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How to solve their automatic control of constant temperature and humidity gradually becomes a research hotspot as the continuous upgrading of air conditioning systems. This study aims to optimize the traditional proportional-integral-differential controller for improvement to solve the time-delay instability phenomenon in temperature and humidity control. The objective of this study is to optimize existing proportional-integral-differential controllers to improve the time-delay instability problem that is common in temperature and humidity control. Firstly, it treats the controlled object as a first-order and second-order system with time-delay characteristics. Next, the Smith predictor controller is generalized equivalent to ensure that the equivalent system does not contain time-delay. Finally, an analysis of the first-order and second-order closed-loop control system is conducted by combining Smith predictive controller and proportional-integral-differential controller. The system achieves the goal of automatic control of constant temperature and humidity by adjusting the control parameters. The experiment showcased that the temperature control time of the proposed control scheme under first-order and second-order time-delays was 16 s and 3 s, respectively. Meanwhile, the humidity control time was 14 s and 13 s, respectively. In practical applications, the proposed control scheme achieved good control effects in all four seasons. This indicates that the controller designed in this study possesses good control performance. It also can achieve the goal of constant temperature and humidity control. This can provide technical support for the automation control of air conditioning systems.

Three-phase linear synchronous actuators play a pivotal role in precision motion control applications such as lithography machines and laser material processing stages. Achieving superior tracking performance is paramount in the design of motion control systems, prompting the utilization of advanced control algorithms. In this study, a novel approach is presented to enhance the tracking capability of a three-phase permanent magnet actuator by introducing a predictive field-oriented control system. The primary contribution of this paper lies in the comprehensive design and implementation of the predictive field-oriented control system. Initially, actuator modeling is conducted in the rotating reference frame (d–q frame) and finite element analysis is performed to determine key electrical quantities, including magnetic flux densities and inductances. To address the challenges posed by time-varying sinusoidal electrical signals, a field-oriented control methodology is proposed. Notably, the novelty of this work is underscored by a distinct emphasis on the predictive control strategy employed in the system. The predictive controller is implemented on a 32-bit ARM Cortex microcontroller, showcasing the practical viability of the proposed approach. Experimental results substantiate the effectiveness of the proposed method in achieving precise trajectory tracking. This paper contributes to the field by providing a rigorous analysis of a three-phase permanent magnet actuator and introducing a predictive field-oriented control system. The methodology outlined here enhances tracking capabilities and signifies a substantial advancement in the broader landscape of precision motion control systems. Thus, this work adds valuable insights to the existing body of knowledge in the domain, while offering a notable contribution to the field. [ABSTRACT FROM AUTHOR]

To improve the tracking performance of intelligent vehicle, a lateral controller based on LQR theory and a longitudinal controller based on sliding mode theory were proposed to meet the requirements of tracking accuracy and stability in this paper. Firstly, a feedforward LQR controller was established based on 2-DOF (two degree of freedom) dynamics model. To solve the problem of feedforward LQR controller stability reduce caused by model linearization, a real-time velocity-road curvature fuzzy adaptive prediction LQR controller combined with CRTV model was established. In addition, to improve the stability and tracking accuracy of longitudinal velocity, a longitudinal tracking controller was proposed based on SMC(sliding mode control) theory. To verify the proposed controller, co-simulation and HIL (hardware in loop) experiment were conducted. The results show that the proposed controller combines tracking accuracy and stability. The tracking performance is significantly improved. [ABSTRACT FROM AUTHOR]

