Fuzzy Predictive Control Research Articles

Artificial intelligent pancreas for type 1 diabetic patients using adaptive type 3 fuzzy fault tolerant predictive control

In this paper, the design methodology of artificial intelligent pancreas is presented. Accurate regulation of blood glucose levels in type 1 diabetic patients is of great importance in the presence of possible faults caused by sensor measurements. Regulation of blood glucose levels using a type 3 fuzzy predictive controller in type 1 diabetic patients in the presence of sensor faults is considered. The proposed structure includes a main control structure and a virtual dynamic, in which the main structure includes a fuzzy identifier, predictive controller, and an adaptive compensator, and the virtual structure is used to identify the sensor faults. Glucose is unknown in the dynamics of type 1 diabetes and is estimated on-line using a type 3 fuzzy system. Also, Lyapunov stability analysis is used to design the adaptive compensator to ensure the stability of the closed-loop system. The proposed methodology is evaluated based on Bergman’s minimum model for different patients under various parametric uncertainties and disturbances.

Fuzzy adaptive model predictive control and sparse identification for unmanned vehicles

Fuzzy adaptive model predictive control and sparse identification for unmanned vehicles

Optimal design of neural network-based fuzzy predictive control model for recommending educational resources in the context of information technology

Abstract As the information technology develops, educational resource recommendation system has become an indispensable part of the education field. At the same time, people’s demand for personalised educational resources is also growing; therefore, how to accurately predict user demand and provide personalised recommendations has become an important issue. In this study, a fuzzy predictive control model on the grounds of neural network is proposed to optimise the design of educational resource recommendation in the context of information technology. After experimental testing, the model recommendation fit reached 95.16 and 92.91% on the two test datasets, which are significantly higher than the control model. The average F1 values of the proposed model also reached 95.21 and 88.77%, which are higher than the control model. In other control experiments, the proposed model of this study also has a better performance. The relevant outcomes showcase that the predictive performance and recommendation effect of the model can be further enhanced by improving the structure of the neural network and the parameter optimisation method. Meanwhile, the proposed model has high performance in information overload and personalised demand, which offers a useful reference for the optimal design of educational resource recommendation system.

Gaussian-inverse gamma mixture distribution–based extended Kalman observer for dynamic positioning control system of offshore platform

Under different working conditions and control objectives, fuzzy model predictive control is used to optimize the control effect by adjusting the weight to achieve stable and accurate control for the dynamic positioning control of offshore platforms. Also, aiming at the situation where the measurement noise of dynamic positioning control may be time-varying and non-Gaussian and the statistics of the noise are not exact, this paper proposes a variational Bayesian extended Kalman filter observer. In order to model non-Gaussian noise more reasonably, the joint posterior distribution of non-Gaussian noise with state and time-varying covariance is described as the product of Gaussian and independent inverse gamma distributions. Then, the variational Bayesian (VB) method was used to simultaneously estimate the system state, the intermediate variable values of the new distribution, and the noise parameters through fixed-point iteration. The fuzzy algorithm is applied to the model predictive control, and the weight parameters of the model predictive control are adaptively changed to better adapt to the changing working conditions in the control process. The simulation results show that the variational Bayesian extended Kalman filter observer has better performance than the existing algorithms when dealing with the time-varying non-Gaussian observation noise, whose statistics are not exact. The variational Bayesian extended Kalman filter observer can significantly improve the accuracy of the control when it is used in fuzzy model predictive control.

A fuzzy compensation-Koopman model predictive control design for pressure regulation in proten exchange membrane electrolyzer

Proton exchange membrane (PEM) electrolyzer have attracted increasing attention from the industrial and researchers in recent years due to its excellent hydrogen production performance. Developing accurate models to predict their performance is crucial for promoting and accelerating the design and optimization of electrolysis systems. This work developed a Koopman model predictive control (MPC) method incorporating fuzzy compensation for regulating the anode and cathode pressures in a PEM electrolyzer. A PEM electrolyzer is then built to study pressure control and provide experimental data for the identification of the Koopman linear predictor. The identified linear predictors are used to design the Koopman MPC. In addition, the developed fuzzy compensator can effectively solve the Koopman MPC model mismatch problem. The effectiveness of the proposed method is verified through the hydrogen production process in PEM simulation.

