Adaptive Interval Type-2 Fuzzy Control Research Articles

Adaptive Interval Type-2 Fuzzy Controller for Variable-speed Wind Turbine

In this paper, an adaptive interval type-2 fuzzy controller is proposed for variable-speed and variable-pitch wind turbines. Because of attractive features of the well-known wind turbine baseline controller, the proposed controller acts as an augmented controller and works in parallel to the baseline controller. As typical variable-speed wind turbines have different controllers for different operation regions, for each operation region, a dedicated interval type-2 fuzzy controller is designed. Because of the uncertainty in wind speed measurement, modern control techniques try to estimate this value. However, in contrast to these modern control techniques, the proposed controller is independent of the wind speed estimation. Thus, there is a better saving in cost and computational burden. To evaluate the effectiveness of the proposed controller, simulations are conducted with wind profiles which span all operation regions. Results show that, compared with the baseline controller, the proposed controller enhances power generations and reduces mechanical loads concurrently.

Adaptive interval type-2 fuzzy controller for nonlinear networked Wiener systems subject to packet dropout and time-varying delay

In this paper, a novel adaptive interval type-2 fuzzy controller (AIT2FC) is proposed for a class of nonlinear networked Wiener systems under packet dropout and time varying delay. The proposed AIT2FC compensates the negative effects of the packet dropout and time varying delay in both forward and feedback loops. The structure of the proposed AIT2FC has three parts, an adaptive interval type-2 Takagi–Sugeno (IT2TS) fuzzy controller, an IT2TS fuzzy Wiener model (IT2TS-FWM), and a time-varying delay and packet dropout compensator. The adaptive IT2TS fuzzy controller has a cascade connection; an IT2TS fuzzy controller followed by an inverse of an autoregressive moving average (IARMA) system. The nonlinear Wiener system is identified online by an IT2TS-FWM. An adaptive Smith predictor (ASP) is proposed to compensate the negative effects related to the time-varying delay. For each communication channel, the packet dropout is compensated via designing a compensation term in the stochastic Bernoulli approach. Based on the Lyapunov stability (LS) function, the parameters of the proposed AIT2FC are updated online. Also, the learning rates are updated online based on the LS function to avoid singularities and guarantee both the stability and fast convergence of the AIT2FC. The results conclude that the proposed controller is better than the other existing controllers.

Adaptive interval type-2 fuzzy control for multi-legged underwater robot with input saturation and full-state constraints

In this paper, a trajectory tracking control strategy is proposed for multi-legged underwater robot in the presence of input saturation and full-state constraints. An anti-windup compensator is introduced to solve the input saturation problem, which can compensate for the saturation difference directly. Besides, the state constrains are addressed by a new state saturation function and the external unknown disturbances are solved by interval type-2 fuzzy neural network approximator. The dynamic surface control method combines with backstepping method is utilised to construct Lyapunov function. The control law and signals of the closed-loop system can be guaranteed to achieve semi-global uniform boundedness. Numerical simulations are presented to show the effectiveness of the proposed algorithm.

One wheel vehicle real world control based on interval type 2 fuzzy controller

In this study, a novel adaptive interval type-2 fuzzy controller (AIT2FC) is used for a single-wheel vehicle (SWV) control problem. A control technique for use by a person riding on an SWV that enables real-world balance control of electric unicycles is proposed. The proposed SWV can keep balance and move around. Moreover, it can also stand up stably in any position when a person rides on it without touching the ground. AIT2FC is an advanced version of the traditional Mamdani-like fuzzy controller. The AIT2FC parameters are adjusted online using an intelligent dynamic tuning method. Moreover, the parameter learning rates of the adaptive laws are determined using a stability algorithm to guarantee the system error convergence. Furthermore, an SWV based on a microcontroller with some handmade hardware circuits is implemented. Finally, the efficiency of the AIT2FC is proved by real-time control of SWVs.

Lyapunov approach based design of a gain adaptive interval type-2 fuzzy controller for servo systems

This paper focuses on the development of a stable Mamdani type-2 fuzzy logic based controller for automatic control of servo systems. The stability analysis of the fuzzy controller has been done by employing the concept of Lyapunov. The Lyapunov approach results in the derivation of an original stability analysis that can be used for designing the rule base of our proposed online gain adaptive Interval Type-2 Fuzzy Proportional Derivative controller (IT2-GFPD) for servo systems with assured stability. In this approach a quadratic positive definite Lyapunov function is used and sufficient stability conditions are satisfied by the adaptive type-2 fuzzy logic control system. Illustrative simulation studies with linear and nonlinear models as well as experimental results on a real-time servo system validate the stability and robustness of the developed intelligent IT2-GFPD. A comparative performance study of IT2-GFPD with other controllers in presence of noise and disturbance also proves the superiority of the proposed controller.