This work presents the design and assessment of four control schemes for the monitoring and regulation of joint trajectories applied in the dynamic model of a SCORBOT-ER V plus robot, which includes the dynamics of the actuators, and the estimation of the friction forces present within the joints. The two classical control strategies calculated torque and PID, and the two advanced control strategies, fuzzy and predictive, are considered. In the latter case, a gravitational compensation stage is incorporated, as well as the inverse models of the motors and the transmissions of belt movement for each joint. Computational tests are performed by applying an external step-type disturbance to the third joint of the robot. Finally, an evaluation of the results obtained is presented through trajectory curves, joint errors, and the three performance indexes residual mean square, residual standard deviation, and index of agreement. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One of the essential systems for the upgrade of Damavand Tokamak is the automatic control of the vacuum vessel pressure profile. This research performs the identification, modeling, and control of the vacuum chamber pressure in Damavand Tokamak. As a first step, the experimental structure is designed and implemented for the application of an identification signal and for the collection of data from the vacuum vessel pressure. Following the creation of the base vacuum, the input voltage to the piezoelectric is changed and applied. At the same time, both the vacuum vessel pressure and applied voltage are measured. Using experimental data, several linear models are identified for control. Then, considering the practical limitations of the control signal (25

In the realm of satellite orbit control, powerful controller design plays a pivotal role in minimizing fuel consumption and ensuring orbit stability. This article introduces an advanced approach to the design of a Linear Quadratic Regulator (LQR) controller with an infinite horizon, tailored for discrete delay systems. The proposed methodology integrates predictive control with a reduction method, aiming for optimality while addressing performance and system constraints. Formulating the control problem as a quadratic program, the predictive control method generates a sequence of control inputs using a reducing horizon strategy. Stability analysis, employing Lyapunov-Krasovsky functions and linear matrix inequalities, yields delay-independent conditions for exponential convergence. A numerical example showcases the controller's effectiveness in maintaining orbit and reducing fuel consumption, underlining its capacity to achieve control objectives despite uncertainties and time delays. This research contributes to robust control strategies in satellite orbit systems, enhancing control performance and operational efficiency.

Virtual synchronous generators (VSGs) are one of the most relevant solutions to integrate renewable energy in weak grids and microgrids. They indeed provide inverters characteristics of rotating machines (inertia for instance) that are useful for stabilizing the system, notably in the context of the high variability of the production. Thanks to the virtual characteristics of the VSG, the virtual parameters of the emulated synchronous machine can be optimally adapted online as a function of the electric environment of the inverter. We call that inverter's control a polymorphic VSG. The online adaptation of the critical control parameters of the VSG helps reduce the risk of deterioration of the inverter's constituents that might be induced by harsh events (frequent in weak grids) but, more importantly, improves the robustness of the system. In this paper, four implementations of a polymorphic VSG controller are compared on a simple microgrid study case to a complete VSG model. For the test, polymorphic VSGs have to minimize frequency and voltage oscillations while withstanding short circuits, which is typically a requirement for units in this context. One of the controls is based on recurrent optimization over a prediction time horizon, and two sub-optimal ones target practical implementation in industrial inverters with limited computational power. Results show a clear reduction in incidents in the microgrid thanks to the controllers. The error reduction with the complete polymorphic VSG is up to 100% for the voltage, 32% for the currents, and 79% for the duty ratio. Those values are decreased by 30 to 50% with the sub-optimal controllers but for a reduction in the computational burden of more than 97%. Recommendations are proposed for the development of an auto-adaptive polymorphic VSG from a high technology-readiness-level perspective, i.e., targeting a compromise between error reduction and computational burden. [ABSTRACT FROM AUTHOR]

In this research, a predictive voltage neural controller and remote monitoring for the nonlinear proton exchange membrane fuel cell (PEMFC) system are implemented in real-time. This paper's major purpose is to precisely and rapidly determine the appropriate hydrogen partial pressure (PH2) control action to improve the fuel cell's nonlinear performance under varying load currents and to enable remote monitoring of the nonlinear PEMFC system based on the internet of things (IoT). The laboratory, virtual instrument engineering workbench (LabVIEW) package is used to demonstrate the real-time performance of the proposed predictive voltage controller applied to the 150-watt PROTIUM PEMFC, which will be used to generate the appropriate amount of hydrogen partial pressure control action that will enter the fuel cell for stabilizing the desired output voltage. The message queuing telemetry transport (MQTT) protocol and a Raspberry Pi 4 acting as a local server are the building blocks upon which the monitoring component of the proposed system is implemented in order to monitor the desired output voltage, the fuel cell output voltage, and PH2. The Raspberry Pi collects the necessary fuel cell data and sends it to the node-red dashboard for monitoring. According to the simulation and the experimental results obtained using the proposed predictive controller on PROTIUM PEMFC, the proposed controller can generate an accurate, prompt, and timely reaction to the hydrogen partial pressure control action to reduce the tracking voltage error and to get rid of the fuel cell output voltage oscillation. The proposed experimental work was compared to the simulation findings to confirm its effectiveness in terms of effectively tracking the desired output voltage, providing a fast response, and achieving the optimal partial pressure of hydrogen. However, in the simulation findings, a voltage error of 0.01 volts was observed without any oscillation. On the other hand, the experimental results indicate a slightly higher voltage error of approximately 0.1 volts, accompanied by oscillations of around ± 0.1 volts. [ABSTRACT FROM AUTHOR]