Research and Simulation Analysis of Fuzzy Intelligent Control System Algorithm for a Servo Precision Press

With the rapid development of the manufacturing industry toward intelligence and flexibility, traditional mechanical presses are unable to meet the increased stamping requirements due to the difficulties in achieving variable speed control and changing slide motion trajectories. Servo presses, driven directly by servo motors, can realize the flexible control of press movement and have become the trend in the industry for future development. The independent research and development of servo press control systems has become a popular topic and challenge in the domestic press industry. This paper proposes a new type of press transmission mechanism suitable for high-precision stamping, analyzes the working principle of its transmission mechanism, and provides a detailed analysis and discussion on the main research methods of fuzzy intelligent control for servo presses, including fuzzy model and basic control algorithms, fuzzy modeling and control of nonlinear systems, fuzzy predictive control, and stability analysis of fuzzy control systems. Based on the principle of the control scheme, a closed-loop control system block diagram and mathematical model for servo high-precision presses are established. Taking the physical prototype parameters of the STSZG1-250 as an example, the control system is dynamically simulated and verified using Simulink modules. Finally, their role and effects in intelligent control, and prospects for the development of fuzzy intelligent control, are discussed.

Enhanced three-dimensional trajectory tracking control for AUVs in variable operating conditions using FMPC-FTTSMC

In addressing the variable operating conditions encountered in complex underwater tasks, an enhanced three-dimensional tracking control method is proposed for autonomous underwater vehicle (AUV) based on fuzzy model predictive control-finite time terminal sliding mode control (FMPC-FTTSMC). Firstly, to enhance adaptability to tasks and environments, fuzzy model predictive control method is designed to achieve precise three-dimensional trajectory tracking. Additionally, a fuzzy weight allocator is employed to enable AUV to autonomously adjust optimization strategies based on real-time states such as task type, speed, and distance from the seabed, thus better coping with changing conditions and improving the universality of the control method. Secondly, a dynamic controller is designed using the finite-time terminal sliding mode control method, combined with a finite-time radial basis function neural network (FTRBFNN) disturbance observer to accurately estimate disturbances. Finally, simulation results demonstrate that the proposed method effectively reduces optimization solving time compared to traditional model predictive control, achieves trajectory tracking control under various conditions, meets preset state constraints, and demonstrates excellent tracking accuracy and robustness.

Development and Application of Modern Intelligent Control Theory

Modern intelligent control theory has experienced multi-stage development from classical control to fuzzy control, neural networks, and model predictive control, which not only improves the flexibility and adaptability of control systems but also promotes interdisciplinary research. With the advancement of science and technology and social development, intelligent control theory will be further combined with machine learning, big data, and other fields in the future to promote industrial automation and technological innovation. This article reviews the development history of modern intelligent control theory, its main theories, and its applications in robot manufacturing, power electronics, and other fields. It also discusses the current challenges and future development trends in the field of intelligent control.

A Comprehensive Review on the Role of Artificial Intelligence in Power System Stability, Control, and Protection: Insights and Future Directions

This review comprehensively examines the burgeoning field of intelligent techniques to enhance power systems’ stability, control, and protection. As global energy demands increase and renewable energy sources become more integrated, maintaining the stability and reliability of both conventional power systems and smart grids is crucial. Traditional methods are increasingly insufficient for handling today’s power grids’ complex, dynamic nature. This paper discusses the adoption of advanced intelligence methods, including artificial intelligence (AI), deep learning (DL), machine learning (ML), metaheuristic optimization algorithms, and other AI techniques such as fuzzy logic, reinforcement learning, and model predictive control to address these challenges. It underscores the critical importance of power system stability and the new challenges of integrating diverse energy sources. The paper reviews various intelligent methods used in power system analysis, emphasizing their roles in predictive maintenance, fault detection, real-time control, and monitoring. It details extensive research on the capabilities of AI and ML algorithms to enhance the precision and efficiency of protection systems, showing their effectiveness in accurately identifying and resolving faults. Additionally, it explores the potential of fuzzy logic in decision-making under uncertainty, reinforcement learning for dynamic stability control, and the integration of IoT and big data analytics for real-time system monitoring and optimization. Case studies from the literature are presented, offering valuable insights into practical applications. The review concludes by identifying current limitations and suggesting areas for future research, highlighting the need for more robust, flexible, and scalable intelligent systems in the power sector. This paper is a valuable resource for researchers, engineers, and policymakers, providing a detailed understanding of the current and future potential of intelligent techniques in power system stability, control, and protection.

