Fuzzy Model Predictive Control Research Articles

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.

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.

Adaptive event-triggered fuzzy MPC for unknown networked IT-2 T-S fuzzy systems

Adaptive event-triggered fuzzy MPC for unknown networked IT-2 T-S fuzzy systems

Comparison of Fuzzy Logic Control and Model Predictive Control for a Smart Adaptive Cruise Control Vehicle System

Adaptive Cruise Control (ACC), Cruise Control (CC), and Automatic Emergency Braking (AEB) serve as the basis of longitudinal automated driving, and as Adaptive cruise control (ACC), cruise control (CC), and automatic emergency braking (AEB) serve as the basis of longitudinal automated driving, and as such have been the subject of much research. Model predictive control (MPC) and fuzzy logic are often considered to be the next steps in improving the capability of these systems, but the two control strategies have not been compared to each other in the ACC, CC and AEB applications. Also, the three features (ACC, CC and AEB) have never been compiled into a single fuzzy logic controller. The purpose of this paper is to design a fuzzy logic-based ACC, CC, and AEB controller and compare it to an equivalent MPC controller. All three controllers control the desired longitudinal acceleration, and their functionality is tested using Matlab’s Fuzzy Logic Designer and other Simulink toolboxes. Ultimately, the results of the analysis demonstrate that the proposed fuzzy controller operates just as well if not better than the MPC controller and that the fuzzy controller is able to operate well in all tested scenarios.

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.

Output feedback fuzzy model predictive control with multiple objectives

We present a dynamic output feedback fuzzy model predictive control (FMPC) approach for the Takagi–Sugeno model with bounded disturbance in this paper. The proposed approach relies on the formulation, at each sampling instant, of a lexicographic optimization problem that takes into account multiple objectives including improving the control performance, enlarging the region of attraction (ROA) and tightening the estimation error set (EES). Since the controller parameters and Lyapunov matrices are kept as degrees of freedom for the lower-ordered optimization problems, larger ROA and less conservative EES are obtained. As a result, the control performance can be greatly improved with respect to previous works. Furthermore, we prove the recursive feasibility of the approach and guarantee the stability of the closed-loop system. The result is verified through a numerical example.

Developing a resilient and sustainable non-linear closed-loop supply chain management framework for the automotive sector industry using a gaussian fuzzy optimization based non-linear model predictive control approach

ABSTRACT Efforts to merge sustainability and resilience within the automotive industry’s supply chain models have proven challenging. This paper proposes a novel non-linear closed-loop supply chain management framework tailored to the tire industry supply chain from the automotive sector to address the issue of exploring interrelationships. Framework employs trapezoidal linguistic cubic fuzzy Z-score technique for order of preference by similarity to the ideal solution ranking approach to prioritize resilience strategies to maintain sustainability performance during sudden disturbances. Furthermore, Gaussian fuzzy optimization-based non-linear model predictive control acts as a feedback controller to integrate sustainability and resilience by providing a stable output based on the objective function related to sustainability dimensions. An experimental study assesses the impact of resilience strategies on total supply chain costs, highlighting significant cost savings. Adopting strategies like multiple sourcing, information sharing, and improved design quality of the supply chain keeps total expected costs optimal for various sustainability levels.

Fuzzy MPPT operation-based model predictive flux control for linear induction motors

This research focuses on a methodology for connecting the linear induction machine (LIM) using model predictive flux control (MPFC) fed by Kyocera solar cells. Additionally, it incorporates a fuzzy logic regulator (FLR) for maximum power point tracking (MPPT) control. The boost converter's characteristics are regulated using MPPT, allowing it to achieve a voltage gain of up to twice the input voltage. The finite state-model predictive flux control (FS-MPFC) is employed to synthesize the three-phase voltage source converter switching states. The paper proposes an improved FS-MPFC strategy for LIMs, eliminating the cost function's need for a weighting parameter. This is achieved by unifying its terms of traditional control methods and reducing the number of calculation steps. The validity and effectiveness of the introduced system are confirmed by extensive simulation results. The findings revealed that the system can accurately extract maximum power from the Kyocera solar cells and track the preset speed values. In addition, it can enhance performance by minimizing the thrust and primary flux under varying speed and load conditions.

