performance, as evidenced by the simulation results.

A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles

Currently, for years, unmanned aerial vehicles have been widely applied in a comprehensive realm. By enhancing computer photography and artificial intelligence, it can automatically discriminate against environmental objectives and detect events that occur in the real scene. The application of collaborative unmanned aerial vehicles will offer diverse interpretations which support a multiperspective view of the scene. Due to diverse interpretations of unmanned aerial vehicles usually deviates, thus, unmanned aerial vehicles require a consensus interpretation for the scenario. To previous purposes, this study presents an original consensus-based method to pilot multi-unmanned aerial vehicle systems for achieving consensus on their observation as well as constructing a group situation-based depiction of the scenario. Further, a fuzzy neural network generalized prediction control system known as a recurrent self-evolving fuzzy neural network is mainly used to ensure stability through the use of a descending gradient online learning rule. At the same time, users can think along the lines of evolutionary biological design. Unmanned aerial vehicles can be modeled as system experts for solving group problems that require the definition of conditions that best describe the scene. First, this method allows each unmanned aerial vehicle to set high-level conditions for detection events by aggregating events based on fuzzy information. These aggregated events are modeled by a fuzzy system ontology, which allows each unmanned aerial vehicle to report its preferences in conditions. Therefore, the interpretation of each drone is compressed to achieve a collective interpretation of the state. The final polls, consent and affinity polls confirmed the final decision group’s reliability ratings. The rated consensus indicates how well the collective interpretation of the scene matches each drone’s point of view.

Advanced embedded generalized predictive controller based on fuzzy gain scheduling for agricultural sprayers with dead zone nonlinearities

Variable rate application of pesticides in agriculture can improve pest control and also increase food production. Nevertheless, incorrect spraying poses risks to the environment and human health, as well as may increase the total cost of production. Nowadays, it is quite known the importance of innovation in techniques and technologies to improve the spraying process in a variable rate application for pest control. This work presents a generalized predictive control (GPC) strategy to cope with nonlinearities. An extension of the stability analysis for constrained GPC controller for infinite horizons is also developed, which guarantees the stability of a fuzzy GPC for a limited variation of its tuning parameters λ and δ. Results show the usefulness in adding a fuzzy logic system, to cope with nonlinearities, leading to a conception of an advanced fuzzy GPC for a limited variation of its tuning parameters.

Generalized Predictive Control of the Active and Reactive Stator Powers of the DFIG for Wind Energy Generation

This paper proposes a Generalized Predictive Control (GPC) strategy for Wind Power Generation (WPG) based on a doubly fed induction generator (DFIG). The objective is to study and apply a robust active and reactive power control strategy based on GPC of the DFIG, this is likely to optimize the energy production and improve the quality of the energy produced. In order to maximize the amount of WPG taken even when the turbine is uncertain or the wind speed varies abruptly, the design is built utilizing the Maximum Power Point Tracking (MPPT) theory. Through numerical modeling with the aid of the Matlab/Simulink software, the predictive control (GPC) of the active and reactive stator powers of the DFIG was validated. A comparison between the GPC of the GADA and the indirect method based on a typical PI controller is developed in order to illustrate the viability of the suggested method. According to the simulation results of this comparison, the suggested method is viable and has promising results.

Modern Control of a Power Supply Based on a Matrix Converter for Water Disinfection by UVC Radiation

Water, an essential resource, has been facing gradual deterioration in quality over an extended period. Water treatment has emerged as a critical concern, with a long-standing acknowledgment of the importance of preserving and appreciating this precious resource. UV-C technology has proven to be an ideal solution that effectively addresses these twin needs. The core principle of UV bactericidal treatment revolves around generating ultraviolet rays within a treatment chamber filled with water. Consequently, ensuring a dependable power source for low-pressure mercury-argon discharge lamps has become imperative to guarantee the efficiency of UV-C disinfection.The primary goal of this study is to supply our discharge lamp with a current source that produces a sinusoidal current at its output, featuring a frequency of 50 kHz and an effective value of 0.65 A. Electronic ballasts enable lamps to enhance radiation quality by operating at high frequencies, and the selection of a power supply is a response to this specific challenge.The aim of this research centers on the utilization of a single-phase matrix converter. The bidirectional switching cells within this converter offer frequency-related advantages. To achieve the desired magnitude and frequency, we employed a PWM control strategy. Subsequently, we implemented a linear current adjustment approach using two controllers: a Proportional-Integral (PI) controller and a Generalized Predictive Control (GPC) in the RST polynomial form. This was done to ensure a high-quality current source with the desired waveform and low Total Harmonic Distortion (THD).The research outcomes have led to environmental simulations conducted using Matlab/Simpower system and Matlab/Simulink.

