Fuzzy Feedforward Control Research Articles

Research on layered control of path tracking for unmanned industrial vehicles based on fully hydraulic steering leakage compensation

Industrial vehicles work in complex terrain, the variable centre of mass position, leakage nonlinearity of full hydraulic steering and other issues lead to poor path tracking stability and accuracy. In this paper, an intelligent hierarchical controller for industrial vehicles’ path tracking is designed with full hydraulic steering leakage compensation, including an upper decision layer and a lower execution layer. The upper decision layer observes the pavement-tyre adhesion coefficients through an extended Kalman filter algorithm, and uses the real-time observations of the adhesion coefficients to establish a variable constraint control for the linear time-varying MPC, and thus performs the path tracking control. The lower actuator layer receives the upper layer’s target steering wheel angle output and performs steering operations. Based on the establishment of a mathematical model of full hydraulic steering considering the leakage characteristics, it analyses the leakage disturbance factors and constructs a fuzzy feed-forward steering leakage compensation controller to compensate for the leakage disturbances in real time for path tracking steering. Simulation and experimental results show that the lateral acceleration, sideslip angle, tyre side slip angle and tracking error of intelligent industrial vehicles under different loads and working conditions are improved by more than 32.4%, 35.8%, 40.2% and 45.8% respectively, and the designed hierarchical controller for the path tracking of intelligent industrial vehicles, which considers the compensation of all-hydraulic steering leakage, can effectively improve the path tracking stability and tracking accuracy of intelligent industrial vehicles under different loads and working conditions.

Application of fuzzy control in the evaporation stage of a first- and second-generation sugarcane ethanol biorefinery

Sugarcane bagasse is a cheap feedstock for one of the most significant biofuel technologies currently being developed: second-generation ethanol (E2G). Most studies regarding E2G production investigate ways to improve the efficiency of this technology. However, studies about its control are still very sparse. In this work, we tried to partially fulfill this gap, addressing the control of a multiple-effect evaporation system of a sugarcane biorefinery. A feedforward fuzzy controller was proposed with membership functions based on a dynamic phenomenological model developed in Environment for Modeling, Simulation, and Optimization (EMSO). This software was also used to carry out simulations to evaluate the disturbance rejection performance. Inference tools based on neural networks and proportional–integral (PI) controllers were also proposed as support to the Fuzzy control system. The tests showed that the Fuzzy scheme outperformed traditional proportional–integral–-derivative (PID) controllers, settling on average 66% faster with a 72% reduction of integral time absolute error (ITAE).

Fuzzy Logic based Control Strategies for Proton Exchange Membrane Fuel Cell System

In this work, new fuzzy logic-based control strategies have been proposed to improve the performance of the non-linear Proton Exchange Membrane Fuel Cell (PEMFC) system. This paper considers the empirical approach to designing the controller. This allows the controller’s design to be straightforward, and the controller can be fine-tuned as and when necessary. Three control strategies have been proposed in this paper, viz. Fuzzy feedforward control strategy (Strategy 1), Fuzzy feedforward controller with a feedback loop (Strategy 2), and Fuzzy feedforward controller with a feedback loop and conditional switch (Strategy 3) are proposed. The performance comparison of the proposed strategies is evaluated in terms of performance indicators such as the integral square error (ISE), integral absolute error (IAE), and total variation (TV) of the closed-loop control system. Furthermore, the proposed strategies keep the voltage at a given setpoint under load current variation. Finally, Strategy 3 is used to regulate the oxygen excess ratio (OER) at a reasonable value under load current variation to avoid oxygen starvation and keep the voltage at a defined point of a PEM fuel cell system. The results show that Strategy 3 provides better performance than other strategies

Variable Pitch Control of Wind Turbine Based on Fuzzy Feedforward and Feedback Linearization Sliding Mode

When the wind turbine operates above the rated wind speed, the pitch angle is usually adjusted to make the output power stable at the rated value. As the traditional linear controller is difficult to achieve better control effect for the large inertia and strong nonlinear wind power generation system, a variable pitch control method combining fuzzy feedforward and feedback linearization sliding mode is proposed in this study. Firstly, the nonlinear wind power model is globally linearized by input/output feedback linearization, and then the feedback controller is designed by combining with sliding mode control. The fuzzy feedforward controller can give the appropriate pitch angle according to the change of wind speed and realize dynamic feedforward compensation. The simulation results show that the proposed control method can make the output power of the wind turbine stable, and has better control accuracy and robustness.