This article presents a comparative study of lateral motion controller design for distributed drive vehicle. Aiming at handling long input delays, two types of robust controllers are specifically investigated. First, a Lyapunov‐Krasovskii functional based robust controller design is proposed. Considering input delay, the design conditions of the state‐feedback controller are derived in terms of linear matrix inequalities (LMIs), which can be effectively solved using LMI toolbox. Second, based on the same control law, a robust predictive controller design is further presented. However, different from the Lyapunov‐Krasovskii functional based robust controller, the control gain of the proposed robust predictive controller is calculated without considering the input delay. An input delay estimator and a predictor are developed instead to reconstruct the delay‐dependent state, which is further used to generate a new feedback error. Moreover, the feasibility of this control scheme is theoretically proved. Finally, extensive co‐simulations with Carsim and Matlab/Simulink are conducted to compare the performance between these two controllers. Compared with the Lyapunov‐Krasovskii functional based robust controller, longer input delay can be handled by using the proposed robust predictive controller. [ABSTRACT FROM AUTHOR]

To minimize the impact of snowfall and ice formation on safety of transportation, salt is sprinkled on the asphalt every winter. However, the use of salt has economical as well as ecological disadvantages. To resolve these problems, road heating systems are used in the northern regions of Europe and America. Despite their widespread usage, considerable potential of the operational optimization is evident. The current systems are controlled under predefined weather conditions such as start of operation at 5 °C air temperature, even when snowfall is absent. Consequently, loss of energy input to heat the system is caused. To avoid unnecessary financial and energetic expense, this study presents CFD-based performance investigation as a basis for a novel predictive controller to increase the operational efficiency of hydronic road heating systems (HRS). The simulation model was developed based on a real operational HRS located in Ingolstadt and composed of bridges and ramps for a total surface of 1989 m2. Climate data of the years 2019–2020 from local weather stations were implemented in the simulation model for performance prediction on extreme climate conditions. This investigation identified that up to 70% of operational hours in terms of energy input can be saved by using a hypothetical predictive controller, thus making the HRS a more economically efficient and environmentally attractive alternate to conventional de-icing techniques. [ABSTRACT FROM AUTHOR]

This paper presents a new development of a predictive voltage neural controller to control the stack terminal output voltage of a nonlinear proton exchange membrane fuel cell (PEMFC) system based on a neural network technique and a back-propagation learning algorithm. The main objective of this paper is to precisely and quickly identify the best control action of the hydrogen partial pressure to enhance the nonlinear performance of the fuel cell output voltage under a variable load current. This optimal control action prevents damage to the fuel cell membrane, thereby prolonging the fuel cell's lifetime. The proposed predictive voltage controller consists of three sub-controllers. The first one is the numerical feed-forward controller (NFFC), which is used to decide the steady-state hydrogen partial pressure (PH2) control action depending on the desired voltage. The second sub-controller is a feedback neural controller that uses a multi-layer perceptron (MLP) and a back-propagation learning algorithm to generate the hydrogen partial pressure feedback control action to track the desired output voltage of the fuel cell during transient conditions. The third sub-controller is the predictive control law equation, which is based on the modified Elman recurrent neural network (MERNN) as an identifier for the PEMFC model and the multi-objective performance index. From the simulation results, the proposed controller, which is composed of the three sub-controllers, has the capability to generate a precisely and quickly timed response to the hydrogen partial pressure control action in order to minimize the tracking voltage error and eliminate oscillation in the output voltage of the fuel cell. Finally, the suggested predictive voltage control strategy's numerical simulation results are then verified by comparison with those of other types of controllers in terms of the minimum number of steps ahead prediction (reducing from 10 to 1 step ahead prediction) and enhancement of the tracking voltage error by 81.8% when comparing with a predictive neural controller and improvement of the tracking voltage error by 87.5% when comparing with an inverse neural controller. Moreover, the oscillation effect in the output voltage is completely eliminated, resulting in a response without any overshoot. [ABSTRACT FROM AUTHOR]