Integration of Predictive Control and Interconnected Structure for Autotuning Velocity Controller

In the servo system, the autotuning controller based on a generalized predictive control (GPC) usually requires a specified reference instruction and the accurately linear system model. The former seriously limits the servo applications, while the latter's lack easily reduces the control performance. This article proposes a variable-structure proportional–integral (VSPI) velocity controller based on a GPC and an interconnected structure to address the above limitations. First, the VSPI controller is designed to track the arbitrary-order instruction without a static difference. Second, based on the Taylor series and the higher order difference operators, an improved prediction model, whose prediction output only includes the velocity error, is constructed to liberate the GPC from the reference instruction. Then, the control parameters of the VSPI controller can be adjusted online by the rolling optimization. Furthermore, the interconnected structure using three modified sliding-mode observers (MSMOs) is designed to efficiently provide the system model for the GPC, where an adaptive gain and an equivalent derivative designed in each MSMO make it possible for the identification parameters to converge exponentially. Finally, the third MSMO is used to identify the load disturbance for promoting the interconnected structure and the disturbance compensation, which can ensure the identification accuracy and the linearization of the system model. Simulation and experimental results demonstrate the effectiveness of the proposed method.

Enhancing active disturbance rejection design via deep reinforcement learning and its application to autonomous vehicle

Taking the velocity regulation of autonomous driving as an example, this paper developed an enhancing active disturbance rejection design employing the deep reinforcement learning-deep deterministic policy gradient (DDPG). In this scheme, the active disturbance rejection control (ADRC) is adopted to online estimate and compensate disturbances and uncertainties, and feasible regions of control parameters are obtained through the Lyapunov method. Then DDPG is combined with ADRC to online adaptively tune control parameters in response to the changing environments, where safety, comfort, and energy-saving are considered in the reward design, and mapping relation between the defined action and state is constructed for the maximal reward. Besides, numerical simulations demonstrate the better performance and stronger robustness of the enhancing design when facing uncertainties, sensor noise, and mechanical faults, and comfort and energy consumption have also been improved to some extent compared with the general ADRC and model predictive control (MPC).

High efficiency far-infrared barrel heating control with excess heat prediction based on generalized predictive control in injection molding

Injection molding accounts for a huge share of the energy consumption of material processing. It has received considerable attention for its potential to enhance production efficiency, which relies on both product consistency and energy efficiency. Far-infrared heating, which has the advantages of high energy efficiency and low dissipation, has emerged as a promising heating technology in injection molding. However, the issue of low heat dissipation can lead to excess heat accumulation and cause substantial temperature errors. In this paper, a new barrel heating control method based on generalized predictive control (GPC) with real-time excess heat prediction model is proposed, in order to improve control accuracy and heating efficiency of far-infrared heating. A constrained multistep optimization combined with excess heat compensation is stated and an experimental platform is designed to validate the superiority of the proposed method under different operation scenarios. Experimental results show that the proposed method outperforms traditional proportional-integral-differential (PID) and GPC methods. Specifically, the proposed method can improve temperature control accuracy by 85.3 and 78.9 %, compared to PID and GPC, respectively. Such high temperature accuracy goes a long way in improving product consistency. Moreover, energy efficiency is improved, e.g., heating time is reduced by 42.7 and 16.5 %, energy consumption is reduced by 27.9 and 25.4 %, compared to PID and GPC, respectively. Control robustness is also verified under different operating conditions. In second-heating experiments, the temperature accuracy is improved by 84.4 % compared to PID. Therefore, the proposed method has the potential to enhance the production efficiency of injection molding.

Application of Optimum Adaptive Generalized Predictive Control to Green Tea Drying

Application of Optimum Adaptive Generalized Predictive Control to Green Tea Drying

A Review on Predictive Control Technology for Switched Reluctance Motor System

The significance of employing control strategies on a switched reluctance motor (SRM) is that they can reduce vibration noise and torque ripple. With the rapid development of digital system processors, predictive control (PC), as a modern control approach, is increasingly applied to enhance the dynamic performance and operational efficiency of SRMs. This review provides a comprehensive overview of the current state of research on PC strategies of SRMs and classifies PC technologies, such as generalized predictive control (GPC), hysteresis predictive control (HPC), deadbeat predictive control (DPC), and model predictive control (MPC). It summarizes the PC schemes from the aspects of predictive current control (PCC), predictive torque control (PTC), and other PC, and it discusses the current trends in technology development, as well as potential research directions. The insights presented herein aim to facilitate further investigations into predictive control techniques for SRM.

