Fuzzy Compensator Research Articles

A fuzzy compensation - Koopman MPC design for pressure regulation in PEM 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 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.

Control of multi-energy drying system: Optimal weighted combination prediction of moisture content and fuzzy compensation of wind speed

In order to monitor the moisture content accurately and improve the drying uniformity of the same batch of materials, an online moisture monitoring approach based on optimal weighted combination model (OWCM) was developed, and a fuzzy control strategy of wind speed compensation was established for multi-field coexistence. The Human Machine Interface (HMI) and remote monitoring modules are designed, which can automatically obtain, view and store environmental parameters on site and mobile devices. The system was loaded into a kelp drying equipment, and moisture content prediction and drying uniformity verification tests were conducted respectively. The results showed that Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the OWCM are 0.022 and 0.019, the prediction accuracy was better than that of single model and average weighted combined model (AWCM). The drying time difference between the front and back of the same batch of kelp was shortened from 1 ∼ 1.5 h to 0 ∼ 0.5 h. This study provides theoretical and technical support for the synergistic utilization of multiple energies and the research and development of aquatic products processing equipment.

Implementation and optimization of perturbation currents for vehicular proton exchange membrane fuel cells online electrochemical impedance spectroscopy measurements

AbstractThis paper presents an implementation method of perturbation currents for vehicular proton exchange membrane fuel cell (PEMFC) online electrochemical impedance spectroscopy (EIS) measurements. The topology of the parallel dual‐boost DC/DC converter system for the EIS measurement is presented. The DCdc and DCac modules in the converter system implement the DC current and the sinusoidal EIS perturbation current independently. Simulation results show that the proposed perturbation current generation method can be implemented efficiently. In the frequency domain, the current of DCdc couples to the perturbation current of DCac, leading to a reduction in the accuracy of the current amplitude after superposition. The mechanism of current amplitude reduction after superposition is discussed. Feed‐forward compensation and fuzzy compensation optimization are proposed for the DCdc current control. Both compensation algorithms achieve excellent results. A comprehensive framework for evaluating the compensation effect is presented. The evaluation results show that feed‐forward compensation has a better advantage in solving the above problems due to its simplicity and less impact on hardware control. Experimental results show that with the optimization algorithm, the input perturbation current increases from 6% to 83% of the theoretical value.

Fuzzy compensation of model predictive control for PMSMs with external disturbances

In some high-speed scenarios, encoders may prove ineffective in providing real-time feedback, thereby increasing the installation and maintenance costs of Permanent Magnet Synchronous Motors (PMSMs). Consequently, the development of sensorless algorithms for medium and high-speed scenarios will enhance the practical applicability of PMSMs. In contrast to the angle estimation scheme for a rotated coordinate system, model predictive control (MPC) commences from the stationary coordinate system to explore Lyapunov stability and directly derive speed and angle based on the stability condition. The primary advantage of this approach lies in its ability to mitigate the accumulation of observation angle iteration errors, while simultaneously simplifying stability conditions. Furthermore, it is important to note that uncertain factors will significantly impede the performance of PMSMs. Subsequently, various fuzzy schemes are introduced to mitigate the impact of uncertain factors in the sensorless mode, and a rigorous proof is provided for the relationship between the fuzzy error and current error. Furthermore, a comprehensive analysis of controller stability conditions is conducted. Ultimately, experimental results demonstrate that the proposed fuzzy compensations effectively counteract external disturbances.

Adaptive decentralized fuzzy compensation control for large optical mirror processing systems

PurposeLarge optical mirror processing systems (LOMPSs) consist of multiple subrobots, and correlated disturbance terms between these robots often lead to reduced processing accuracy. This abstract introduces a novel approach, the nonlinear subsystem adaptive dispersed fuzzy compensation control (ADFCC) method, aimed at enhancing the precision of LOMPSs.Design/methodology/approachThe ADFCC model for LOMPS is developed through a nonlinear fuzzy adaptive algorithm. This model incorporates control parameters and disturbance terms (such as those arising from the external environment, friction and correlation) between subsystems to facilitate ADFCC. Error analysis is performed using the subsystem output parameters, and the resulting errors are used as feedback for compensation control.FindingsExperimental analysis is conducted, specifically under the commonly used concentric circle processing trajectory in LOMPS. This analysis validates the effectiveness of the control model in enhancing processing accuracy.Originality/valueThe ADFCC strategy is demonstrated to significantly improve the accuracy of LOMPS output, offering a promising solution to the problem of correlated disturbances. This work holds the potential to benefit a wide range of practical applications.

