Fuzzy Predictive Control Research Articles

A review of thermal management of proton exchange membrane fuel cell systems

Proton exchange membrane fuel cells (PEMFCs) have the advantages of long operation cycles, high energy efficiency, and no pollution of reaction products. Temperature is an important factor to ensure the operation of fuel cell systems. Too high temperature will cause irreversible damage to the proton exchange membrane, and too low temperature will greatly reduce the power generation efficiency of fuel cells. Therefore, the effective thermal management temperature control can ensure the stable operation of the system under steady state and dynamic variable load. It can also improve the reaction efficiency of the fuel cell system and prolong the life of the fuel cell. This paper mainly summarized the cooling mode and control strategy of PEMFCs based on thermal management system. The application of different cooling methods is further discussed. The characteristics of traditional proportional-integral-derivative (PID) control, fuzzy PID control, predictive control, adaptive control, and other common thermal management control strategies were described in detail. The research status of scholars in various countries were analyzed, respectively, and the cooling effects of different strategies are compared. Through the review and research on the temperature control of PEMFCs, it is found that the stable operation of the stack is inseparable from the coordination of reasonable cooling mode and control strategy.

Design of High–performance Fuzzy–Predictive Controllers for a Photovoltaic/Battery Pumping System

Design of High–performance Fuzzy–Predictive Controllers for a Photovoltaic/Battery Pumping System

Model Predictive Control Based Type-3 Fuzzy Estimator for Voltage Stabilization of DC Power Converters

The stability of power electronic devices is prone to instability when supplying constant power loads (CPL) due to their negative impedance specifications. This necessitates an efficient control methodology to overcome the destabilization issue and regulate the response of the output voltage. In particular, this article proposes a novel interval type-III fuzzy neural network-based model predictive control (IT3FNNMPC) to mitigate the destructive effects of CPL in dc/dc boost converters implemented in the microgrid (MG) and power system distribution. In a typical MGs configuration, the IT3FNNMPC controller is formulated and designed in a constraint control problem to achieve a fast-transient response of system voltage. The feasibility and applicability of the designed IT3FNNMPC controller have been validated by hardware-in-the-loop real-time simulations.

Fuzzy subspace-based constrained predictive control design for a greenhouse micro-climate

Fuzzy subspace-based constrained predictive control design for a greenhouse micro-climate

Robust Control for Dynamic Train Regulation in Fully Automatic Operation System Under Uncertain Wireless Transmissions

For enhancing the operation efficiency of the fully automatic operation (FAO) system in the urban rail transit (URT), this paper investigates the robust dynamic train regulation problem with respect to frequent disruptions and imperfect wireless transmissions. To better express the characteristic of the arriving passengers, the fuzzy passenger arrival rate is adopted to address the uncertainty of the passenger flow, and a T-S fuzzy state-space model is established to express the periodical movement of the train traffic in an URT loop line. By considering the possible packet dropout phenomenon during the wireless data transmissions, which may lead to the instability of the train traffic system and degrade the performance of the regulation strategy, a robust real-time train regulation strategy is developed based on the fuzzy predictive control theory, which distinguishes existing studies in that the uncertainty dropout rate is contemplated to address the complexity of the actual operation environment. A sufficient condition for the proposed control law is presented to guarantee that the nominal train schedule is recovered from disturbed situations with a given attenuation level by means of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> performance index, and meanwhile the optimization of the upper bound on the objective function balancing the service efficiency and control cost is achieved. Numerical simulations based on the Beijing subway loop line 2 are presented for demonstration of the effectiveness of the introduced strategy.

A Review of Compensation Topologies and Control Techniques of Bidirectional Wireless Power Transfer Systems for Electric Vehicle Applications

Owing to the constantly rising energy demand, Internal Combustion Engine (ICE)-equipped vehicles are being replaced by Electric Vehicles (EVs). The other advantage of using EVs is that the batteries can be utilised as an energy storage device to increase the penetration of renewable energy sources. Integrating EVs with the grid is one of the recent advancements in EVs using Vehicle-to-Grid (V2G) technology. A bidirectional technique enables power transfer between the grid and the EV batteries. Moreover, the Bidirectional Wireless Power Transfer (BWPT) method can support consumers in automating the power transfer process without human intervention. However, an effective BWPT requires a proper vehicle and grid coordination with reasonable control and compensation networks. Various compensation techniques have been proposed in the literature, both on the transmitter and receiver sides. Selecting suitable compensation techniques is a critical task affecting the various design parameters. In this study, the basic compensation topologies of the Series–Series (SS), Series–Parallel (SP), Parallel–Parallel (PP), Parallel–Series (SP), and hybrid compensation topology design requirements are investigated. In addition, the typical control techniques for bidirectional converters, such as Proportional–Integral–Derivative (PID), sliding mode, fuzzy logic control, model predictive, and digital control, are discussed. In addition, different switching modulation schemes, including Pulse-Width Modulation (PWM) control, PWM + Phase Shift control, Single-Phase Shift, Dual-Phase Shift, and Triple-Phase Shift methods, are discussed. The characteristics and control strategies of each are presented, concerning the typical applications. Based on the review analysis, the low-power (Level 1/Level 2) charging applications demand a simple SS compensation topology with a PID controller and a Single-Phase Shift switching method. However, for the medium- or high-power applications (Level 3/Level 4), the dual-side LCC compensation with an advanced controller and a Dual-Side Phase-Shift switching pattern is recommended.