Modeling and Fuzzy Feedforward Control of Fuel Cell Air Supply System

As an important part of the fuel cell subsystem, the air supply system of the proton exchange membrane fuel cell (PEMFC) plays an important role in improving the output performance and durability of fuel cells. It is necessary to control the oxygen excess ratio of fuel cell systems in the process of variable load, preventing the oxygen starvation in the loading process and excessive parasitic power consumption caused by oxygen saturation. At this time, the modeling of fuel cell systems and the development of control strategies are critical. The development of a control strategy depends on the construction of the control model. Aiming at the difficulty of air supply system modeling, this paper uses radial basis function (RBF) neural network and state equation method to establish the dynamic model of air supply systems. At the same time, PID, fuzzy logic plus PID (FL + PID), feedforward plus PID (FF + PID), fuzzy feedforward plus fuzzy PID (FF + FLPID) control strategy are proposed to control the oxygen excess ratio of the system. The simulation results show that fuzzy feedforward plus fuzzy PID (FF + FLPID) has the best effect and the oxygen excess ratio can be followed in 1 s.

Accelerated Adaptive Fuzzy Optimal Control of Three Coupled Fractional-Order Chaotic Electromechanical Transducers

In this article, we investigate the issue of the accelerated adaptive fuzzy optimal control of three coupled fractional-order chaotic electromechanical transducers. A small network where every transducer has the nearest-neighbor coupling configuration is used to form the coupled fractional-order chaotic electromechanical transducers. The mathematical model of the coupled electromechanical transducers with nearest-neighbors is established and the dynamical analysis reveals that its behaviors are very sensitive to external excitation and fractional order. In the controller design, the recurrent nonsingleton type-2 sequential fuzzy neural network (RNT2SFNN) with the transformation is designed to estimate unknown functions of dynamics system in the feedforward fuzzy controller, and it is constructed to approximate the critic value and actor control functions by using policy iteration (PI) in the optimal feedback controller. Meanwhile, the speed functions are employed to achieve accelerated convergence within a pregiven finite time and a tracking differentiator is used to solve the explosion of terms associated with traditional backstepping. The whole control strategy consists of a feedforward controller integrating with the RNT2SFNN, tracking differentiator, and speed function in the framework of the backstepping control and a feedback controller fusing with the RNT2SFNN and PI under an actor/critic structure to solve the Hamilton–Jacobi–Bellman equation. The proposed scheme not only guarantees the boundness of all signals and realizes the chaos suppression, synchronization, and accelerated convergence, but also minimizes the cost function. Simulations demonstrate and validate the effectiveness of the proposed scheme.

Research on a forecasted load-and time delay-based model predictive control (MPC) district energy system model

District energy system is an underground network used to cool and heat buildings efficiently, which has the characteristics of large scale, multiple equipment, large lag, nonlinear and time-varying dynamics. The traditional feedback control strategies not only cause mismatch and hysteresis of energy supply and load demand in time series but also lead to high energy consumption. Therefore, a forecasted load-and time delay-based model predictive control district energy system model is proposed in this paper. Taking a district energy system in Tianjin University as a test case, the superiority of the proposed control model is ascertained. A TRNSYS and MATLAB co-simulation platform is developed as the test platform. The results show that the proposed control model can not only improve matching the extent of energy supply and load demand in time series, but can also reduce the energy peak and total energy consumption of the district energy system. Specifically, energy consumption is reduced by 9.56% and 5.30% in summer and 7.75% and 2.61% in winter compared with the control models based on the traditional feedback control and load feedforward fuzzy control strategies, respectively.

A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System

The energy consumption of air-conditioning systems is a major part of energy consumption in buildings. Optimal control strategies have been increasingly developed in building heating, ventilation, and air-conditioning (HVAC) systems. In this paper, a load forecast fuzzy (LFF) control strategy was proposed. The predictive load based on the SVM method was used as the input parameter of the fuzzy controller to perform feedforward fuzzy control on the HVAC system. This control method was considered as an effective way to reduce energy consumption while ensuring indoor comfort, which can solve the problem of hysteresis and inaccuracy in building HVAC systems by controlling the HVAC system in advance. The case study was conducted on a ground source heat pump system in Tianjin University to validate the proposed control strategy. In addition, the advantages of the LFF control strategy were verified by comparing with two feedback control strategies, which are the supply water temperature (SWT) control strategy and the room temperature fuzzy (RTF) control strategy. Results show that the proposed LFF control strategy is capable not only to ensure the minimum indoor temperature fluctuations but also decrease the total energy consumption.