Estimated-State Feedback Fuzzy Compensator Design via a Decentralized Approach for Nonlinear-State-Unmeasured Interconnected Descriptor Systems

This paper investigates the decentralized fuzzy control problems for nonlinear-state-unmeasured interconnected descriptor systems (IDSs) that utilize the observer-based-feedback approach and the proportional–derivative feedback control (PDFC) method. First of all, the IDS is represented as interconnected Takagi–Sugeno (T–S) fuzzy subsystems. These subsystems can effectively capture the dynamic behavior of the system through fuzzy rules. For the stability analysis of the system, this paper uses the free-weighing Lyapunov function (FWLF), which allows the designer to set the weight matrix, to achieve the desired control performance and design the controller more easily. Furthermore, the control problem can be transformed into a set of linear matrix inequalities (LMIs) through the Schur complement, which can be solved using convex optimization methods. Simulation results confirm the effectiveness of the proposed method in achieving the desired control objectives and ensuring system stability.

Experimental verification of active damping of powertrain vibrations with simple fuzzy logic compensation for time-varying control period

To ensure comfortability and lifetime of components, transient vibrations in a vehicle powertrain must be suppressed. This study proposes a novel active vibration control strategy with straightforward fuzzy inference compensation for time-fluctuations of control periods of engines used as actuators. First, a model prediction algorithm including a sampled-data controller (SDC) is applied for addressing the maximal phase lag of the control input caused by the fluctuated control period. Fluctuated renewal timings of the control input that are deviated from those of the periodical operated SDC are defined by fuzzy sets. These fuzzy sets are expressed as “Nearly past timing” and “Nearly future timing.” Using a human-intuition-like fuzzy compensation with only four inference rules, unknown control inputs at fluctuated update timings are reasonably determined from such fuzzy sets and periodical control signals given by the SDC. Experiments using an actual test device are performed to investigate the damping performance of the proposed control scheme. The experimental tests demonstrate that the novel active damping strategy significantly reduces transient vibrations despite the fluctuated control period. Moreover, several different test conditions newly reveal the robustness of the fuzzy compensation against fluctuations of variable regions in the control periods.

Formation Control of Non-Holonomic Mobile Robots: Predictive Data-Driven Fuzzy Compensator

A key research topic in the field of robotics is the formation control of a group of robots in trajectory tracking problems. Using organized robots has many advantages over using them individually, such as efficient use of resources, increased reliability due to cooperation, and better resistance against defects. To achieve this, a controller is proposed that steers the leader robot and subsequent follower robots asymptotically to a reference trajectory. The basic controller is feedback linearization. To ensure stability against perturbations, a compensator based on type-3 fuzzy logic systems (T3-FLSs) and a data-driven control strategy is designed. The approach involves employing a finite number of open-loop data and using the model-based predictive controller (MPC) approach to acquire sufficient criteria for stability. An infinite-horizon function is minimized online, which allows the data-based control policy to be considered the optimal control method. The gains of the constrained data-based control signal are computed at each time step to enhance accuracy. Applying the data-based state feedback controller to the system yields positive and stable state trajectories with appropriate transient responses. The suggested data-driven compensator is guaranteed to handle constraints. A practical example is simulated to evaluate the proposed strategy.