This paper presents a novel event-triggered predictive control (ETPC) approach for the stabilization of discrete-time output-feedback networked control systems (NCSs). The studied NCS is considered to be subject to both random external input and output disturbances, and network imperfections including random communication delay, random packet dropout, packet disorder, limitation of network bandwidth, and network resources. In the proposed algorithm, an observer-based event detector is designed for reducing the number of sent packets through the communication network using the estimated system states by the Luenberger observer. In this way, the system’s energy resources are saved and network-induced effects are skipped. A switched predictive controller with multiple gains are used to compensate for network-induced effects. Controller gains are designed compatible with different possible values of delays and packet dropouts. A novel augmented representation of the state-space equations of the system is derived to design observer gain and controller gains. The asymptotic stability of the system is guaranteed by designing the observer and controller based on the Lyapunov function through solving linear matrix inequalities (LMIs). Putting all the aforementioned points together has made the whole framework presented in this paper a comprehensive one. The effectiveness of the proposed approach is demonstrated by comparative simulation results.

Virtual synchronous generators (VSGs) are one of the most relevant solutions to integrate renewable energy in weak grids and microgrids. They indeed provide inverters characteristics of rotating machines (inertia for instance) that are useful for stabilizing the system, notably in the context of the high variability of the production. Thanks to the virtual characteristics of the VSG, the virtual parameters of the emulated synchronous machine can be optimally adapted online as a function of the electric environment of the inverter. We call that inverter’s control a polymorphic VSG. The online adaptation of the critical control parameters of the VSG helps reduce the risk of deterioration of the inverter’s constituents that might be induced by harsh events (frequent in weak grids) but, more importantly, improves the robustness of the system. In this paper, four implementations of a polymorphic VSG controller are compared on a simple microgrid study case to a complete VSG model. For the test, polymorphic VSGs have to minimize frequency and voltage oscillations while withstanding short circuits, which is typically a requirement for units in this context. One of the controls is based on recurrent optimization over a prediction time horizon, and two sub-optimal ones target practical implementation in industrial inverters with limited computational power. Results show a clear reduction in incidents in the microgrid thanks to the controllers. The error reduction with the complete polymorphic VSG is up to 100% for the voltage, 32% for the currents, and 79% for the duty ratio. Those values are decreased by 30 to 50% with the sub-optimal controllers but for a reduction in the computational burden of more than 97%. Recommendations are proposed for the development of an auto-adaptive polymorphic VSG from a high technology-readiness-level perspective, i.e., targeting a compromise between error reduction and computational burden.

Optimizing energy consumption in buildings is a significant challenge in today’s society. A major part of energy consumption is in heating, ventilation and air conditioning (HVAC) systems. In this paper, the aim is to reduce the energy consumption of air handling units (AHU) by applying optimal control. This system used in this study has four AHUs, all of which are assumed to be the same. Due to the uncertainty of the temperature of the heat exchanger’s (H/E) inlet and outlet water, a model of the system was first made using its hypothetical capacity according to the ASHRAE standards. The inlet and outlet water temperatures are calculated using simulated and real data. In order to increase the model’s accuracy and facilitate implementation on a real system, the data obtained is used to train a dynamic recurrent neural network (RNN) for the H/E. Furthermore, to increase the system’s stability and bolster its response to disturbances, which change system parameters over time and reduce the accuracy of neural network models, an online recursive least squares (RLS-based) adaptive constrained generalized predictive controller (AGPC) is used to control its outlet air temperature. The AGPC attempts to minimize the computational load and estimates the transfer function by using continuously updated input-output data from the model; this model has fewer parameters than the RNN model. Finally, the power consumption of the H/E is calculated. The outlet humidity and airflow are controlled using an optimal controller to minimize energy consumption. The results show a reduction in the energy consumption of 54.95% with respect to the previous work and of 69.9% compared to the dataset from the real system.