Two-dimensional iterative learning hybrid robust fuzzy predictive control for nonlinear multi-phase batch processes with asynchronous switching

For nonlinear multi-phase batch processes with uncertainties, time-varying delays, and unknown bounded disturbances, an iterative learning hybrid robust fuzzy predictive control approach is developed within the context of two-dimensional systems. First, a two-dimensional T-S fuzzy Fornasini-Marchesini comprehensive feedback error model is constructed, which includes a state increment and an output error. Based on the established model and considering an asynchronous switching situation between a controller and a system state, a feedback error switching model, including match and mismatch cases, is developed. Then, a two-dimensional iterative learning hybrid robust fuzzy predictive controller is built, based on the established switching model. Second, building on related theories and methodologies, the stability conditions are derived by a linear matrix inequality form, while the system's asymptotic and exponential stability are analyzed. Next, the stability conditions are calculated to get the control law gains, minimum and maximum dwelling times. The acquired gains of control law greatly minimize the controller's learning cycle, while the advanced switching signal based on the obtained dwelling times is dynamically adjusted to ensure a smooth transition and the system's stability during switching. Ultimately, a simulation case is utilized to validate the proposed method's effectiveness and feasibility.

Research on Predictive Control Method of Pigsty Environment Based on Fuzzy Control

At present, most of the environmental control systems of pigsties use direct control methods; when factors, such as temperature and humidity, exceed the set threshold value, the corresponding actuator is turned on for regulation. However, such methods have problems such as low control accuracy, high energy consumption, and untimeliness. In order to save on energy consumption and improve control accuracy, this paper takes the predicted value, set value, and current detection value of the internal environment of a pigsty as input, and combines fuzzy control and direct control methods to realize the predictive control of the pigsty environment. The test results show that, compared with the direct control method, the fuzzy predictive control method can make fluctuations in the internal temperature and humidity of the pigsty less close to the set threshold value, while the ammonia concentration hardly exceeds the set threshold value. The results show that predictive control can more accurately control the internal environment of the pigsty and reduce energy costs by about 20%. Therefore, this method can provide scientific and effective environmental control methods for agricultural production processes, such as livestock breeding and greenhouse cultivation, in semi-confined spaces.

Retraction Note: Fractional- order T- S fuzzy predictive control based free- form Surface reconstruction

Retraction Note: Fractional- order T- S fuzzy predictive control based free- form Surface reconstruction

Enhancing grid stability through predictive control and fuzzy neural networks in flywheel energy storage systems integration

Renewable energy systems, exemplified by solar and wind power, are increasingly integrated into modern power grids to mitigate environmental impact and reduce reliance on fossil fuels. However, the inherent intermittency and unpredictability of renewable energy sources pose challenges to grid stability and reliability. Energy Storage Systems (ESS) offer a promising solution to address these challenges by smoothing out power fluctuations and ensuring a consistent power supply. Among various ESS technologies, Flywheel Energy Storage Systems (FESS) have emerged as a noteworthy contender due to their rapid response times, low operating costs, and extended lifespan. This paper focuses on investigating the operation of a novel unit comprising a solar power system integrated with a Flywheel Energy Storage System (PV-FESS). The aim is to develop an effective control algorithm utilizing adaptive fuzzy neural networks and model predictive control (ANFIS-MPC) to manage power fluctuations stemming from renewable energy sources within the grid. The proposed control strategy aims to optimize the operation of the PV-FESS system by dynamically adjusting the energy absorption or release of the flywheel to maintain grid stability. Simulation studies conducted using Matlab-Simulink/Simcape software validate the efficacy of the ANFIS-MPC algorithm in mitigating abnormal fluctuations from renewable energy sources. The results demonstrate that the PV-FESS system effectively balances power fluctuations, ensuring a stable and reliable power output to the grid.

Application of fuzzy prediction control model based on neural network in teaching resource recommendation and matching

As technology evolves, the allocation and use of educational resources becomes increasingly complex. Due to the many factors involved in recommending and matching English education resources, traditional predictive control models are no longer adequate. Therefore, fuzzy predictive control models based on neural networks have emerged. To increase the effectiveness and efficiency of using English educational resources (EER), this research aims to create a neural network-based fuzzy predictive control model (T-S-BPNN) for resource suggestion and matching. The results of the study show that the T-S-BPNN model α proposed in the study starts from 0 and increases sequentially by 0.1 up to 1, observing the change in MAE values. The experiment’s findings demonstrate that the value of MAE is lowest at values around 0.5. The T-S-BPNN model, on the other hand, gradually plateaued in its adaptation rate up to 7 runs, reaching about 9.8%. The accuracy rate peaked at 0.843 when the number of recommendations reached 7. The recall rate also peaked at 0.647 when the number of recommended English courses reached 7. The R-value for each set hovered around 0.97, which is a good fit. And the R-value of the training set is 0.97024, which can indicate that the T-S-BPNN model model proposed in the study fits well. It indicates that the algorithm proposed in the study is highly practical.