Development and Control of a Pneumatic-Actuator 3-DOF Translational Parallel Manipulator with Robot Vision.

A vision-based three degree-of-freedom translational parallel manipulator (TPM) was developed. The developed TPM has the following characteristics. First, the TPM is driven by three rodless pneumatic actuators and is designed as a horizontal structure to enlarge its horizontal working space to cover a conveyor. Then, a robot-vision system (including a webcam mounted on the TPM) collects images of objects on the conveyor and transfers them through the LabVIEW application programming interface for image processing. Since it is very difficult to achieve precise position control of the TPM due to the nonlinear couplings among the robot axes, feedback linearization is utilized to design an adaptive interval type-2 fuzzy controller with self-tuning fuzzy sliding-mode compensation (AIT2FC-STFSMC) for each rodless pneumatic actuator to attenuate nonlinearities, function approximation errors, and external disturbances. Finally, experiments proved that the vision-based three degree-of-freedom TPM was capable of accurately tracking desired trajectories and precisely executing pick-and-place movement in real time.

Adaptive Control of Venturini Modulation Based Matrix Converters Using Interval Type-2 Fuzzy Sets

In this paper, an adaptive interval type-2 fuzzy control scheme is proposed for matrix converters. This strategy combines the power of adaptive control theory with type-2 fuzzy logic systems to achieve accurate tracking and robustness to both structured and unstructured uncertainties. Unlike other control techniques, no a priori offline training, weights initialization, parameters, or dynamics’ knowledge is required. Results for different situations highlight the performance of the proposed controller in transient and steady-state conditions under grid voltage uncertainties. Furthermore, the adaptive type-2 fuzzy controller is compared against its type-1 counterpart.

Observer-based adaptive interval type-2 fuzzy control of uncertain MIMO nonlinear systems with unknown asymmetric saturation actuators

An observer-based controller is proposed for a class of uncertain MIMO nonlinear systems preceded by actuators with unknown asymmetric saturations. The gains of actuators are unknown nonlinear functions that are generally functions of states. The unknown nonlinearities are approximated by interval type-2 fuzzy systems where the consequent parameters are tuned adaptively. Meanwhile, the interval type-2 fuzzy systems due to their structure can cope with the uncertainties in antecedent membership functions. The states of the system do not need to be known. To tackle the unavailability of states an observer based on strictly positive real (SPR) theory is designed to estimate the states. To cope with the fuzzy approximation error and external disturbances an adaptive continuous robust structure is proposed. The continuous robust structure due to adaptiveness does not need the bound of uncertainties and due to continuity does not have the chattering problem. All the adaptive laws are derived via Lyapunov synthesis approach. Meanwhile, all the signals in the closed-loop system are guaranteed to be semi-globally uniformly ultimately bounded (SGUUB) and the mean-square tracking error can be made arbitrarily small. Simulation results reveal the effectiveness of the proposed method in terms of coping well with the uncertainties and saturation actuators while maintaining satisfactory performance.

A simplified adaptive interval Type-2 fuzzy control in practical industrial application

Adaptive Type-2 fuzzy control possesses control performance better than the traditional adaptive fuzzy control. However, heavy computation burden obviously blocks the utilization of adaptive Type-2 fuzzy control in industrial application. By adopting novel piecewise fuzzy sets and center-average type-reduction, a simplified adaptive interval Type-2 fuzzy controller involving less computation is developed for practical industrial application. In the proposed controller, the inputs are divided into several subintervals and then two piecewise fuzzy sets are used for each subinterval. With the manner of piecewise fuzzy sets and a novel fuzzy rules inference engine, only part of fuzzy rules are simultaneously activated in one control loop, which exponentially decreases the computation and makes the controller appropriate in industrial application. The simulation and experimental study, involving the popular magnetic levitation platform, shows the predicted system with theoretical stability and good tracking performance. The analysis indicates that there is far less computation of the proposed controller than the traditional adaptive interval Type-2 fuzzy controller, especially when the number of fuzzy rules and fuzzy sets is large, and the controller still maintains good control performance as the traditional one.

Design of Adaptive Interval Type-2 Fuzzy Control System and Its Stability Analysis

Due to the requirement of dealing with the uncertainty and random disturbance in the real-time applications, a framework of adaptive type-2 fuzzy control system is designed. Since most of existed stability analysis results can not be used for the proposed system, by transferring the interval membership degrees into the constrained typical membership degrees, the closed-loop stability of proposed control system is analyzed. The necessary stability conditions are deduced. All stability analysis results are summarized into a Theorem. Comparing with other stability analysis results, the proposed method need less constrains and is easy to be used for controller design issue.