Supercavitation technology can greatly improve the speed of underwater vehicles by reducing drag. However, the time-delay characteristic of a supercavitating vehicle presents control challenge. In this paper, a linear parameter-varying (LPV) time-delay model for a supercavitating vehicle is established by analysing the time-delay characteristics of the planing force. Moreover, considering that the cavitation radius is easily perturbed by external disturbances, a supercavitating LPV delay model depicted as a polytope is established. Based on the model, a predictive controller based on time-delay characteristics is designed. The controller can solve linear matrix inequalities (LMI) online to find the optimal solution at a given moment. In addition, the controller introduces a different Lyapunov function and multiple free control variables to reduce the system's conservativeness and expand the system's initial feasible region, which can improve the control performance of the system. A simulation verifies that the system exhibits good stability and robustness.

As society grows, the urbanized population proliferates, and urbanization accelerates. Increasing traffic problems affect the normal process of the city. The urban transportation system is vital to the effective functioning of any city. Science and technology are critical elements in improving traffic performance in urban areas. In this paper, a novel control strategy based on selecting the type of traffic light and the duration of the green phase to achieve an optimal balance at intersections is proposed. This balance should be adaptable to fixed behavior of time and randomness in a traffic situation; the goal of the proposed method is to reduce traffic volume in transportation, the average delay for each vehicle, and control the crashing of cars. Due to the distribution of urban traffic and the urban transportation network among intelligent methods for traffic control, the multi-factor system has been designed as a suitable, intelligent, emerging, and successful model. Intersection traffic control is checked through proper traffic light timing modeled on multi-factor systems. Its ability to solve complex real-world problems has made multiagent systems a field of distributed artificial intelligence that is rapidly gaining popularity. The proposed method was investigated explicitly at the intersection through an appropriate traffic light timing by sampling a multiagent system. It consists of many intersections, and each of them is considered an independent agent that shares information with each other. The stability of each agent is proved separately. One of the salient features of the proposed method for traffic light scheduling is that there is no limit to the number of intersections and the distance between intersections. In this paper, we proposed method model predictive control for each intersection's stability; the simulation results show that the predictive model controller in this multi-factor model predictive system is more valuable than scheduling in the fixed-time method. It reduces the length of vehicle queues. [ABSTRACT FROM AUTHOR]

In this paper, a real-time predictive control strategy is developed to control the energy consumption of hybrid electric vehicles with lower sensitivity to prediction accuracy. A predictive Best-Mode concept is introduced based on the future speed predictions, by which the trend of battery state of charge is estimated. The estimated battery state of charge is used to better management of the battery charge mode. The optimum work zones of the components are then selected according to the best battery charging mode and the vehicle speed and power demand. This controller is less sensitive to the prediction accuracy and enables the system to work at the near-optimal points. The results show that the predictive Best-Mode controller is capable of minimizing the energy consumption in real-time applications, very close to the results of the offline dynamic programming with a 2% error margin. The predictive Best-Mode strategy's performance is better than the finite-horizon dynamic programming, except for accurate prediction with a longer than 20-sec prediction horizon. [ABSTRACT FROM AUTHOR]

