Adaptive Type-2 Fuzzy Controller Research Articles

A fractional adaptive type-2 fuzzy structural control system: Theoretical/experimental study

A new modified sliding mode control (SMC) method based on type-2 fuzzy neural networks (T2FNNs) is introduced for the active mass damper (AMD) systems. An adaptive T2FNN is used in the switching part, and another adaptive T2FNN is used to estimate the uncertainty of the AMD system. T2FNNs are adopted to predict the system’s uncertainties and establish a dynamic model of the AMD system, independent of the mathematical model. The chattering phenomenon is also taken into account and analyzed. The stability is studied by using the Lyapunov approach to derive training rules for both T2FNNs in dynamic modeling and switching part. Numerical simulation and experimental verifications confirm the feasibility and effectiveness of the designed T2FNN based SMC. The results reveal that the designed controller outperforms the conventional controllers in reducing the vibration peak and reducing the root mean square (RMS) value of structural displacement and acceleration. It also exhibits good robustness against external disturbances and structural dynamic perturbations. The suggested algorithm combines the advantages of adaptive control and fuzzy logic, addressing the issue of chattering in control and overcoming the lack of self-learning capability in conventional SMCs.

ADAPTIVE FUZZY BACKSTEPPING CONTROL FOR BLOOD GLUCOSE REGULATION

In this paper, we propose an adaptive type-2 fuzzy backstepping controller for blood glucose regulation. To mimic at best the behavior of a real pancras, the nonlinear Bergman minimal model is adopted. To deal with the strict feedback form of this model, we use backstepping algorithm. To overcome the knowledge of the several parameters of model to deduce the control laws, we use adaptive type-2 fuzzy systems to obtain the control signals. The stability of the closed loop system is proven using Lypunov theory. A simulation example with a comparative study is given to schow the performances of the proposed approach.

ADAPTIVE FUZZY STATE OBSERVER BASED ROBUST CONTROLLER FOR A ROBOTIC SYSTEM

In this paper we propose to develop an adaptive type-2 fuzzy robust controller with a stat observer to control a robotic system. For this, we the approximate the system with two type-2 fuzzy systems. To overcome the problem of the state measures, we propose a state observer allowing to converge quickly to the real values and hence guarantees the stability of the closed loop system. The robustness of the closed loop system is ensured by using a modified sliding mode control, where the chattering phenomenon are removed. Simulation results are given to show the efficiency of the proposed approach.

Investigation of chaotic behavior and adaptive type-2 fuzzy controller approach for Permanent Magnet Synchronous Generator (PMSG) wind turbine system

<abstract> <p>This article begins with a dynamical analysis of the Permanent Magnet Synchronous Generator (PMSG) in a wind turbine system with quadratic nonlinearities. The dynamical behaviors of the PMSG are analyzed and examined using Poincare map, bifurcation model, and Lyapunov spectrum. Finally, an adaptive type-2 fuzzy controller is designed for different flow configurations of the PMSG. An analysis of the performance for the proposed approach is evaluated for effectiveness by simulating the PMSG. In addition, the proposed controller uses advantages of adaptive type-2 fuzzy controller in handling dynamic uncertainties to approximate unknown non-linear actions.</p> </abstract>

Design and experimental research of observer-based adaptive type-2 fuzzy steering control for automated vehicles with prescribed performance

Steer-by-Wire (SbW) system is a significant electromechanical subsystem of automated vehicles. This paper proposes an observer-based type-2 fuzzy control method for the SbW system with uncertain nonlinearity, unknown modeling parameters, and unavailable state. First, an interval type-2 fuzzy logic system (IT2 FLS) and an IT2 FLS-based state observer are constructed to estimate the uncertain nonlinearity and unavailable state of SbW systems. Then, a prescribed performance control (PPC) method is proposed to achieve the prescribed tracking performance of SbW systems. Much importantly, a modified performance function is incorporated in this control method, such that the prescribed tracking performance can be guaranteed within a finite time regardless of the initial state. Finally, simulation and vehicle experiments are given to verify the effectiveness and superiority of the proposed methods.

