Takagi-Sugeno Fuzzy Research Articles

Pantograph Catenary Contact Force Regulation Based on Modified Takagi-Sugeno Fuzzy Models

In this paper, a new contact force control technique, based on the modified Takagi-Sugeno model and the parallel distributed compensation concept is developed to suppress vibrations between the pantograph and the catenary by regulating the contact force to a reference value, thereby achieving stable current collection. The proposed method uses simple and standard PID and modified Takagi-Sugeno fuzzy controllers. The two controllers guarantee the designed system's robust stability. Furthermore, based on a simplified pantograph–catenary system model, the comparative simulation results show that variations of the contact force can be almost attenuated. As a result, the system stability is guaranteed, and the performance robustness is verified.

Takagi–Sugeno Fuzzy Model-Based Control for Semi-Active Cab Suspension Equipped with an Electromagnetic Damper and an Air Spring

Variable damping shock absorbers have received extensive attention for their efficient vibration reduction performance, and air springs have also been widely used in high-end commercial vehicles due to their nonlinear stiffness characteristics. This paper presents a novel semi-active cab suspension integrated with an air spring and a variable damping electromagnetic damper (A-EMD). The electromagnetic damper (EMD) prototype was designed, manufactured and tested. Then, due to the interference of nonlinear stiffness characteristics of the air spring with the controller in the subsequent design, the Takagi–Sugeno fuzzy method was adopted to segmentally linearize its nonlinearity, based on which an H∞ state feedback semi-active controller was designed to control the EMD to generate variable damping force. Furthermore, a Luenberger state observer was designed to provide immeasurable state parameters for the controller. Numerical simulations were carried out to validate the effectiveness of the proposed approaches, and the results show that the proposed control strategy can significantly improve the ride comfort of the A-EMD system. The vibration dose value (VDV) acceleration under the bump road and the frequency-weighted acceleration root mean square (FWA-RMS) under the random road decreased by 36.05% and 19.77%, respectively, compared with the passive suspension system.

Equivalent-input-disturbance-based preview repetitive control for Takagi–Sugeno fuzzy systems

This paper investigates the preview repetitive control (PRC) problem for a class of Takagi–Sugeno (T-S) fuzzy systems with parametric uncertainties and external disturbances. First, a nonlinear plant is represented by a T-S fuzzy model and a fuzzy equivalent-input-disturbance (EID)-based PRC law is used to improve the tracking performance and disturbance attenuation. Next, by using a new equality constraint, a continuous-discrete two-dimensional (2D) system is established and the design problem of the EID-based PRC law is transformed into the stability problem of such a 2D system. Then, a sufficient stability condition is derived in terms of linear matrix inequality (LMI). Moreover, the LMI-based parametric method for the controller is provided. Different from the existing LQR-based PRC design method, the LMI-based one can be easily achieved by the powerful Matlab’s LMI Toolbox. Finally, two numerical examples are provided to illustrate the effectiveness and superiority of the developed controller design technique.

Further Results on Dissipativity Analysis for T–S Fuzzy Systems Based on Sampled-Data Control

This article investigates the dissipative stability and stabilization problems of Takagi–Sugeno fuzzy systems by employing sampled-data control. First, in order to enhance the adaptability of the controller, the internal and external influence factors, such as communication time delay and aperiodic sampling pattern, are taken into consideration in the design process. Then, some new terms are proposed to construct the looped-functional-like Lyapunov functional, which can improve the dissipative performance, enlarge the admissible upper bound of the aperiodic sampling periods, and reduce the occupation of control signals in the communication channel. Next, based on the free-matrix-based integral inequalities, some novel dissipative criteria are presented in terms of linear matrix inequalities. Finally, the superiority of the provided criteria is demonstrated through a truck–trailer system.

Special issue on “Optimal design and operation of energy systems”

Special issue on “Optimal design and operation of energy systems”

Estimation of Energy Consumption and Flight Time Margin for a UAV Mission Based on Fuzzy Systems

An essential aspect to achieving safety with a UAV is that it operates within the limits of its capabilities, the available flight time being a key aspect when planning and executing a mission. The flight time will depend on the relationship between the available energy and the energy required by the UAV to complete the mission. This paper addresses the problem of estimating the energy required to perform a mission, for which a fuzzy Takagi–Sugeno system was implemented, whose premises were developed using fuzzy C-means to estimate the power required in the different stages of the mission. The parameters used in the fuzzy C-means algorithm were optimized using particle swarm optimization. On the other hand, an equivalent circuit model of a battery was used, for which fuzzy modeling was employed to determine the relationship between the open-circuit voltage and the state of charge of the battery, which in conjunction with an extended Kalman filter allows determining the battery charge. In addition, we developed a methodology to determine the minimum allowable battery charge level. From this, it is possible to determine the available flight time at the end of a mission defined as the flight time margin. In order to evaluate the developed methodology, a physical experiment was performed using an hexarotor UAV obtaining a maximum prediction error equivalent to the energy required to operate for 7 s, which corresponds to 2% of the total mission time.