Hydraulically actuated horizontal axis wind turbine pitch control by model free adaptive controller

In the present work a model free fuzzy feedforward PID controller has been developed for pitch control application of the horizontal axis wind turbine through electrohydraulic pitch actuation system. The controller has been designed to achieve maximum power tracking and protect from high wind gusts even with fault in the actuating system. Electrohydraulic pitch actuating system has high power to weigh ratio, self-lubricating property, good controllability etc. as its merit of application. Nonlinear mathematical model of small horizontal axis wind turbine along with proportional valve controlled electrohydraulic pitch actuating system has been developed. The BEM theory has been used to estimate aerodynamic load on the turbine blade and pitch for maximum power capture by the turbine with the variation of the wind speed. The present model has been validated with experimental results. The performance of the proposed fuzzy feedforward PID controller has been investigated with various test signals as well as predicted pitch demand from wind data for maximum power tracking and to save from high wind gusts. The performance index of proposed fuzzy feedforward PID controller has been found as 0.08606, 0.08849 and 0.09809 with normal leakage, high leakage and very high leakage respectively.

An Adaptive Fuzzy Feedforward-Feedback Control System Applied to a Saccharification Process

Abstract In industrial bioprocess control, disturbance sources typically influences process variable regulation. These disturbances may reduce a system control performance or even affect the final bioproduct quality. Therefore, feedforward control is desired because it anticipates the effects caused by these disturbances in an attempt to keep the process variable at the setpoint value. However, designing a feedforward control law requires process modeling, which can be a tough task when dealing with bioprocesses that are intrinsically nonlinear and multivariable systems. Thus, an adaptive feedforward control law or other advanced control system is needed for satisfactory disturbance rejection. For this reason, a general fuzzy feedforward control system is proposed in this paper to replace the classical feedforward control, making it easier to implement the feedforward control action by avoiding nonlinear and multivariable process modeling. The adaptive fuzzy feedforward-feedback (A4FB) system was applied to a product concentration control loop in an enzymatic reactor, to reject disturbances caused by variations in the substrate and enzymatic solutions feed concentration. The results showed that the A4FB controller rejected much more disturbance effects than classical feedforward control law, demonstrating its advantage, supported by not only its simple implementation, but also its improved disturbance rejection.

High-Order Sliding Mode Observer Based OER Control for PEM Fuel Cell Air-Feed System

This paper presents oxygen excess ratio (OER) control methods for proton exchange membrane fuel cell systems based on high-order sliding mode (HOSM) observer. The nonlinear and strong coupling characteristics make it difficult for the nonlinear uncertain systems to realize OER closed-loop feedback control. An improved HOSM observer is developed to observe the unmeasurable system states. Even under the interference of model parametric uncertainties, external disturbances, and measurement noises, the HOSM observer can still show fast convergence and good robustness. With the output error injection feature of improved HOSM algorithm, OER can be reconstructed in finite time. The proposed two stage sliding mode based on super twisting algorithm plus feedforward (TS_SM_STW+FF) controller and TS_SM_STW plus fuzzy feedforward (TS_SM_STW+Fuzzy_FF) controller are applied to regulate the estimated OER to track optimal value, respectively. The dynamic performance of OER with proposed methods is validated through the RT-LAB hardware-in-loop platform. The experimental results have shown the feasibility and effectiveness of proposed controllers as compared with conventional strategies.