Fixed-time adaptive general type-2 fuzzy logic control for air-breathing hypersonic vehicle

This paper proposes a novel fixed-time adaptive general type-2 fuzzy logical control (FAGT2FLC) scheme for an air-breathing hypersonic vehicle (AHV) with uncertainties. Firstly, the AHV dynamic model is transformed into a strict feedback form. Then, the FAGT2FLC is designed based on the transformed model to improve robustness and guarantee fixed-time convergence of the closed-loop system. The general type-2 fuzzy logic system (GT2FLS) is utilized to approximate the model uncertainties; for the purpose of designing adaptive laws, the [Formula: see text]-plane method is employed to represent the GT2FLS. A parameter projection operator is used to solve the possible singularity problem of parameter adaption. Besides, a fixed-time differentiator is used to deal with the “explosion of terms” inherent in backstepping method. Theoretical analysis based on relevant lemmas shows that the closed-loop system will converge into a small error band in fixed time. Lastly, detailed simulations are carried out to demonstrate the effectiveness and superiority of the proposed control scheme.

Adaptive Type-2 Fuzzy Neural Network Inherited Terminal Sliding Mode Control for Power Quality Improvement

This article proposes an adaptive type-2 fuzzy neural network control system to enhance the performance of power quality improvement. First, the dynamic model of APF with lumped uncertainties caused by parameter perturbation of ac inductor and dc capacitor is briefly introduced. Then, an integral-type terminal sliding mode control (TSMC) is developed for the finite-time reference signal tracking. Meanwhile, in terms of the considered chattering problem, saturation function is utilized in the proposed TSMC. Moreover, an adaptive type-2 fuzzy neural network (T2RFSFNN) is derived to achieve the model-free design, by applying the recurrent feature selection algorithm in the type-2 fuzzy neural network. To enhance the capacity to represent the uncertainties, adaptive learning mechanisms for updating the parameters of T2RFSFNN are derived by the Lyapunov theorem. Furthermore, a robust compensator with an adaptive uncertainty estimation law is investigated to relax the requirement for lumped uncertainties. Finally, the control performance using the developed T2RFSFNN is evaluated by some comparative experimental results.

Trajectory tracking of a quadrotor using a robust adaptive type-2 fuzzy neural controller optimized by cuckoo algorithm

This paper proposes an adaptive and robust adaptive control strategy based on type-2 fuzzy neural network (T2FNN) for tracking the desired trajectories of a quadrotor. The designed methods can control both the position and the orientation of a quadrotor. Contrary to common sliding mode controllers (SMCs), the robust adaptive trajectory tracking scheme presented here is based on SMC with exponential reaching law; which helps reduce the phenomenon of chattering. Moreover, parameters including the gains of sliding surfaces, are optimized by cuckoo optimization algorithm (COA), and the results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO). The designed methods in this study are called adaptive T2FNN controller and the exponential SMC (ESMC)-T2FNN. The law for updating the T2FNN is obtained online by using the Lyapunov stability theory. Considering undesired factors such as uncertainties, external disturbances and control signal saturation, the results of our controllers are compared with those of the adaptive type-1 fuzzy neural network controller (T1FNN) and ESMC-T1FNN. The extensive simulations demonstrate the effectiveness of the proposed COA-based ESMC-AT2FNN approach compared to the other tested techniques (i.e. GA, PSO and ACO) in terms of the improved transient and steady-state trajectory-tracking performance. The mean and standard deviation values concerning the COA are obtained through statistical analyses at 0.00006173 and 0.000092, respectively. This paper also examines the complexity of COA in optimizing the trajectory tracking control of quadrotor and investigates the effects of COA parameters on optimization results. The stable performance of the cuckoo algorithm is demonstrated by varying its parameters and analyzing the obtained results. These results also show the convergence of COA for the considered problem.

Observer-Based Composite Adaptive Type-2 Fuzzy Control for PEMFC Air Supply Systems

Polymer electrolyte membrane fuel cell (PEMFC) air supply systems are usually affected negatively by model uncertainties, external disturbance, and unmeasured variables. In this article, we propose a composite adaptive type-2 fuzzy controller based on a high-gain observer and a disturbance observer for oxygen excess ratio (OER) of PEMFC air supply systems. First, the derivatives of system output, which are unavailable due to limited sensors, are estimated via the high-gain observer. Then, interval type-2 fuzzy logic systems (IT2 FLSs) are adopted to approximate the unknown system dynamics and the disturbance observer is designed to estimate compound disturbance including unknown external disturbance and fuzzy approximation error. Finally, in order to improve the tracking performance, two composite adaptive updating laws are constructed by utilizing the estimated tracking error and the modeling error. Theoretical analysis shows that the system tracking error is uniformly ultimately bounded by Lyapunov stability theory. Numerical simulations and hardware-in-loop experiments are presented to demonstrate the effectiveness and superiority of the proposed controller.