An Optimal Homogenous Stability-Based Disturbance Observer and Sliding Mode Control for Secure Communication System

To prevent the loss of data or distortion of the real sent data in the secure communication, the disturbance and uncertainty rejection is very important technique. Especially, the secure communication of the chaotic system, where the master and slave systems are used to encrypt and decrypt the signal. These systems are should be a chaotic. The chaotic system is unpredictable and unstable. The security is obtained if the master and slave systems are synchronizing. Therefore, this paper proposes a new disturbance observer (DO) for estimating attacked signals on public channels and uncertain values of the secure communication system (SCS). First, a new condition of the reaching law was proposed via a homogenous stability condition, which was used to construct the disturbance observer and synchronization controller. Second, the proposed stability is applied to design a new DO for estimating the attacked signals on the public channels and the variations of the parameters of all master and slave systems (MSSs). Third, the MSSs were synchronized by the sliding mode control (SMC) with the same reaching law with the proposed DO. The calculations of controller and observer designs were based on the Takagi-Sugeno fuzzy (TSF) system, where the MSSs were taken into account as the fuzzy system. However, the TSF was used for calculation only. The original chaotic of MSSs were used to keep the characteristics of the chaos phenomenon. Final, the mathematically proven was provided to show the corrections of the proposed methods. To show the superior powers of the proposed method, the simulation of MATLAB software and experiment of local network via the internet router were used to conduct the logical analysis. The tested disturbances and uncertainties were mostly compensated by the proposed DO.

Direct quaternion method-based stability criteria for quaternion-valued Takagi-Sugeno fuzzy BAM delayed neural networks using quaternion-valued Wirtinger-based integral inequality

<abstract><p>This paper investigates the global asymptotic stability problem for a class of quaternion-valued Takagi-Sugeno fuzzy BAM neural networks with time-varying delays. By applying Takagi-Sugeno fuzzy models, we first consider a general form of quaternion-valued Takagi-Sugeno fuzzy BAM neural networks with time-varying delays. Then, we apply the Cauchy-Schwarz algorithm and homeomorphism principle to obtain sufficient conditions for the existence and uniqueness of the equilibrium point. By utilizing suitable Lyapunov-Krasovskii functionals and newly developed quaternion-valued Wirtinger-based integral inequality, some sufficient criteria are obtained to guarantee the global asymptotic stability of the considered networks. Further, the results of this paper are presented in the form of quaternion-valued linear matrix inequalities, which can be solved using the MATLAB YALMIP toolbox. Two numerical examples are presented with their simulations to demonstrate the validity of the theoretical analysis.</p></abstract>

Vehicle Actuator Fault Detection With Finite-Frequency Specifications via Takagi-Sugeno Fuzzy Observers: Theory and Experiments

This article presents a new nonlinear observer-based method to detect the faults of both steering and torque actuators of autonomous ground vehicles. To this end, the nonlinear vehicle system is reformulated in a Takagi-Sugeno (TS) fuzzy model with both measured and unmeasured nonlinear consequents, called N-TS fuzzy model. Differently from the classical TS fuzzy technique with linear consequents, this N-TS fuzzy reformulation enables an effective use of differential mean value theorem to deal with unmeasured nonlinearities, which is known as a major challenge in TS fuzzy observer design. Moreover, the N-TS fuzzy form also allows reducing not only the design conservatism but also the numerical complexity of the design conditions as well as the observer structure, which is crucial for real-time vehicle application. To minimize the disturbance effect with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathscr {H}_\infty$</tex-math></inline-formula> performance and maximize the fault sensibility with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathscr {H}_-$</tex-math></inline-formula> performance, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information on the disturbance/fault frequency ranges is taken into account in the N-TS fuzzy observer design via the generalized Kalman–Yakubovich–Popov (KYP) lemma. Based on Lyapunov stability theory, the design of the proposed multiobjective <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathscr {H}_-/\mathscr {H}_\infty$</tex-math></inline-formula> N-TS fuzzy fault detector is recast as an optimization problem with strict linear matrix inequality (LMI) constraints, which can be effectively solved with numerical solvers. Both numerical and experiments are performed under realistic driving conditions to demonstrate the theoretical and practical interests of the new finite-frequency fuzzy fault detection method.