Nonlinear Adaptive Generalized Predictive Control for PH Model of Nutrient Solution in Plant Factory Based on ANFIS

A plant factory is typically considered to be an exceedingly advanced product management system characterized by higher crop yields and better quality control. The pH value of the nutrient solution is crucial for determining the health and productivity of crops. However, the nutrient solution process exhibits inherent complexity, such as parameters uncertainty, multi-disturbances, and strong nonlinearity. Therefore, the traditional control method cannot meet the necessary requirements. The main objective of this paper is to address the issues of parameter uncertainty, strong nonlinearity, and multiple disturbances in the regulation process of the nutrient solution while achieving accurate control of the nutrient solution pH in a plant factory. This is performed so that a dynamic model of a nutrient solution for pH is developed and a nonlinear adaptive controller is presented, which comprises a linear adaptive generalized predictive controller, a nonlinear adaptive generalized predictive controller, and a switching mechanism. The parameters of the controller are adjusted by generalized predictive control (GPC) laws. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is used to estimate the unmodeled dynamics to depress the influence of nonlinearity on the system. The experiments show that the mean errors and standard errors for gain-scheduling the proportional-integral-derivative (PID) control strategy are 0.1388 and 0.4784, respectively. The mean errors and standard errors for the nonlinear adaptive controller are 0.1046 and 0.3009, respectively. Simulation results indicate that the presented method can acquire a better control effect in the case of various complex situations. Therefore, by achieving precise control of the pH value, it is possible to provide a suitable growth environment for crops, promoting healthy crop growth and increasing crop yield.

Neural network predictive control applied to automotive paint drying and curing processes

In the automotive painting process, maintaining optimal operating conditions in drying and curing ovens is crucial to ensure high-quality paint finishes, particularly for specific car body-in-white (BIW) parts' temperature profiles. This paper presents a methodology for developing and implementing a Neural Network Predictive Control (NNPC) system for painting drying and curing processes in an automotive oven used in the electrodeposition stage (Elpo oven). The objective is to enhance temperature control for BIW parts. To train the Artificial Neural Networks (ANN) in the various zones of the Elpo oven, a dataset was generated using a phenomenological model based on first principles. Random disturbances were applied to the input variables (conveyor speed and zone temperatures) to capture the dynamic response of the output variables (temperature in specific BIW parts). The dataset was split into training (80 %), validation (10 %), and test (10 %) sets, and the ANN training was performed using the backpropagation algorithm. The implemented NNPC utilizes the trained ANNs to predict future temperature values in specific BIW parts (controlled variables). To determine the optimal control signals (manipulated variables), an optimizer based on the Generalized Predictive Control (GPC) model was employed, and the objective function was minimized using the Ant Colony Optimization Algorithm (ACO). Through simulations of four operational scenarios with applied disturbances, the results demonstrate satisfactory performance of the NNPC, effectively maintaining controlled temperatures of BIW parts close to predefined setpoints. The utilization of NNPC offers improved temperature control for BIW parts, mitigating painting issues, reducing rework and operating costs, while ensuring painting quality is uncompromised.

Robust Nonlinear Predictive Control Applied to Induction Motors

In this paper, a nonlinear predictive controller is proposed for a variable-speed induction motor. The research work is directed towards improving the trajectory tracking capability, stability guarantee, robustness to parameter variations, and disturbance rejection. The Generalized Predictive Control (GPC) without constraint and output-constrained controller to induction motor drive is illustrated. The variables to be controlled are rotor speed and flux trajectory. The load torque is considered an unknown disturbance. Finally, the tuning parameter of GPC is automatically determined. The simulation results show a good performance for the nonlinear dynamic system.

Multi-Area Load Frequency Control in Power System Integrated With Wind Farms Using Fuzzy Generalized Predictive Control Method

Frequency security is critical for the power systems’ stability and reliability. The integration of renewable energy resources brings new challenges to the frequency security of power systems. To provide a more robust control method for controlling the frequency and tie-line power flow of an interconnected power system integrated with wind farms (IPSWF), a load frequency control (LFC) controller of generalized predictive control (GPC) based on the Takagi-Sugeno (T–S) fuzzy model (fuzzy-GPC) is proposed in this paper. First, the T–S fuzzy model of the IPSWF is constructed. Then, the GPC is used to design the controller. Lastly, to validate the proposed scheme, an LFC strategy is established for a two-area interconnected power system integrated with a wind farm in power systems computer aided design (PSCAD). The control performance of proportional-integral, GPC, and fuzzy-GPC controller are compared under two faulty operating conditions. The simulation results show that the performance indicators and the dynamic behavior when the fuzzy-GPC controller is used are better than the performance when the PI controller and GPC controller are used. The proposed fuzzy-GPC used in the LFC of the IPSWF can regulate the frequency deviation and tie-line power deviation adaptively and achieve minimum frequency deviation and tie-line power deviation in a multi-area IPSWF.