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.

Complexity reduction of explicit MPC based on fuzzy reshaped polyhedrons for use in industrial controllers

The explicit model predictive control (EMPC) generates the rules of control defined for a set of polyhedral regions. Online EMPC calculations consist of searching a look-up table to find the appropriate control law according to a particular state. This paper discusses the complexity of online computation and the memory required to store data in an EMPC implementation. Therefore, a new reshaping method is applied to the active regions so that the definition of the polyhedron has regular boundaries. This approach has made some improvements. First, the usable memory will be a lot less for the actual implementation compared to the traditional EMPC approach. Second, the small number of new clusters reduces search time in explicit lookup tables and speeds up overall implementation. To this end, fuzzy clustering is used to introduce a novel method of transforming polyhedrons in the context of fuzzy explicit model predictive (FEMPC) control, followed by a new fuzzy-based piece-wise affine (PWA) explicit formulation for control law calculations. The stability of the proposed method is investigated using the Lyapunov stability criteria. The proposed algorithm has been tested on a nonlinear continuous stirred tank reactor (CSTR) benchmark system and simulation tests show that the proposed approach involves a compromise between storage space requirements and online efficiency.

Adaptive fuzzy Lyapunov-based model predictive control for parallel platform driving simulators

The paper proposes an adaptive Lyapunov-based nonlinear model predictive control (MPC) to cope with the problems in nonlinear systems subjecting to system constraints and unknown disturbances of the parallel car driving simulator. Commonly, standard nonlinear controllers could guarantee the overall system stability for the parallel structure. However, the constraints tend to impact the control performance and stability adversely. Therefore, MPC plays a vital role in the proposed technique to explicitly consider all the practical constraints and simultaneously enhance the system’s robustness. Nevertheless, the accuracy of the modeling process has a significant effect on the MPC performance, and thus, the convergence cannot be guaranteed in the presence of the model uncertainties. To overcome this problem, by the merit of the fuzzy adaptive law, the control system takes the disturbances and unmodelled parameters into account. Moreover, the feasibility and stability of the approach, which is the fundamental problem of MPC, are ensured according to the Lyapunov-based nonlinear controller, backstepping aggregated with sliding mode control (SMC), and hence inherit advantages of these controls. Simulation results show the efficiency and superior constituted controllers of the proposed method.

Generalized fuzzy cascade predictive functional control design for different types of challenging dynamics systems

This paper introduces a generalized form of cascade predictive functional control. The inner loop includes a state feedback controller, which aims to increase damping and stabilize the system. The outer loop utilizes an output feedback controller (OFC) to track reference and reject disturbance. The gains of the proposed controller structure are obtained optimally concerning to a specified prediction horizon through the predictive functional controller (PFC) method, which is improved by a fuzzy logic controller (FLC). The designed FLC plays a trade-off role between inner loop and outer loop gains values by tuning weighting factors in the PFC approach. In this paper, the proposed solution is formulated to convert the tracking problem into a regulation problem which results in complexity reduction and lower computational cost. Moreover, based on the proposed solution, a generalized form of OFC is obtained concerning to disturbance and reference characteristics. As a comparative study, the proposed controller is applied to the different types of challenging dynamics systems and compared to the discrete-time linear quadratic regulator (DLQR) control technique to reveal the benefits and reliable performance of the proposed control structure.

Embedded predictive controller based on fuzzy linear parameter-varying model: A hardware-in-the-loop application to an ESP-lifted oil well system

This work addresses the development of a fuzzy infinite horizon model predictive control (FIHMPC) strategy and its application to an ESP-lifted oil well system in an embedded hardware. The proposed scheme is based on a fuzzy model to approximate the nonlinear system to a linear parameter-varying model and incorporate it into the proposed MPC law. Slacked terminal constraints and zone control scheme are incorporated to the FIHMPC controller assuring optimization feasibility to handle ESP time-varying operation envelope. The implementable zone FIHMPC controller is applied through an ESP32 micro-controller to ESP-lifted oil well system equations, executed in MATLAB, composing a hardware-in-the-loop (HIL) simulation scheme. Nonlinear mismatch scenarios with unmeasured disturbances show the effectiveness of the proposed controller over multiple operating conditions. Computational memory and processing analysis over simulations also validates the controller application.