Design and Development of Coffee Machine Control System Using Fuzzy Logic

The food and beverage industry is currently rife in urban and outside cities. Many locations are used as places to sell drinks, especially coffee which is a native plant of Indonesia. Nowadays, coffee compounding requires good technology. There are many coffee processing machines on the market. The coffee machine is capable of making expresso coffee, latte coffee and others. This coffee machine also combines coffee ingredients, sugar and milk as a carrier for a delicious aroma. In addition, the water pressure from the coffee machine heating boiler, the strong pressure of the coffee machine piston also affects the results of making a cup of coffee, the stronger the pressure, the thicker the coffee produced and the slower the flow of water in the coffee machine. glass of water because basically the stronger the pressure applied to it. the coffee grounds, the tighter and tighter the gaps that the water itself will pass, as well as the thickness of the resulting coffee water will be more concentrated. With Fuzzy Inference, it is possible to determine the optimal pressure to be exerted by the coffee machine piston based on the weight of the coffee grounds (grams) on the coffee machine piston and the specifications of the type of coffee machine used. Determining the optimal pressure on the coffee grounds will affect the taste of the coffee water produced and the speed of making a cup of coffee. This study uses the optimal pressure on the piston using the fuzzy inference method. The purpose of this research is to create a simulation for evaluating the performance of a coffee machine using fuzzy logic to solve the problem of damage to the piston. The fuzzy approach in this research uses the fuzzy Takagi Sugeno Kang method.

Further improvements on Robust Stability Analysis for Takagi-Sugeno Fuzzy Systems with Time-Varying Delay

Further improvements on Robust Stability Analysis for Takagi-Sugeno Fuzzy Systems with Time-Varying Delay

Bipartite Consensus for Takagi-Sugeno Fuzzy Uncertain Multi-Agent Systems With Gain Fluctuations

This paper examines the problem of bipartite consensus for Takagi-Sugeno fuzzy multi-agent systems subject to uncertainties. The principal intention of this work is to develop a non-fragile controller through which the considered multi-agent system can achieve bipartite consensus. An undirected signed graph is considered to describe the cooperative and competitive interaction among neighboring agents. On this circumstance, we propose a consensus protocol by the utilization of graph theory. Further, with the aid of Lyapunov stability theory, the sufficient conditions are derived in the form of linear matrix inequalities which ensures the bipartite consensus of the considered multi-agent system. At last, two numerical examples are provided with their simulations to describe the viability of the developed theoretical findings.

New Control Design via Switching Approach with Higher Order Derivatives of Lyapunov Function for Takagi-Sugeno Fuzzy Systems

This paper proposes a new control design method via two techniques to decrease the conservativeness in control design conditions for Takagi-Sugeno fuzzy systems that can represent a wide class of nonlinear systems. The first technique we employ is to use higher order derivatives of Lyapunov function that is shown to reduce the conservativeness in the literature. The second one is a switching approach that takes advantages of a property of the membership function. In this paper, we provide control design methods and their less conservative conditions based on the above-mentioned techniques. The proposed control design conditions are independent of the membership function and are written in a linear matrix inequality form. Finally, Numerical examples illustrate the effectiveness of the proposed control methods by comparing with the existing methods.

Constrained Control for a Class of TS Fuzzy Systems

This article investigates handling control constraints in the controller design phase for a class of Takagi–Sugeno (TS) fuzzy systems. Line integral Lyapunov function (LILF) is used to reduce the conservativeness compared to common quadratic Lyapunov function (CQLF) and fuzzy Lyapunov function (FLF). Two cases, namely unsaturating and saturating control, are studied. First, an inclusion condition is set to guarantee the behavior in a region where control constraints violation does not occur. For the second case, the saturation may take effect and is allowed. Thanks to the saturation function, convex writing to outrun the nonlinearity inconvenience. For both cases, stabilizing design procedures are proposed such that closed-loop stability and its linked performances are not affected. Furthermore, the proposed conditions enlarge the domain of stability as possible. Hence, comparisons are made with other approaches to show the effectiveness of our approach.

Broad learning system with Takagi–Sugeno fuzzy subsystem for tobacco origin identification based on near infrared spectroscopy

Tobacco origin identification is significantly important in tobacco industry. Modeling analysis for sensor data with near infrared spectroscopy has become a popular method for rapid detection of internal features. However, for sensor data analysis using traditional artificial neural network or deep network models, the training process is extremely time-consuming. In this paper, a novel broad learning system with Takagi–Sugeno (TS) fuzzy subsystem is proposed for rapid identification of tobacco origin. Incremental learning is employed in the proposed method, which obtains the weight matrix of the network after a very small amount of computation, resulting in much shorter training time for the model, with only about 3 s for the extra step training. The experimental results show that the TS fuzzy subsystem can extract features from the near infrared data and effectively improve the recognition performance. The proposed method can achieve the highest prediction accuracy (95.59%) in comparison to the traditional classification algorithms, artificial neural network, and deep convolutional neural network, and has a great advantage in the training time with only about 128 s.

Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization

To achieve the maximum power from wind in variable-speed regions of wind turbines (WTs), a suitable control signal should be applied to the pitch angle of the blades. However, the available uncertainty in the modeling of WTs complicates calculations of these signals. To cope with this problem, an optimal controller is suitable, such as particle swarm optimization (PSO). To improve the performance of the controller, fractional order PSO (FPSO) is proposed and implemented. In order to construct this approach for a two-mass WT, we propose a new state feedback, which was first applied to the turbine. The idea behind this state feedback was based on the Taylor series. Then, a linear model with uncertainty was obtained with a new input control signal. Thereafter, the conventional PSO (CPSO) and FPSO were used as optimal controllers for the resulting linear model. Finally, a comparison was performed between CPSO and FPSO and the fuzzy Takagi–Sugeno–Kang (TSK) inference system. The provided comparison demonstrates the advantages of the Taylor series with combination to these controllers. Notably, without the state feedback, CPSO, FPSO, and TSK fuzzy systems cannot stabilize WTs in tracking the desired trajectory.

Low cost microcontroller implementation of Takagi–Sugeno Fuzzy MPPT controller for photovoltaic systems

Maximum power point trackers have a significant role in optimizing the energy conversion efficiency in a photovoltaic system. The numeric achievements of MPPT algorithm can be implemented and tested by using several embedded boards as Digital Signal Processor, Field-Programmable Gate Array, Arduino, and dspace. Alternatively, for the low cost, availability and simplicity, the PIC microcontrollers can be used compared with the other hardware devices. Therefore, this paper presents the implementation of a Takagi–Sugeno fuzzy controller on a low cost PIC microcontroller, for tracking the maximum power point of a PV module. The PV system consists of a PV emulator, DC-DC converter, and resistive load. The different steps in design, simulation and realization of the T-S Fuzzy logic controller are discussed. The proposed controller system was evaluated by comparing its performance metrics, in terms of efficiency and the Integral Square Error, with existing technique in the literature. The results corresponding to the experimental validation show that the proposed MPPT controller is able to ensure a perfect tracking of the maximum power point with variation of irradiance and is performing better than reported in a previous work.

Fault detection for Takagi–Sugeno fuzzy systems using multiple observers and ellipsoidal analysis

AbstractThis paper proposes a novel fault detection method for Takagi–Sugeno fuzzy systems via multiple observer design and ellipsoidal analysis. By considering only one fault distribution matrix at a time, the original system is divided into a limited number of fault modes. For each fault mode, an observer with finite‐frequency fault sensitivity performance and full‐frequency unknown inputs robustness performance is designed to generate the residual signal. Based on the designed observer, the ellipsoidal analysis is used to evaluate a residual and to give a fault detection index for each mode. In addition, the fault detection result is provided by combining “or” logical operation of the multiple indices. Numerical simulations and an AC servo motor experimental platform are used to verify the viability and validity of the proposed method.

Compensation-Based Output Feedback Control for Fuzzy Markov Jump Systems With Random Packet Losses.

This article is concerned with the problem of compensation-based output feedback control for Takagi-Sugeno fuzzy Markov jump systems subject to packet losses. The phenomenon of packet losses is assumed to randomly occur in the feedback channel, which is modeled by a Bernoulli process. Employing the single exponential smoothing method as a compensation scheme, the missing measurements are predicted to help offset the impact of packet losses on system performance. Then, an asynchronous output feedback controller is designed by the hidden Markov model. Based on the mode-dependent Lyapunov function, some novel sufficient conditions on the controller existence are derived such that the closed-loop system is stochastically stable with strict dissipativity. Besides, an algorithm for determining the optimal smoothing parameter is proposed. Finally, the validity and advantages of the design approach are manifested by some simulation results.

Relaxed Observer Design of Discrete-Time Takagi–Sugeno Fuzzy Systems Based on a Lightweight Gain-Scheduling Law

This study is concerned with proposing much more effective Luenberger-like fuzzy observers than relevant multiinstant observers reported in recent literature, in other words, less conservative results can be given while less computing resource is consumed as an important premise. For the first time, the variation bound of two adjacent time-variant Lyapunov matrices encountered in the synthesis of fuzzy observer is characterized by a contraction factor, and thus the key replacement of the lagged term is developed in order to construct an effective synchronization framework of fuzzy observer synthesis. Since there only exist the current-time normalized fuzzy weighting functions in the obtained synchronization framework, the conservatism caused by the mismatch of different sampling instants can be avoided. Based on this, a lightweight gain-scheduling law is designed in which a large number of redundancy gains in recent multiinstant observers can be condensed in order to save computing resource to the greatest extent. Finally, two benchmark examples are borrowed for validating the superiority of our proposed results.