Adaptive H8 Fuzzy Control for a Class of Uncertain Discrete-Time Nonlinear Systems

In this paper, an adaptive interval type-2 fuzzy controller is proposed for a class of unknown nonlinear discrete-time systems with training data corrupted by noise or rule uncertainties involving external disturbances. Adaptive interval type-2 fuzzy control scheme and control approach are incorporated to implement the main objective of controlling the plant to track a reference trajectory. The Laypunov stability theorem has been used to testify the asymptotic stability of the whole system and the free parameters of the adaptive fuzzy controller can be tuned on-line by an output feedback control law and adaptive laws. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. The simulation example is given to confirm validity and tracking performance of the advocated design methodology.

Adaptive interval type-2 fuzzy sliding mode control for unknown chaotic system

In this paper, a novel adaptive interval type-2 fuzzy sliding mode control (AIT2FSMC) methodology is proposed based on the integration of sliding mode control and adaptive interval type-2 fuzzy control for chaotic system. The AIT2FSMC system is comprised of a fuzzy control design and a hitting control design. In the fuzzy control design, an interval type-2 fuzzy controller is designed to mimic a feedback linearization (FL) control law. In the hitting control design, a hitting controller is designed to compensate the approximation error between the FL control law and the interval type-2 fuzzy controller. The parameters of the interval type-2 fuzzy controller, as well as the uncertainty bound of the approximation error, are tuned adaptively. The adaptive laws are derived in the sense of Lyapunov stability theorem, thus the stability of the system can be guaranteed. The proposed control system compared to adaptive fuzzy sliding mode control (AFSMC). Simulation results show that the proposed control systems can achieve favorable performance and robust with respect to system uncertainties and external disturbances.

Observer-based robust adaptive interval type-2 fuzzy tracking control of multivariable nonlinear systems

In this paper, in order to deal with training data corrupted by noise or rule uncertainties, a new observer-based indirect adaptive interval type-2 fuzzy controller is developed for nonlinear MIMO systems involving external disturbances using fuzzy descriptions to model the plant. Based on the universal approximation theorem, a fuzzy logic controller equipped with a training algorithm is proposed such that the tracking error, because of the matching error and external disturbance, is attenuated to an arbitrary desired level using the H∞ tracking design technique. Simulation results show that the interval type-2 fuzzy logic system can handle unpredicted internal disturbances—data uncertainties, very well, but the adaptive type-1 fuzzy controller must expend more control effort in order to handle noisy training data. In the meantime, the adaptive fuzzy controller can perform successful control and guarantee that the global stability of the resulting closed-loop system and the tracking performance can be achieved.

Direct adaptive interval type-2 fuzzy control of multivariable nonlinear systems

A fuzzy logic controller equipped with a training algorithm is developed such that the H ∞ tracking performance should be satisfied for a model-free nonlinear multiple-input multiple-output (MIMO) system, with external disturbances. Due to universal approximation theorem, fuzzy control provides nonlinear controller, i.e., fuzzy logic controllers, to perform the unknown nonlinear control actions and the tracking error, because of the matching error and external disturbance is attenuated to arbitrary desired level by using H ∞ tracking design technique. In this paper, a new direct adaptive interval type-2 fuzzy controller is developed to handle the training data corrupted by noise or rule uncertainties for nonlinear MIMO systems involving external disturbances. Therefore, linguistic fuzzy control rules can be directly incorporated into the controller and combine the H ∞ attenuation technique. Simulation results show that the interval type-2 fuzzy logic system can handle unpredicted internal disturbance, data uncertainties, very well, but the adaptive type-1 fuzzy controller must spend more control effort in order to deal with noisy training data. Furthermore, the adaptive interval type-2 fuzzy controller can perform successful control and guarantee the global stability of the resulting closed-loop system and the tracking performance can be achieved.

Indirect adaptive interval type-2 fuzzy control for nonlinear systems

A new indirect adaptive type-2 fuzzy controller for a nonlinear dynamical systems is developed in this paper. The parameters of the Membership Functions (MFs) characterising the linguistic terms in the type-2 fuzzy IF-THEN rules change according to some adaptive laws for the purpose of controlling a plant to track a reference trajectory. A supervisory controller is appended to the type-2 fuzzy controller to force the state to be within the constraint set. Stability of this adaptive scheme is established using Lyapunov stability tools, where we guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. The simulation results for an inverted pendulum, show better performances, that is, tracking error and control effort can be made smaller.