This paper presents a novel event-triggered predictive control (ETPC) approach for the stabilization of discretetime output-feedback networked control systems (NCSs). The studied NCS is considered to be subject to both random external input and output disturbances, and network imperfections including random communication delay, random packet dropout, packet disorder, limitation of network bandwidth, and network resources. In the proposed algorithm, an observer-based event detector is designed for reducing the number of sent packets through the communication network using the estimated system states by the Luenberger observer. In this way, the system's energy resources are saved and network-induced effects are skipped. A switched predictive controller with multiple gains are used to compensate for network-induced effects. Controller gains are designed compatible with different possible values of delays and packet dropouts. A novel augmented representation of the state-space equations of the system is derived to design observer gain and controller gains. The asymptotic stability of the system is guaranteed by designing the observer and controller based on the Lyapunov function through solving linear matrix inequalities (LMIs). Putting all the aforementioned points together has made the whole framework presented in this paper a comprehensive one. The effectiveness of the proposed approach is demonstrated by comparative simulation results. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. • Stability analysis of the constrained generalized predictive controller (GPC) is performed with the use of Lagrange multipliers. • A gain scheduling fuzzy GPC controller is proposed to regulate the flow rate and pressure of an agricultural sprayer. • The scheduling of the GPC parameters λ and δ via fuzzy logic allows for the incorporation of nonlinearities. • Experimental results show a reduction in oscillations caused by the dead zone nonlinearity and variation in the system model caused by the changes of spray nozzles. [ABSTRACT FROM AUTHOR]

A general predictive controller based on the subspace model identification method is proposed for vehicle stabilization. Traditional predictive controllers are always developed based on the principle model of vehicles, which inevitably suffers from parameter uncertainty and poor adaptability. In contrast to that, the proposed subspace-based general predictive controller is realized by a data-driven process and presents good adaptability in vehicle stability control. Inspired by subspace-based predictor construction, the keys of the predictive controller are as follows: (1) system model identification according to the model structure of the control object by input and output data; (2) output prediction of the system by the identified model; and (3) optimal control law designed by combining the linear–quadratic–Gaussian index with the predictive output. The main problem in the controller development lies in the recursive estimation of relevant matrices, which is limited by the subspace model identification theory. The implementation of the vector autoregressive with exogenous input model and the propagator method in subspace identification algorithm effectively solves the problem of estimation accuracy and calculation efficiency. Combined with a linear–quadratic–Gaussian index function, the predictive law for vehicle stability control is derived in detail. Finally, based on the vehicle model validated by standard road test, the effectiveness and robustness of the predictive controller are proved through the numerical simulations of various maneuvers under different road adhesive conditions. [ABSTRACT FROM AUTHOR]

As society grows, the urbanized population proliferates, and urbanization accelerates. Increasing traffic problems affect the normal process of the city. The urban transportation system is vital to the effective functioning of any city. Science and technology are critical elements in improving traffic performance in urban areas. In this paper, a novel control strategy based on selecting the type of traffic light and the duration of the green phase to achieve an optimal balance at intersections is proposed. This balance should be adaptable to fixed behavior of time and randomness in a traffic situation; the goal of the proposed method is to reduce traffic volume in transportation, the average delay for each vehicle, and control the crashing of cars. Due to the distribution of urban traffic and the urban transportation network among intelligent methods for traffic control, the multi-factor system has been designed as a suitable, intelligent, emerging, and successful model. Intersection traffic control is checked through proper traffic light timing modeled on multi-factor systems. Its ability to solve complex real-world problems has made multiagent systems a field of distributed artificial intelligence that is rapidly gaining popularity. The proposed method was investigated explicitly at the intersection through an appropriate traffic light timing by sampling a multiagent system. It consists of many intersections, and each of them is considered an independent agent that shares information with each other. The stability of each agent is proved separately. One of the salient features of the proposed method for traffic light scheduling is that there is no limit to the number of intersections and the distance between intersections. In this paper, we proposed method model predictive control for each intersection’s stability; the simulation results show that the predictive model controller in this multi-factor model predictive system is more valuable than scheduling in the fixed-time method. It reduces the length of vehicle queues.