Proportional fuzzy feed-forward architecture control validation by wind tunnel tests of a morphing wing

In aircraft wing design, engineers aim to provide the best possible aerodynamic performance under cruise flight conditions in terms of lift-to-drag ratio. Conventional control surfaces such as flaps, ailerons, variable wing sweep and spoilers are used to trim the aircraft for other flight conditions. The appearance of the morphing wing concept launched a new challenge in the area of overall wing and aircraft performance improvement during different flight segments by locally altering the flow over the aircraft’s wings. This paper describes the development and application of a control system for an actuation mechanism integrated in a new morphing wing structure. The controlled actuation system includes four similar miniature electromechanical actuators disposed in two parallel actuation lines. The experimental model of the morphing wing is based on a full-scale portion of an aircraft wing, which is equipped with an aileron. The upper surface of the wing is a flexible one, being closed to the wing tip; the flexible skin is made of light composite materials. The four actuators are controlled in unison to change the flexible upper surface to improve the flow quality on the upper surface by delaying or advancing the transition point from laminar to turbulent regime. The actuators transform the torque into vertical forces. Their bases are fixed on the wing ribs and their top link arms are attached to supporting plates fixed onto the flexible skin with screws. The actuators push or pull the flexible skin using the necessary torque until the desired vertical displacement of each actuator is achieved. The four vertical displacements of the actuators, correlated with the new shape of the wing, are provided by a database obtained through a preliminary aerodynamic optimization for specific flight conditions. The control system is designed to control the positions of the actuators in real time in order to obtain and to maintain the desired shape of the wing for a specified flight condition. The feasibility and effectiveness of the developed control system by use of a proportional fuzzy feed-forward methodology are demonstrated experimentally through bench and wind tunnel tests of the morphing wing model.

A Highly Accurate Model-Free Motion Control System with a Mamdani Fuzzy Feedback Controller combined with a TSK Fuzzy Feed-forward Controller

In this paper, a new intelligent robot motion control architecture --- a highly accurate model-free fuzzy motion control- is proposed in order to achieve improved robot motion accuracy and dynamic performance. Its architecture combines a Mamdani fuzzy proportional (P) and a conventional integral (I) plus derivative (D) controller for the feedback part of the system, and a Takagi-Sugeno-Kang fuzzy controller for the feed-forward, nonlinear part. The fuzzy P + ID controller improves the performance of the nonlinear system, and the TSK fuzzy controller uses a TSK fuzzy inference system based on extended subtractive- clustering method which integrates information on joint angular displacement, velocity and acceleration for torque identification. The advantage of this kind of model-free control is that it uses the information directly from the input/output of the nonlinear system, without any complex robot model computation, in order to decrease the control system's sensitivity to any dynamical uncertainty. Furthermore, parametric search for clustering parameters in extended subtractive clustering secures the high accuracy of the system identification. Consequently, this proposed model-free fuzzy motion control benefits from the advantages of two kinds of fuzzy system. It not only incorporates flexible design, good performance and simple conception but also ensures precise motion control and great robustness. Comparisons with other intelligent models and results from numerical studies on a 4-bar planar parallel mechanism show the effectiveness and competitiveness of the proposed control.

Research on Compound Pitch Control Technology of Direct-drive Permanent Magnet Wind Turbine

The conventional PID pitch controller was usually used in direct-drive permanent magnet wind turbine in order to maintain the output power unchanged above the rated wind speed. However, due to the complex structure of wind turbine as well as high nonlinearity of wind speed, the effect of PID control method is often less than ideal. A new pitch control method combined with fuzzy adaptive PID and fuzzy feed forward controller is proposed in this paper. The fuzzy adaptive PID controller is able to ensure the unit having a better control result than PID controller at various wind speeds. The fuzzy feed forward controller has improved the responsiveness of pitch control system. With the simulation of a 1.5MW wind turbine, the responsiveness and stability of wind turbine can be effectively proved by the new pitch control method above the rated wind speed, the system’s overshoot is also reduced, which satisfies the demand of power control.

Fault Self-repairing Flight Control of a Small Helicopter via Fuzzy Feedforward and Quantum Control Techniques

In this paper, the longitudinal-lateral attitude control and fault self-repairing of a small helicopter is investigated using fuzzy feedforward and quantum control techniques. The Lagrange-Euler equation is used to derive a mathematical model of the helicopter flight dynamics. To handle the complex faults of the helicopter flight system, a model reference-based self-repairing control law is proposed using quantum control techniques, which can improve the helicopter’s self-repairing and control precision. In addition, a fuzzy feedforward compensation controller is designed to improve the anti-disturbance performance. Finally, simulation results are given to illustrate the effectiveness of the developed intelligent self-repairing controller.