A Novel Technique for Load Frequency Control of Multi-Area Power Systems

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability.

Sliding-Mode-Control-Theory-Based Adaptive General Type-2 Fuzzy Neural Network Control for Power-line Inspection Robots

In this paper, adaptive general type-2 fuzzy neural network control for motion balance adjusting of a power-line inspection robot is developed. It is used to enhance the anti-interference ability of the controlled plant. General type-2 fuzzy system is adopted because of its ability to more effectively handle uncertainties which may exist as external disturbances and parameter perturbations. The structure of general type-2 fuzzy system is designed by mimicking the neural network. The adaptive laws can be obtained based on the sliding mode control theory. This provides a kind of dynamic general type-2 fuzzy system whose membership functions and consequent parts are changing adaptively. The proposed controller is used to control an under-actuated non-linear power-line inspection (PLI) robot. Different simulation conditions are considered to test the anti-interference ability of the proposed controller. Simulation results indicate that the proposed method can strengthen the PLI robot’s anti-interference ability in a better way as compared to its interval type-2 fuzzy counterpart and PD controller.

Adaptive Type-2 Fuzzy Control for Regulation of Glucose Level in Type 1 Diabetes

In this paper, an adaptive fuzzy controller is presented to regulate the glucose level in type-1 diabetic patients. The dynamics of the glucose-insulin are estimated by the type-2 fuzzy systems. The parameters of the type-2 fuzzy systems are adjusted based on the adaptation laws that are obtained from the Lyapunov stability analysis. In order to evaluate the proposed controller’s performance, the modified Bergman model is considered for a number of diabetic patients under various conditions. Also, the parameters of the modified Bergman model are considered as time-varying and it is shown that the proposed control scheme results in good performance. Finally, the performance of the proposed method is compared with some other techniques.

Observer-based fractional-order adaptive type-2 fuzzy backstepping control of uncertain nonlinear MIMO systems with unknown dead-zone

A new problem of observer-based fractional adaptive type-2 fuzzy backstepping control for a class of fractional-order MIMO nonlinear dynamic systems with dead-zone input nonlinearity is considered in the presence of model uncertainties and external disturbances where the control scheme is constructed by combining the backstepping dynamic surface control (DSC) and fractional adaptive type-2 fuzzy technique. First, a linear state observer estimates immeasurable states. Second, the unknown nonlinear functions of the uncertain system are approximated with interval type-2 fuzzy logic systems. Third, to avoid the complication of backstepping design process, the DSC is used. Fourth, by using the fractional adaptive backstepping, fractional adaptive laws are constructed, the proposed method is applied to a class of uncertain fractional-order nonlinear MIMO system. In order to have a better control performance in reducing tracking error, the controller parameters are tuned by using the PSO algorithm. Stability of the system is proven by the Mittag-Leffler method. It is presented that the proposed design guarantees the boundedness property for the system and also the tracking error can converge to a small neighborhood of the zero. The simulation examples are given to show the efficiency of the proposed controller.

Adaptive type-2 fuzzy backstepping control of uncertain fractional-order nonlinear systems with unknown dead-zone

A new problem of adaptive type-2 fuzzy fractional control with pseudo-state observer for commensurate fractional order dynamic systems with dead-zone input nonlinearity is considered in presence of unmatched disturbances and model uncertainties; the control scheme is constructed by using the backstepping and adaptive technique. To avoid the complexity of backstepping design process, the dynamic surface control is used. Also, Interval type-2 Fuzzy logic systems (IT2FLS) are used to approximate the unknown nonlinear functions. By using the fractional adaptive backstepping, fractional control laws are constructed; this method is applied to a class of uncertain fractional-order nonlinear systems. In order to better control performance in reducing tracking error, the PSO algorithm is utilized for tuning the controller parameters. Stability of the system is proven by the Mittag–Leffler method. It is shown that the proposed controller guarantees the boundedness property for the system and also the tracking error can converge to a small neighborhood of the origin. The efficiency of the proposed method is illustrated with simulation examples.