An adaptive frequency regulation strategy with high renewable energy participating level for isolated microgrid

To improve the renewable energy sources (RES) utilization of isolated microgrids (IMG) in frequency regulation process, an adaptive frequency regulation strategy with high renewable energy participating level is proposed in this paper: 1) an adaptive droop control method is conceived for primary frequency regulation, in which the designed droop coefficients are adjustable with the real-time wind velocity and state of charge (SoC) of energy storage units (ESUs); as the most important contribution, 2) a general flexible model predictive control (FMPC) framework with adaptive cost function and constraints is developed for secondary frequency regulation. For 2), i) the weight coefficients of the cost function can be adaptively adjusted according to frequency deviation through a pre-designed intelligent function, in which the specification of determining the key parameters is involved; ii) the designed constraints' boundaries can dynamically shift with wind velocity and ESU's SoC; iii) the negative impact of model's uncertainties can also be mitigated by implementing the proposed FMPC. Finally, the effectiveness of the proposal is verified by the comprehensive simulation and experimental study, and the results show that the proposal can significantly improve RES's utilization level while guaranteeing frequency regulation performance and operation safety in IMG.

Generalized Predictive Current Control for Dual-Three-Phase PMSM to Achieve Torque Enhancement Through Harmonic Injection

The output torque capability of a dual-three-phase permanent magnet synchronous machine (DTP-PMSM) can be increased by intentionally injecting proper harmonic currents, which can be beneficial in some applications that require high torque density. To achieve this, current references containing multiple harmonic components with time-varying frequencies must be accurately tracked by the current controllers, which can hardly be achieved by conventional control methods. Fortunately, to track these complex but predetermined current references, generalized predictive control (GPC) shows unique advantages for its characteristic of multi-step prediction and receding horizon optimization. In this paper, a current control strategy based on GPC is proposed for the DTP-PMSM, which enhances the output torque capability by tracking the harmonic current references with high accuracy in both phase and magnitude. Parameter tuning and model mismatch issues of the proposed GPC are discussed. The effectiveness and merits of the proposed control strategy are validated by comparison study based on simulation and experiments.

Research on Self-driving Vehicle Path Tracking Adaptive Method Based on Predictive Control

The autonomous vehicles can improve the traffic utilisation and ensure the safe driving. Thus, the relative analysis is one hot research topic nowadays. The path tracking control system is one key link for the quick and stable tracking of target paths of autonomous vehicles. For analyzing the route tracing process of the Self-driving vehicles under complex working conditions, this paper proposes an adaptive model prediction controller. The controller chooses a vehicle dynamics model and incorporates a fuzzy adaptive control method into the model predictive control algorithm. Through the fuzzy adaptive control method, the weight of the objective function in the model predictive control algorithm is adaptively optimized to improve the control effects. The joint simulation tests are applied in the CarSim, MATLAB and Simulink software. Compared with the general model predictive controller, the outcomes indicate that the conceptualized controller can effectively reduce the tracking errors and ensure safe driving.

A Variable Self-Tuning Horizon Mechanism for Generalized Dynamic Predictive Control on DC/DC Boost Converters Feeding CPLs

In high-power electronic industrial systems, constant power loads (CPLs) can affect the system performance or even cause instability due to its inherent negative impedance characteristics. Regarding this control issue, model predictive control (MPC) methods are generally applied to dc/dc converters feeding CPLs, aiming to achieve satisfactory control performance. However, they are mostly based on a fixed horizon design, whose optimal performance may be far from the desired one due to the change of operating conditions, e.g., different variation levels of CPLs. In this context, a novel generalized dynamic predictive control (GDPC) strategy employing a simple self-tuning horizon mechanism is proposed for the first time, allowing the controller to adaptively optimize the system transient-time performance even under largely-varied operating conditions. First, a disturbance observer is utilized to reconstruct the lumped disturbances within the system, which are subsequently brought into the control design through feedforward compensation loops. Second, an adaptive horizon is introduced into the generalized predictive control (GPC) design and rigorous stability analysis based on Lyapunov theorem is given. The effectiveness and performance improvement of the proposed regulation methodology are verified by both simulation and experimental studies in reference to the double-loop proportional-integral (PI) control and a benchmark GPC algorithm.