Longitudinal and Lateral Stability Control Strategies for ACC Systems of Differential Steering Electric Vehicles

To ensure lateral stability during the cruising of a differential steering vehicle (DSV), this paper presents a curving adaptive cruise control (ACC) system coordinated with a differential steering control (DSC) system, which considers both longitudinal cruising capability and lateral stability on curved roads. Firstly, a DSV dynamics model is developed and a control strategy architecture for a curving ACC system is designed. Then, the car-following control strategy for the curving ACC system is designed based on the fuzzy model predictive control (FMPC) algorithm. The strategy aims to improve the economy and balances car following, safety, comfort and economy. Moreover, fuzzy logic rules are designed to update the weight coefficients of the performance indicators in real time. Finally, the lateral stability controller is designed based on the preview algorithm and the sliding mode control (SMC) algorithm. The simulation results show that the lateral stability of the DSV during the curving cruise is realized via the control of the differential drive torque of the two front wheels. The proposed FMPC controller and SMC controller based on the preview control algorithm satisfy the performance in terms of vehicle following and lateral stable driving in the process of cruising.

Fuzzy model predictive control for frequency regulation of temporary microgrids during load restoration

AbstractTemporary microgrids containing a high proportion of distributed renewable generations can offer great benefits during the recovery stage after large‐scale power system blackout, by picking up critical load and speeding up the entire grid recovery process. To solve the frequency security problem of temporary microgrids faced in the load restoration process, this paper proposes an adaptive fuzzy model predictive control (adaptive fuzzy‐MPC) strategy to obtain effective frequency regulation to suit the special characteristics of this process. First, the load restoration plan within a short time frame is regarded as the feedforward information and assists in the establishment of the MPC model. Then, considering the uncertainty of renewable distributed generations, the regulation resources are cooperated by an adaptive fuzzy controller which provides the weights of different regulation resources adopted in MPC. Specifically, the base weights of the energy storage system and renewable distributed generations are obtained from fuzzy rules, and then the weight for each regulation resource is modified by the designed adjustment rules. Simulation results illustrate that the proposed strategy can efficiently improve frequency performance while making various frequency regulation resources with different regulation capacities cooperate properly, which can further accelerate the load restoration progress and enhance system resilience.

Iterative Neuro-Fuzzy Hammerstein Model Based Model Predictive Control for Wind Turbines

Strong nonlinearity and high volatility are the main features of wind turbine systems. Under the conditions of random wind speed changes, how to quickly control the wind turbine output power within the rated range is a major challenge for wind power system control. This paper proposed an iterative neuro-fuzzy Hammerstein model based model predictive control for wind turbines. Initially, an iterative neuro-fuzzy Hammerstein model is used to characterize the feature of wind turbines. The nonlinear static component's aerodynamic property is estimated using neuro-fuzzy networks, and the linear dynamic component is identified using the AutoRegressive with Exogenous Input (ARX) model. Following this, a generalized wind turbine controlled object with linear properties is constructed by computing the inverse of the nonlinear part, converting the wind turbine's nonlinear control challenge into a linear model control problem. This is done by the unique structure of the Hammerstein model, which allows the linear and nonlinear parts to be separated. Furthermore, a globally convergent parameter learning method is proposed and applied to identify the nonlinear parameters of the Hammerstein model. Eventually, the implementation of the Hammerstein-MPC is compared with the MPC, fuzzy MPC, and PI controllers by FAST simulation. These results demonstrate the superiority of the control effect based on the generalized wind turbine system.

Research on the dynamic positioning of remotely operated vehicles applied to underwater inspection and repair of hydraulic structures

Hydraulic structures (such as dams and aqueducts) can suffer from damages, such as cracks and collapse due to natural aging as well as the impact of natural disasters (such as earthquakes and floods). Determining underwater damage is generally not easy because of significant concealment and safety hazards. Remotely operated vehicles (ROVs) have gradually become capable assistants for the regular underwater maintenance of hydraulic structures. With disturbances from undercurrents and propeller reactions, achieving precise positioning of ROVs has always been a complicated technical problem, and it is also one of the important factors affecting underwater inspections and repair accuracy. To address the environmental disturbances during ROV dynamic positioning, a fuzzy predictive control algorithm combining fuzzy control and model predictive control is proposed in this paper. This algorithm can dynamically adjust the power output of propellers in real-time according to the undercurrent data measured by sensors and carry out differential correction until the ROV reaches the predetermined position. Moreover, the aim of this paper is to analyze disturbances in the longitudinal (X), lateral (Y), and depth (Z) directions during the motion of the ROV. The parameters are adjusted through the algorithm to simulate the dynamic positioning of the ROV's spatial movements. By setting different external environmental conditions, the power adjustment strategy and different dynamic positioning performances of ROVs can be obtained to enhance the adaptability of ROVs in complex environments and their stability in underwater inspection and repair and improve their underwater operation quality and efficiency.