A T-S fuzzy state observer-based model predictive reset control for a class of fuzzy nonlinear systems with event-triggered mechanism

In this study, the problem of observer-based control for a class of nonlinear systems using Takagi-Sugeno (T-S) fuzzy models is investigated. The observer-based model predictive event-triggered fuzzy reset controller is constructed by a T-S fuzzy state observer, an event-triggered fuzzy reset controller, and a model predictive mechanism. First, the proposed controller utilizes the T-S fuzzy model and is constructed based on state observations and discrete sampling output, which can greatly reduce the occupation of communication resources. Then, the model predictive strategy for reset law design is designed in this paper. With a reasonable reset of the controller state at certain instants, the performance of the reset control systems is improved. Finally, the validity of the proposed method is illustrated by simulation. The merits of the proposed controller in improving transient performance and reducing the communication occupation are demonstrated by comparing its results with the output feedback fuzzy controller and the first-order fuzzy reset controller.

LMI-Based Robust Fuzzy Model Predictive Control of Discrete-Time Fuzzy Takagi-Sugeno Large-Scale Systems Based on Hierarchical Optimization and H∞ Performance

This paper studies robust fuzzy model predictive control of discrete-time fuzzy Takagi-Sugeno large-scale systems based on hierarchical optimization and H_[Formula: see text] Performance. The considered system in this paper is a large-scale system. So, the Takagi-Sugeno (T-S) fuzzy approach which uses nonlinear local models is applied to estimate the nonlinear system to reduce the burden of modeling. The model predictive control (MPC) is a practical application that is useful for all systems but the problem is the online computational cost. For large-scale systems, this problem is the online computational burden. Besides, it leads to conservative or even no solution. By the proposed method, more relaxed solutions are achieved. First, the T-S system with nonlinear local models is adopted. Second, the hierarchical optimization scheme is considered for the optimization part of the MPC. Third, by inspiring the [Formula: see text] performance, uncertainties and disturbances that are appeared are reduced. Moreover, the linear matrix inequalities (LMIs) are used to solve the proposed method that the computational burden significantly is reduced at the same time. Simulation results are finally presented to show the effectiveness of the proposed controller.

Dual-EKF-Based Fault-Tolerant Predictive Control of Nonlinear DC Microgrids With Actuator and Sensor Faults

The issue of a state estimation-based fault-tolerant controller for direct current (dc) microgrids (MGs) is studied in this article. It is considered that the dc MG contains nonlinear constant power load (CPL) and is subjected to actuator faults. Current sensors are not installed and the voltages of the dc MG are measured in the presence of noise and sensor faults. To estimate the system states, a novel dual-Extended Kalman filter is proposed, which simultaneously estimates the states and faults. The fault- and noise-free estimations are then deployed in a nonlinear Takagi–Sugeno fuzzy predictive controller to regulate the dc MG. The proposed method outperforms the exiting results, being robust against faults and noise. Also, the predictive scheme makes it robust against system uncertainties and forces the system states to converge the desired values, precisely. The accuracy and robustness of the developed method are evaluated and compared to advanced state-of-the-art techniques for a typical dc MG with a resistive load, CPL, and energy storage unit.

Adaptive TS fuzzy MPC based on Particle Swarm Optimization-Cuckoo Search algorithm

A novel technique to building an adaptable fuzzy model predictive control (AFMPC) is suggested in this study, based on the algorithm Particle Swarm Optimization-Cuckoo Search (PSOCS). This technique combines the particle swarm optimization (PSO) algorithm’s iterative scheme with the Cuckoo Search (CS) algorithm’s searching approach. To identify the system parameters at each time step, an on-line adaptive fuzzy identification is used. Based on a predictive technique, these factors are utilized to generate the goal function. The PSOCS method is then used to solve the optimization issue and select the best control signal. The suggested controller’s utility is demonstrated using an experimental communicating three-tank system, in which the proposed approach-based PSOCS algorithm outperforms both the approach-based CS and the approach-based PSO algorithms.

Control of Nonlinear Active Suspension System Based on Fuzzy Model Predictive Controller

Recent years, active suspension system has been widely used in automobiles to improve the road holding ability and the riding comfort. This article presents a new fuzzy model predictive control for a nonlinear quarter car active suspension system. A nonlinear dynamical model of active suspension is established, where the nonlinear dynamical characteristic of the spring and damper are considered. Based on the proposed fuzzy model predictive control method is presented to stabilize the displacement of the active suspension in the presence of different road profiles. Parameters of the model predictive and Fuzzy logic control laws are designed to estimate the (Bump and Sinusoidal) road profile input in the active suspension. At last, the reliability of the fuzzy model predictive control method is evaluated by the MATLAB simulation tool. Simulation result shows that the fuzzy model predictive control method obtained the satisfactory control performance for the active suspension system.