The power generation from wind turbine are variable because of dependence on environmental conditions and it is important to extract maximum energy from wind. This paper proposes a new method to extract maximum energy from wind turbine systems. The artificial neural network (ANN) is used to estimate the wind speed based on the rotor speed and the output power. In addition to ANN, a predictive controller is used to maximize the efficiency of the boost converter. In predictive controller, duty cycle of boost converter is controlled to obtain the maximum power point based on the slope method. One of the most interesting advantages of this controller is simplicity of control and implementation that is leads to fast response and exact tracking. The method has been developed and analyzed by utilizing a turbine directly driven permanent-magnet synchronous generator (PMSG). The simulation results verify the performance of the proposed method. Results show that this method maximizes wind energy extraction with more accuracy and fastness.

Model predictive control has been widely used in the industry. This can control the multivariable system with constraints on input and output variables but it needs online computation solver, and creates the non-convex solution in nonlinear plant due to the parameter uncertainties. The online computational problem and non-convex solution of the model predictive control are achieved via neural network model predictive control. The paper explores the speed control of permanent magnet synchronous motor (PMSM) by using neural network model predictive control (NNMPC) technique. The multi-layer artificial neural network is used to identify the dynamics of PMSM. The set point speed tracking control of PMSM is identified by using neural network model predictive control strategy. By using the set of input and output data obtained from the system, the multi input-output feed-forward neural network model is created. Levenberg-Marquardt algorithm is used to train the process models of the PMSM. That provides future plant output for control optimization of the predictive control. The overall system is developed and tested in the MATLAB/Simulink. To evaluate the efficiency of the controller proposed, it is compared with a constrained model predictive controller through the studies of simulation. The overshoot and settling time of the speed response of the PMSM are measured and analyzed for NNMPC and constrained MPC. [ABSTRACT FROM AUTHOR]

In the control of permanent magnet synchronous machines (PMSMs), predictive controllers have received considerable attention due to the enhanced dynamic responses. However, periodic disturbances caused by dead-time effects and sampling errors deteriorate the performances of PMSMs. In order to suppress periodic disturbances, one typical control structure is to adopt several corresponding resonant controllers connected in parallel with the main controller. Even though excellent steady-state performances can be obtained, coupling effects between reference tracking and periodic disturbance attenuation can be observed, leading to significant transient oscillations under the situation of a reference step change. In order to realize decoupling between reference tracking and disturbance attenuation, this paper proposes a dual-loop predictive control structure, in which the reference tracking is only determined by the primary predictive controller. Periodic disturbance attenuation is achieved through an embedded disturbance rejection loop. By applying the proposed controller, periodic disturbances are strongly attenuated while smooth and fast transient performances are simultaneously guaranteed. Furthermore, a wide range of low frequency harmonics can be successfully suppressed, including but not limited to the main periodic harmonics. The steady-state performance, dynamic responses, and robustness against parameter variations of the proposed controller are verified through experiments based on a 2.3-kW PMSM. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates the dynamic event-triggered predictive control problem of interval type-2 (IT2) fuzzy systems with imperfect premise matching. First, an IT2 fuzzy systems model is proposed, including a dynamic event-triggered mechanism, which can save limited network resources by reducing the number of data packets transmitted, and a predictive controller, which can predict the state of the system between the two successful transmitted instants to deal with unreliable communication networks. Then, according to the Lyapunov stability theory and imperfect premise matching method, sufficient conditions for system stabilization and the controller gain are obtained. Finally, the validity of the proposed method is demonstrated by the numerical examples.

In the realm of satellite orbit control, powerful controller design plays a pivotal role in minimizing fuel consumption and ensuring orbit stability. This article introduces an advanced approach to the design of a Linear Quadratic Regulator (LQR) controller with an infinite horizon, tailored for discrete delay systems. The proposed methodology integrates predictive control with a reduction method, aiming for optimality while addressing performance and system constraints. Formulating the control problem as a quadratic program, the predictive control method generates a sequence of control inputs using a reducing horizon strategy. Stability analysis, employing Lyapunov-Krasovsky functions and linear matrix inequalities, yields delay-independent conditions for exponential convergence. A numerical example showcases the controller's effectiveness in maintaining orbit and reducing fuel consumption, underlining its capacity to achieve control objectives despite uncertainties and time delays. This research contributes to robust control strategies in satellite orbit systems, enhancing control performance and operational efficiency., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)