Dual-Mode Robust Fuzzy Model Predictive Control of Time-Varying Delayed Uncertain Nonlinear Systems With Perturbations

For time-varying delayed nonlinear systems with parameter uncertainties and persistent disturbances, an online and an offline robust fuzzy model predictive control (RFMPC) algorithms are proposed in this paper. Both methods guarantee the input-to-state stability (ISS) of the system. Furthermore, a novel alternative optimization (AOP) approach and a dual-mode OP/AOP strategy are proposed to prevent the performance deterioration of the online optimal control (OP) approach due to the challenges in addressing the bilinear matrix inequality (BMI) constraints. With the established AOP and dual-mode OP/AOP techniques, the optimization problem constrained by BMIs is transformed into convex, and a significantly more precise approximation of the ellipsoidal minimal robust positively invariant (EmRPI) set can be calculated. Besides, the system can eventually converge into a more compact ellipsoidal set. These two alternative optimization methods can be easily extended to various nonlinear systems. A numerical example and a continuous-time stirring tank (CSTR) example are provided to validate the effectiveness and advantages of the established methodologies.

Dynamic positioning for semi-submersible platform using stable fuzzy model predictive control

More and more semi-submersible platforms are being employed in the deep-sea industry as a result of the offshore oil and gas industry’s rapid growth, which also raises the bar for the positioning capabilities of semi-submersible platforms’ positioning systems. Model predictive control is frequently used in dynamic positioning control because it is effective at addressing a variety of limitations. Yet, there are still issues to be resolved regarding how to enhance model predictive control’s robustness under various working settings and how to offer workable solutions for model predictive control under stable conditions. The approach of stable fuzzy model predictive control for the dynamic placement of a semi-submersible platform is suggested in this research based on this. This method increases the tracking accuracy and tracking speed of dynamic positioning by incorporating the T-S fuzzy algorithm into model predictive control and allowing the coefficients in the weight matrix of the objective function to be modified adaptively. In addition, the solution is limited to the feasible domain by the addition of the control Lyapunov function, ensuring the stability and efficiency of the entire control procedure. A semi-submersible platform’s fixed-point control and trajectory tracking can be accomplished using the proposed technique, which has better trajectory tracking effects than the conventional model predictive control, according to comprehensive simulation findings.

Adaptive Cruise System Based on Fuzzy MPC and Machine Learning State Observer

Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. Firstly, a deep learning-based dynamic normal wheel load observer is added to the perception layer of the conventional ACC system and the observer output is used as a prerequisite for brake torque allocation. Secondly, a Fuzzy Model Predictive Control (fuzzy-MPC) method is adopted in the ACC system controller design, which establishes performance indicators, including tracking performance and comfort, as objective functions, dynamically adjusts their weights and determines constraint conditions based on safety indicators to adapt to continuously changing driving scenarios. Finally, the executive controller adopts the integral-separate PID method to follow the vehicle’s longitudinal motion commands, thus improving the system’s response speed and execution accuracy. A rule-based ABS control method was also developed to further improve the driving safety of vehicles under different road conditions. The proposed strategy has been simulated and validated in different typical driving scenarios and the results show that the proposed method provides better tracking accuracy and stability than traditional techniques.

Security-based dynamic output-feedback model predictive control for nonlinear systems in T-S fuzzy form subject to deception attacks

This paper is concerned with the security-based fuzzy model predictive control (FMPC) problem for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with deception attacks on the measured outputs. With respect to the unmeasurable system states, the nonlinearity of the system and the destructiveness of deception attacks, the dynamic output-feedback control in the framework of FMPC is adopted, meanwhile, the worst-case optimization problem over the infinite moving horizon is formulated for the performance analysis and control synthesis. By means of the quadratic function approach and the singular value decomposition technique, the non-convexity caused by couplings between decisive variables is coped with and conditions are derived to suffice the terminal constraint set. In addition, to mitigate the destruction of attacks to the recursive feasibility, the inequality analysis technique is applied with the aid of the introduction of special scalars. Based on the establishments, a certain auxiliary optimization problem with solvability is put forward to find the desired controllers, and sufficient conditions are obtained to guarantee that the underlying system subject to deception attacks under the proposed FMPC-based controllers is mean square secure in H2-sense. Finally, an illustrative example is used to demonstrate the validity of the proposed methods.