Incremental state model in predictive control: A new fuzzy control proposal for nonlinear systems

In this work, a fuzzy predictive optimal control for multivariable nonlinear systems with pure time delays is presented. Therefore, dynamic local linear state models are used at each point of the state space obtained by fuzzy Takagi-Sugeno (T-S) modelling. The modelling error is considered as white noise, and the state is observed using the Kalman Filter (KF). This method does not take into account possible input constraints. Therefore, it applies to systems where there are no saturation problems. In this work, a new approach in the Model Predictive Control (MPC) method is proposed by calculating the control signal increment as a function of the error between a reference state vector and the prediction at N-steps of the state vector instead of using the traditional MPC approach which is based on calculating the error between a reference and the predicted output. The proposed method has a satisfactory tracking performance. The main feature of the proposed method is that it attains computational savings compared to other methods that have used incremental state models, which making it more appropriate for real-time applications.

A data-driven digital-twin model and control of high temperature proton exchange membrane electrolyzer cells

The high temperature proton exchange membrane electrolyzer cells (HT-PEMEC) are promising for hydrogen generation from fluctuating and intermittent renewable energy. In this study, a data-driven method is developed to study the dynamic behavior of HT-PEMEC. This method combines multiphysics simulation and nonlinear system identification, avoiding expensive experimental costs and time-consuming full multiphysics calculations. Dynamic models for predicting the power consumption, hydrogen production and temperature are identified, and the verified fit is 96.31%, 97.87%, 87.73%, respectively, which demonstrated the accuracy of the identification model. Subsequently, the identification model was used to predict the dynamic behavior of HT-PEMEC and design control strategies. Fuzzy control strategy and neural network predictive control strategy are implemented to alleviate overshoot and suppress fluctuations so as to improve the durability of the electrolyzer. Moreover, compared with the fuzzy control strategy, the neural network predictive control strategy reduces the power overshoot by approximately 92%. This data-drive digital-twin model can not only guide dynamic experimental research, but also can be extended to study the dynamic behavior of various fuel cells and electrolyzer cells. • 2D multiphysics model of HT-PEMEC was developed to generate a database. • The dynamics of power consumption, H 2 generation and temperature are analyzed. • System identification method is used to identify the dynamic behavior of HT-PEMEC. • Control strategies are designed to suppress fluctuations and improve durability.

Automatic control of computer application data processing system based on artificial intelligence

Abstract To shorten the travel time and improve comfort, the automatic train driving system is considered to replace manual driving. In this article, an automatic control method of computer application data-processing system based on artificial intelligence is proposed. An automatic train operation (ATO) introduced the structure and function of an autopilot system (train), optimized the train running on the target curve, introduced the basic principle of fuzzy generalized predictive control (PC) algorithm, and combined with the characteristics of ATO system design the speed controller based on optimization algorithm, the target curve to make use of the designed controller to track and simulation validation. The experimental outcomes demonstrate that when the train runs to 90 s, the displacement difference reaches about 40 m, which proves that the fuzzy PC has better displacement tracking, punctual arrival, and higher stopping accuracy.

Load forecast and fuzzy control of the air-conditioning systems at the subway stations

The average annual electricity consumption of the subway stations in China is 1.8–2.3 million kWh, of which ventilation and air-conditioning systems account for approximately 46%. Optimizing the ventilation and air-conditioning control system is an important energy-saving method in urban rail transit. This study applied the technologies of neural network and fuzzy control to the load forecast and the control of the air-conditioning system in subway stations to reduce the energy consumption of the air-conditioning system. Firstly, the energy consumption of the air-conditioning system was calculated by TRNSYS software. Then, a load forecast model of the air-conditioning system was established using neural network technology, and the accuracy of the load forecast model was verified through comparative analysis. Finally, the predictive fuzzy control model of the air-conditioning system was established. The temperature and the humidity in the subway station with the predictive fuzzy control and the traditional temperature control were studied, as well as the energy consumption of the air-conditioning system. Results showed that the neural network technology could effectively predict the load of the subway station's air-conditioning system. The predictive fuzzy control could offset the delay of control quantity adjustment of the air-conditioning system to a certain extent. Compared with the traditional temperature control method, the temperature fluctuation of the station hall and platform under predictive fuzzy control is smaller, and the total energy consumption of the air-conditioning system in summer is reduced by 7.13%. This study provides a reference for reducing the energy consumption of the air-conditioning system in the urban rail transit stations.