Adaptive Wireless Power Transfer System With Relay Transmission and Communication

Wireless power transmission (WPT) systems have been increasingly utilized for wireless charging of electric vehicles. However, most research efforts or commercial products only address wireless power transmission but not wireless data communication. In addition, limitation of the transmission range is always a gap obstructing its popularity. Accordingly, this article presents a novel WPT scheme incorporating both relay transmission and magnetic field data communication. For power transmission, a resonant inverter with pulsewidth modulation was developed using relay coils to induce longer distance alternating current. For data transmission, a magnetic field communication system based on impedance changes and not reliant on extra radio frequency links was realized. Fuzzy compensation was used to stabilize the transmission of both power and data to counter changes in mutual inductance between coils. Phase-shift keying was utilized to generate digital signals for data transmission. Our experimental prototype was tested and verified to simultaneously transmit power and data in compliance with SAE TIR J2954 specifications using real-world experiments.

Real-Time Implementation of an Adaptive PID Controller for the Quadrotor MAV Embedded Flight Control System

This paper presents the real-time implementation of an altitude-embedded flight controller using proportional, integral, and derivative (PID) control, adaptive PID (APID) control, and adaptive PID control with a fuzzy compensator (APIDFC) for a micro air vehicle (MAV), specifically, for a Parrot Mambo Minidrone. In order to obtain robustness against disturbance, the adaptive mechanism, which was centered on the second-order sliding mode control, was applied to tune the classical parameters of the PID controller of the altitude controller. Additionally, a fuzzy compensator was introduced to diminish the existence of the chattering phenomena triggered by the application of the sliding mode control. Four simulation and experimental scenarios were conducted, which included hovering, as well as sine, square, and trapezium tracking. Moreover, the controller’s resilience was tested at 1.1 m above the ground by adding a mass of about 12.5 g, 15 s after the flight launch. The results demonstrated that all controllers were able to follow the reference altitude, with some spike or overshoot. Although there were slight overshoots in the control effort, the fuzzy compensator reduced the chattering phenomenon by about 6%. Moreover, it was found that in the experiment, the APID and APIDFC controllers consumed 2% and 4% less power, respectively, when compared to the PID controller used to hover the MAV.

The Prediction of Badminton Flight Trajectory Based on an Intelligent Compensator

A real-time visual servo badminton trajectory tracking system (VSBTTS) is proposed in this study. VSBTTS is composed of two parts: the image unit and the tracking operation unit. The primary function of the image unit is to segment the moving shuttlecock from a continuous image taken by the camera and detect the moving object. The tracking operation unit is used to track parts of the shuttlecock found in the image unit. Then, the flight trajectory of the shuttlecock is estimated; however, it is difficult for tracking operation units to avoid estimation errors, preventing them from achieving full accuracy. In this study, a fuzzy compensation controller is used to compensate for the system estimation errors of the flight trajectory. Using the fuzzy compensator, the trajectory of the shuttlecock is accurately estimated. Experimental results demonstrate the usefulness of the proposed shuttlecock trajectory tracking system.

Trajectory tracking control strategy of the gantry welding robot under the influence of uncertain factors

In this paper, aiming at the problem of high-precision trajectory tracking of the gantry welding robot system under the influence of uncertain factors, a composite adaptive fuzzy compensation controller based on the computed torque control strategy has been proposed. The controller is composed of an adaptive fuzzy feedback control strategy and a dead zone adaptive fuzzy control strategy, which realizes high-precision trajectory tracking of the gantry welding robot, improves the solving speed of the control algorithm, and its tracking errors has been reduced. Screw theory and Lie group lie algebra are used to solve the problem of high algorithm complexity of controllers in multi-degree-of-freedom robot systems due to Newton-Euler dynamics modeling. Based on the computed torque control, the adaptive fuzzy feedback control strategy is adopted to compensate for the modeling errors and external disturbances in the gantry welding robot system, and the dead zone adaptive fuzzy control strategy is designed to compensate for the nonlinear dead zone structure with unknown parameters. The Lyapunov equation is introduced to prove the stability of the controller. Finally, the controller designed in this paper is compared with the conventional controller through simulation and experiment on the gantry welding robot platform, which verifies the effectiveness and superiority of the controller.