Fuzzy predictive controller for trajectory tracking of a wheeled mobile root

This paper presents a mobile robot control methodology that uses a fuzzy predictive control system to accurately track given paths. It is specifically designed for scenarios of two successive and distinct paths within a shared reference trajectory. Through the combination of fuzzy logic and predictive control methods, the system aims to significantly enhance tracking accuracy. Modelling the system, using a T-S fuzzy system provides a comprehensive model framework to optimize the tracking process. Furthermore, the use of a well-tuned fuzzy system facilitates dynamic adjustments of the weighting matrices of the predictive controller. The combination of fuzzy logic and predictive techniques results in a robust control system capable of handling complex tracking tasks. The simulation results describe the accuracy, robustness, and efficiency of the suggested control strategy. The system is particularly effective in scenarios with two successive paths within a shared reference trajectory, where precise tracking is essential. This approach is crucial for mobile robots or vehicles navigating complex, changing environments.

Data-driven ensemble optimal compensation control for partially known delayed and persistently disturbed nonlinear systems

In almost all industrial processes, the development of an accurate mathematical model that comprehensively characterizes the regulated system poses a substantial obstacle. Diverse factors, such as unidentified system components and time-varying external disturbances, contribute to this phenomenon. This can result in a disparity between the model and the plant and thereby compromising the performance of the designed controller. In this study, a novel data-driven ensemble optimal compensation control framework is proposed for partially known nonlinear systems with state delays and structural disturbances. Initially, the estimated system model is leveraged to construct a robust fuzzy model predictive controller (RFMPC) based on the Lyapunov–Krasovskii functional (LKF), ensuring the input-to-state stability (ISS) of the plant. Then, a reinforcement learning (RL)-based compensator is assembled to gradually minimize the model-plant mismatch across the entire state space. To streamline the training process of the compensator and facilitate its stability, a dynamic network update scheme is devised, incorporating a moving average gradient calculation method and a learning rate tuning strategy. Furthermore, comparative discussions with some prevalent methods are demonstrated. With the established control framework, the performance of the RFMPC can be securely enhanced, and convergence is ensured within a narrow proximity to equilibrium, despite time-varying perturbation dynamics. By integrating this approach with deep learning (DL) methods, the efficacy of numerous industrial processes could be significantly improved. A numerical simulation and a discretized continuous-time stirring tank reactor (CSTR) example validate the effectiveness and benefits of the proposed control strategy.

Application of Fuzzy Predictive Control for Smoke Sensing Alarm in Automatic Train Driving Control

Application of Fuzzy Predictive Control for Smoke Sensing Alarm in Automatic Train Driving Control

Robust fuzzy model predictive control for nonlinear discrete‐time systems

SummaryThis paper studies the issue of Robust Fuzzy Model Predictive Control (RFMPC) of nonlinear discrete‐time systems in the presence of disturbance. To this end, Takagi‐Sugeno (T‐S) fuzzy model is adopted in order to characterize uncertain nonlinear systems and facilitate providing controller for such systems. Non‐parallel distributed compensation (non‐PDC) technique is applied to design improved RFPMC with better performance. The optimization problem of RFMPC is represented in terms of linear matrix inequalities (LMIs) so as to decrease online computational burden. Besides, feasibility and stability which are decisive factors in MPC theory are investigated for the proposed method. Ultimately, two examples including cart‐damper‐spring system are simulated to evaluate the results of this research work.

Optimal model predictive fuzzy control of DC–DC convertor

AbstractNowadays, controlling industrial processes and choosing the proper method for this purpose is very important. DC–DC converters are widely used in industrial applications, such as DC–DC step‐up converters of 150 and 250 V in power drives and power supplies of portable electrical and hybrid vehicles. A predictive control algorithm is a method to deal with such complex processes. Many methods have been proposed as predictor control, leading to better and more accurate evolution with problems. This study proposes a fuzzy explicit predictive control method for adjusting the boost DC–DC converter. A fuzzy method for selecting the weights of the cost function of the predictive control algorithm of the DC–DC converter will be presented. In addition, for better evaluation and analysis, the designed controller is compared with similar methods, and the simulation results show that the controller designed on this system has had a proper performance with other methods in realizing the desired goals.

A Novel Modified Fuzzy-Predictive Control of Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

A Novel Modified Fuzzy-Predictive Control of Permanent Magnet Synchronous Generator Based Wind Energy Conversion System