Optimal design, intelligent fuzzy logic and model predictive control for high-purity ethyl-methyl carbonate and diethyl carbonate production using reactive dividing wall column

Methyl carbonate (EMC) and Diethyl carbonate (DEC), as excellent solvents for lithium-ion electrolytes, are synthesized by transesterification, and consecutive reaction seriously complicate optimization and control issues. Process synthesis, parameters optimization and advanced control are crucial points that should be addressed for this ill-conditioned and highly nonlinear system, which is the motivation and novelty of present work. With single-objective and multi-objective genetic algorithm, intensified processes for EMC and DEC production were optimized respectively with heterogeneous catalyst. And reactive dividing wall column achieved 23% and 13% total annualized cost reductions compared with reactive distillation for two target carbonates. Based on optimal steady-state solution, intelligent fuzzy logic and model predictive control of EMC and DEC production is conducted to clarify their respective strengths in intensified process with consecutive reaction. Sequential optimization for parameters of FLC was carried out with Integral of Squared Error (ISE) as objective. Finally, control performance of FLC and LPMC are compared with that under PID control, and results show that overshoot and transition time under FLC and LMPC are obviously mitigated, along with evident reduction in ISE.

FXESO based FNMPC path following control for underactuated surface vessels with roll stabilisation

External ocean disturbances result in the roll motion of underactuated surface vessels (USVs), which probably have severe effects. To improve the USV capability of path following and speed maintenance in rudder roll stabilisation (RRS) control, a fixed-time extended state observer (FXESO) based fuzzy nonlinear model predictive control (FNMPC) scheme was proposed. A 4-degrees of freedom (DOF) nonlinear USV model is established for investigation. Subsequently, the FXESO is designed to estimate the velocity states and lumped disturbance terms of the USV model, which are used to simplify the predictive model. Subsequently, the disturbance compensation nonlinear model predictive control (NMPC) with fuzzy rules is designed to handle multi-objective cooperative control problems between path following and RRS. Fuzzy rules are designed to improve the performance of path following and speed maintenance by altering the objective function weights in MPC. The stability of proposed scheme was theoretically proven. The effectiveness of proposed algorithm was verified through simulations and comparisons.

A Novel Integral Reinforcement Learning-Based H∞ Control Strategy for Proton Exchange Membrane Fuel Cell in DC Microgrids

In this paper, a novel integral reinforcement learning (IRL) based H∞ control algorithm is proposed for proton exchange membrane fuel cells (PEMFCs) to enhance its performance in DC microgrids. The proposed control algorithm produces superior dynamic and steady-state responses without requiring the system model information. The stability of the proposed control algorithm is rigorously proven. Furthermore, its superiority over the PI control, model predictive control (MPC), fuzzy PI control and adaptive MPC is analyzed. Hardware-in-the-loop experimental results are given to verify the effectiveness of the proposed control algorithm.

Stable feedback linearization-based economic MPC scheme for thermal power plant

The major concern of modern power plants is changing from tracking control to environmental and economic issues. The economic model predictive control (EMPC) scheme, which incorporates the boiler-turbine unit's dynamic tracking and economic optimization into one online framework, can well enhance the dynamic economic performance. Considering the strong nonlinearity that existed in the boiler-turbine system, this paper presents an advanced EMPC scheme based on the input/output feedback linearization (IOFL) approach. The boiler-turbine dynamics are converted via the IOFL method to a linear form, which can be readily used to constitute the standard EMPC scheme. A dual-mode method is adopted in this paper to guarantee the stability of the IOFL EMPC strategy for the boiler-turbine system, in which the first-mode optimizes the economic objective function while preserving the states of the system within a feasible region, and the second-mode moves the system state to the optimum operating point using an auxiliary controller. The simulations under the MATLAB environment demonstrate that the application of the IOFL-based EMPC scheme enhances the economic and dynamic output performance under load demand changes in comparison with fuzzy hierarchical MPC and fuzzy economic MPC schemes.