Position and Attitude Tracking of MAV Quadrotor Using SMC-Based Adaptive PID Controller

A micro air vehicle (MAV) is physically lightweight, such that even a slight perturbation could affect its attitude and position tracking. To attain better autonomous flight system performance, MAVs require good control strategies to maintain their attitude stability during translational movement. However, the available control methods nowadays have fixed gain, which is associated with the chattering phenomenon and is not robust enough. To overcome the aforementioned issues, an adaptive proportional integral derivative (PID) control scheme is proposed. An adaptive mechanism based on a second-order sliding mode control is used to tune the parameter gains of the PID controller, and chattering phenomena are reduced by a fuzzy compensator. The Lyapunov stability theorem and gradient descent approach were the basis for the automated tuning. Comparisons between the proposed scheme against SMC-STA and SMC-TanH were also made. MATLAB Simulink simulation results showed the overall favourable performance of the proposed scheme. Finally, the proposed scheme was tested on a model-based platform to prove its effectiveness in a complex real-time embedded system. Orbit and waypoint followers in the platform simulation showed satisfactory performance for the MAV in completing its trajectory with the environment and sensor models as perturbation. Both tests demonstrate the advantages of the proposed scheme, which produces better transient performance and fast convergence towards stability.

Adaptive Sliding Mode Controller for Robotic Manipulator Tracking Control with Fuzzy Design

This paper introduces an adaptive sliding mode controller design based on fuzzy compensation for efficient robotic manipulator tracking control. This work introduces design of Adaptive Fuzzy Controller based on sliding control principles for Robotic Manipulators. In the work, an adaptive fuzzy sliding mode control algorithm is proposed for tracking control of robot manipulators. The fuzzy system uses a set of fuzzy rules, the parameters of which are modified in real-time by adaptive laws, to approximate unknown nonlinearities. This makes it easier to direct the nonlinear system's output to follow a specific trajectory. An adaptive control algorithm based on the adaptive fuzzy model is created using the Lyapunov approach. Both the chattering and the stable performance are assured.

Dynamic modeling and fuzzy adaptive control strategy for space flexible robotic arm considering joint flexibility based on improved sliding mode controller

Space flexible robotic arms can be installed on space stations or spacecraft to perform on-orbit tasks such as capturing artificial satellites, removing space debris, and assisting astronauts in overhauling. The space flexible robotic arm is a nonlinear complex dynamic system with disturbance torque and nonlinear factors coupled with joint and load flexibility. Many nonlinear terms and disturbance torque in the dynamic equations constitute the uncertain part of the space flexible robotic arm. The space flexible robotic arm is affected by the uncertain part, which will cause trajectory tracking errors. In this paper, the improved sliding mode control strategy based on fuzzy compensation is proposed to compensate for the uncertain part to enhance the trajectory tracking accuracy. Firstly, the dynamics model of the space flexible robotic arm considering joint flexibility is established. Next, the control law of the control strategy is designed by the tanh function. Then, the adaptive law of fuzzy systems is designed by the Lyapunov stability theorem. Finally, numerical simulation analysis and prototype control experiments show that the control strategy can effectively reduce the speed fluctuation of the space flexible robotic arm. Compared with the classical sliding mode control strategy, the control strategy decreased the means of absolute error by 9.1425%, which reveals that the strategy can enhance the trajectory tracking accuracy of the space flexible robotic arm.

Adaptive quantized fuzzy backstepping control of uncertain nonlinear system

ABSTRACT An adaptive quantized fuzzy backstepping controller (AQFBC) is proposed in this study for a strict state feedback nonlinear system with quantized input signal and uncertainties. The designed control scheme contains a backstepping controller, adaptive law, and fuzzy compensator. The main controller in the quantized nonlinear system is based on the backstepping technique. The unknown parameters of the nonlinear system with uncertainties are estimated using an adaptive law. A fuzzy compensator is proposed to guarantee the system stability, which is affected by the quantized input signal. Finally, the effectiveness of the AQFBC is demonstrated using two numerical examples.