The Coordinated Control of Circulating Fluidized Bed Boiler with Intelligence Feedforward Control

AbstractAn coordinated control strategy has been proposed and successfully applied to 300MW circulating fluidized bed (CFB) units in China. The paper describes the new coordinated control (NCC) system to overcome the long settling time, strong coupling, nonlinearity and inconstancy encountered in CFB combustion. NCC is based on both fuzzy Feedforward (fuzzy-FF) control and the fuzzy-PID feedback control. The fuzzy-FF control path contains a set of multi-input single-output fuzzy inference systems obtained from steady-state input—output plant data and is used to improve the dynamics of CFB unit. The control output is mainly determined by the fuzzy-FF path, diminishing the control effort on the PID controllers. The fuzzy-PID controller supplies the complementary control signal component for regulation and disturbance rejection in small neighborhoods of the commanded trajectories. A self-adapting algorithm and non-uniform grid scheduling is proposed in the fuzzy-PID controller. The strategy is implemented through the function code on many typical DCS (EDPF-NT, XDPS, etc). The industrial application results show that this strategy achieves better performance in the specific range of load variations.

An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot

Pneumatic muscle actuators (PMA) show great potential in wearable and compliant rehabilitation devices as they are flexible and lightweight. However, the varying and non-linear behavior of the actuators imposes modeling and control challenges, which are difficult to comprehend. This research proposes a new wearable ankle rehabilitation robot, first of its kind in the world driven by PMAs in a parallel form. The focus of this presented work is to develop an iterative controller to overcome the challenges for PMA driven devices. A fuzzy feedforward controller is proposed to accurately predict the behavior of PMA. A modified Genetic Algorithm (GA) is developed to identify the optimal set of parameters for the fuzzy controller. The iterative controller has been tested on the proposed PMA driven ankle rehabilitation robot, and is found capable of mapping the complex relationship in length, force and pressure of the PMA with high accuracy. Experimental results show excellent trajectory tracking performance of the controller when given various desired trajectories.

Reentry attitude tracking control based on fuzzy feedforward for reusable launch vehicle

This paper presents a novel fuzzy tracking control method, which is used for the reentry attitude tracking problem of a reusable launch vehicle. Based on Takagi-Sugeno fuzzy description of reentry attitude dynamics, the tracking error of attitude angles is studied. Without the need of an augmented system, exponential stability of the tracking error can be obtained by a fuzzy feedforward controller. In addition, to achieve a good tracking performance, H∞ and decay rate ν control methods are considered. The solutions to the robust fuzzy tracking control problem can be obtained by applying linear matrix inequalities and fuzzy logic controller tools. The simulation results demonstrate the effectiveness of the proposed method.

Fuzzy Modeling and Fuzzy Control [Book Review

This book presents a systematic framework targeting at fuzzy modeling and fuzzy control of nonlinear systems with uncertainties. The book is organized into three major parts incorporating 13 chapters. The first part contains four chapters focusing on the modeling of nonlinear dynamical systems by using fuzzy logic. The second part includes five chapters in which fuzzy inference and control techniques are addressed. The final part consists of four chapters, which covers several advanced topics in fuzzy control ranging from controller and filter design to chaotification of fuzzy systems and feedforward fuzzy control of nonlinear systems via Fourier integrals. One of the major contributions of this book is that it presents the latest original research developments in the field. It provides rich examples and references, too. It is a valuable resource for those researchers and practitioners interested in expanding their knowledge from fuzzy logic and applications to nonlinear dynamical systems.

Mining the fuzzy control rules of aeration in a Submerged Biofilm Wastewater Treatment Process

This paper presents a special rule base extraction analysis for optimal design of an integrated neural-fuzzy process controller using an “impact assessment approach.” It sheds light on how to avoid some unreasonable fuzzy control rules by screening inappropriate fuzzy operators and reducing over fitting issues simultaneously when tuning parameter values for these prescribed fuzzy control rules. To mitigate the design efforts, the self-learning ability embedded in the neural networks model was emphasized for improving the rule extraction performance. An aeration unit in an Aerated Submerged Biofilm Wastewater Treatment Process (ASBWTP) was picked up to support the derivation of a solid fuzzy control rule base. Four different fuzzy operators were compared against one other in terms of their actual performance of automated knowledge acquisition in the system based on a partial or full rule base prescribed. Research findings suggest that using bounded difference fuzzy operator ( O b) in connection with back propagation neural networks (BPN) algorithm would be the best choice to build up this feedforward fuzzy controller design.