Adaptive type-2 fuzzy traffic signal control with on-line optimization

Adaptive type-2 fuzzy traffic signal control with on-line optimization

Observer‐based robust adaptive T2 fuzzy tracking control for flexible air‐breathing hypersonic vehicles

This study presents an observer-based robust adaptive type-2 (T2) fuzzy tracking control scheme for the longitudinal dynamics of flexible air-breathing hypersonic vehicles. The control scheme is composed of three parts, namely an uncertain dynamics approximator, a reduced-order adaptive state observer, and command filter-based backstepping altitude and velocity controllers. First, the approximator is implemented by utilising interval T2 fuzzy logic systems for approximating unknown vehicle dynamics in the control scheme. Then, the reduced-order adaptive state observer is designed by the combination of the approximator and output state feedback techniques, which guarantee the estimation of unmeasurable states as well as uncertain dynamics identification. By using the linear matrix inequalities technique, a new condition for obtaining the gains of the state observer is presented. Next, on the basis of the designed approximator and state observer, the altitude and velocity tracking controllers are constructed by command-filtered backstepping control approach which avoids the explosion of complexity in conventional backstepping design. Thorough stability analysis of the closed-loop systems is conducted. Nominal and comparison simulation studies considering parametric uncertainty, incomplete state measurement, and measurement noises are carried out, illustrating the effectiveness of the proposed control scheme.

Adaptive type-2 fuzzy controller for nonlinear delay dominant MIMO systems: an experimental paradigm in LabVIEW

Higher order nonlinear systems with prevailing delay have been very challenging in significance with stability and process performance. This paper investigates the performance of a type-2 Mamdani intelligent controller implemented a delay dominant system which is modelled using black box approach identified to be a second order nonlinear model for a dual spherical tank liquid level system (DSTLLS) under LabVIEW environment. The adaptive approach of the intelligent Mamdani-based fuzzy controller proves itself to be very competent compared with the earlier experimented methods that already exist. The performance indices like integrated absolute error (IAE) and integrated squared error (ISE) are also calculated for different varying set point changes of DSTLLS. The response and error reduction efficiency of this method of adaptive type-2 intelligent fuzzy (ATIF) controller has been experimented for different flow configurations of this DSTLLS, like multiple input single output (MISO), multiple input multiple output (MIMO) and single input single output (SISO).

Adaptive type-2 fuzzy controller for nonlinear delay dominant MIMO systems: an experimental paradigm in LabVIEW

Adaptive type-2 fuzzy controller for nonlinear delay dominant MIMO systems: an experimental paradigm in LabVIEW

Decoupled Adaptive Neuro-Interval Type-2 Fuzzy Sliding Mode Control Applied in a 3DCrane System

Moving an object attached to a cable along a predetermined path is a very complex task when the angles of oscillation impose severe constraints. However, to minimize the swing angle, adaptive control laws are necessary, especially in case where the systems dynamics are prone to uncertainties. In this paper, we propose a decoupled adaptive neuro-interval type-2 fuzzy controller based on the sliding mode theory for the control of 3DCrane system. The considered 3DCrane system involves a plan movement in conjunction with a lifting movement. It has three control inputs only (trolley and hoisting forces) with five controlled variables (the trolley position in the XOY plane, the length of the lifting cable, and the two angles of swing). Overall, control subsystems are regarded as being decoupled interactions and that are taken as disturbances acting in the control of each individual subsystem. In the proposed approach, a conventional controller (PD) and the neuro-interval type-2 fuzzy controller are used in parallel; the PD controller ensures the asymptotic stability in compact space, the adaptation parameters of neuro-interval type-2 fuzzy inference system rules are obtained by derivation, and Lyapunov method is used to demonstrate the stability of the online learning algorithm. To validate the proposed approach, the laboratory equipment 3DCrane system is used as an experimental platform. The presented results are obtained for a trajectory tracking with a minimum of oscillations.

Indirect adaptive type-2 bionic fuzzy control

In this study, an indirect adaptive type-2 bionic fuzzy control method is proposed for a class of nonlinear systems. By regarding the niche of each species in an ecosystem as the antecedent, the fuzzy system with biological characteristics is constructed based on the niche “ecostate-ecorole” theory. In the actual system, we design a fuzzy control system using a type-2 bionic fuzzy system and provide both the adaptive law and constraint conditions of the system parameters. The stability of the closed-loop system is proved with all the state variables uniformly bounded in the Lyapunov sense. Additionally, the convergence of the bionic fuzzy control system is analyzed. Finally, the simulation results obtained for a permanent magnet direct current motor demonstrate the effectiveness and superiority of the designed method.