Motion Trajectory Control System for Production Line Robots Based on Variable Domain Fuzzy Control

Mechatronics production line is widely used in the production and operation of all walks of life, which greatly reduces the amount of labor of employees, improves the production level of enterprises, and creates great social and economic benefits for enterprises. The traditional manipulator system has modeling uncertainty and is vulnerable to external interference. In this paper, a compound control scheme is designed by combining the torque controller and fuzzy compensator, which effectively improves the system's response speed and greatly reduces the steady-state tracking error of the system. At the same time, the tracking process of the scheme is stable and flexible, and the chattering is minimal, which effectively improves the trajectory tracking performance of the system. Experiments show that the algorithm effectively solves the contradiction between computational complexity and control accuracy, improves the system's response speed, and reduces the system's steady-state tracking error.

Tracking control strategy for space flexible manipulator considering nonlinear friction torque based on adaptive fuzzy compensation sliding mode controller

Space flexible manipulator (SFM) is one of the most promising development directions of space mechanism, which is attributed to more and more spacecraft on-orbit using it to perform maintenance, capture, and assembly operations. The SFM is a complex multi-output dynamic system with nonlinear friction torque. Nonlinear friction torque makes it difficult for precise tracking control of space flexible manipulators. In this paper, nonlinear friction torque in dynamics equations of the SFM is identified by the fuzzy rules based on the universal approximation property. The nonlinear friction torque is identified to improve the preciseness of the control torque in the SFM servo system. Thus, the accuracy of the rotation angle is improved, and the tracking errors of end-effectors are reduced. Firstly, the dynamic equations of the SFM considering nonlinear friction torque are established by the Hamilton principle. Then the sliding mode control strategy is proposed to control the SFM based on adaptive fuzzy compensation. Besides, the stability of the closed-loop servo system in the SFM is proved by the Lyapunov theorem. Finally, numerical simulation analysis and ground control experiments of the SFM are carried out. The results of control experiments show that the tracking error can be reduced in this control strategy. Therefore, the control strategy can improve the control accuracy of the SFM.

Dynamic modeling and fuzzy compensation sliding mode control for flexible manipulator servo system

The flexible manipulator servo system considering the second-order modal and two-dimensional deformation is too complicated, which is not conducive to real-time control. In order to establish dynamic equations that can be controlled in real-time, it is necessary to analyze each nonlinear factor. Based on the results of dynamic analysis, a model simplification method is proposed that ignores the second-order modal deformation. What's more, an improved fuzzy compensation sliding mode control (FCSMC) strategy is proposed to control the angular velocity of the servo system. The friction torque and the ignoring nonlinear factors are compensated by the fuzzy systems. The improved FCSMC strategy can reduce the chattering phenomenon and the angular velocity errors. Simulation experiments and physical control experiments prove the effectiveness of the improved FCSMC strategy. The results show that the improved FCSMC strategy has smaller angular velocity errors. Therefore, the strategy can effectively improve the movement accuracy of flexible manipulators, which is conducive to the precise end-effectors.

Generalized Type-2 Fuzzy Control for Type-I Diabetes: Analytical Robust System

The insulin injection rate in type-I diabetic patients is a complex control problem. The mathematical dynamics for the insulin/glucose metabolism can be different for various patients who undertake different activities, have different lifestyles, and have other illnesses. In this study, a robust regulation system on the basis of generalized type-2 (GT2) fuzzy-logic systems (FLSs) is designed for the regulation of the blood glucose level. Unlike previous studies, the dynamics of glucose–insulin are unknown under high levels of uncertainty. The insulin-glucose metabolism has been identified online by GT2-FLSs, considering the stability criteria. The learning scheme was designed based on the Lyapunov approach. In other words, the GT2-FLSs are learned using adaptation rules that are concluded from the stability theorem. The effect of the dynamic estimation error and other perturbations, such as patient activeness, were eliminated through the designed adaptive fuzzy compensator. The adaptation laws for control parameters, GT2-FLS rule parameters, and the designed compensator were obtained by using the Lyapunov stability theorem. The feasibility and accuracy of the designed control scheme was examined on a modified Bergman model of some patients under different conditions. The simulation results confirm that the suggested controller has excellent performance under various conditions.