General Type-2 Fuzzy Logic Controllers Research Articles

Towards Systematic Design of General Type-2 Fuzzy Logic Controllers: Analysis, Interpretation, and Tuning

This article aims to provide a new perspective on how the deployment of general type-2 (GT2) fuzzy sets affects the mapping of a class of fuzzy logic controllers (FLCs). It is shown that an α-plane represented a GT2-FLC is easily designed via baseline type-1 and interval type-2 FLCs and two design parameters (DPs). The DPs are the total number of α planes and the tuning parameter of the secondary membership function that are interpreted as sensitivity and shape DPs, respectively. We provide a clear understanding and interpretation of the sensitivity and shape DPs on controller performance through various comparative analyses. We present design approaches on how to tune the shape DP by providing a tradeoff between robustness and performance. We also propose two online scheduling mechanisms to tune the shape DP. We explore the effect of the sensitivity DP on the GT2-FLC and provide practical insights on how to tune the sensitivity DP. We present an algorithm for tuning the sensitivity DP that provides a compromise between computational time and sensitivity. We validate our analyses, interpretations, and design methods with experimental results conducted on a drone. We believe that this article provides clear explanations on the role of DPs on the performance, robustness, sensitivity, and computational time of GT2-FLCs.

A new fractional-order general type-2 fuzzy predictive control system and its application for glucose level regulation

In this paper a new robust fractional-order predictive controller is presented and employed to regulate the glucose level in type-1 diabetes. The dynamics of the system is fully unknown an it is online estimated by a fractional-order model using interval Type-2 (T2) fuzzy logic system. The proposed control system is composed of two main controllers which are the predictive General T2 Fuzzy Logic Controller (GT2-FLC) and compensator controller. In this structure, the main controller is the GT2-FLC which is optimized via the Biogeography-based Optimization (BBO) algorithm such that to minimize a cost function in a fixed prediction horizon. The compensator controller is designed to guarantee the closed-loop asymptotic stability. The performance of proposed control strategy is examined on the modified Bergman’s model of some patients with time-varying parameters, external noise perturbation and meal disturbances. The effectiveness of the proposed control scheme is verified and is compared with the other T2 fuzzy and well-known model predictive controllers. The results of the paper clearly show the superiority of the proposed T2 fuzzy logic control system.

Balance Adjustment of Power-Line Inspection Robot Using General Type-2 Fractional Order Fuzzy PID Controller

In this study, a general type-2 fractional order fuzzy PID (GT2FO-FPID) controller is proposed to fulfil the balance adjustment of the Power-line Inspection (PLI) robot system. It is a combination of Mamdani general type-2 fuzzy logic controller (GT2-FLC) and fractional PID controller. Since the PLI robot system is an under-actuated system, it’s necessary to get complete information of the system. However, when all state variables are treated as input to the controller, there is a problem with the rule explosion. Because of this, the information fusion method is adopt to solve the problem and simplify the controller design. At the same time, fractional-order integral-differential operators and input-output scaling factors, which are taken as design variables and optimized by genetic algorithm (GA). To assess the performance of proposed controller based on symmetry criterion, we compared it against existing controllers, i.e., interval type-2 fractional order fuzzy PID (IT2FO-FPID), type-1 fractional order fuzzy PID (T1FO-FPID), and conventional fractional order (FOPID) controllers. Furthermore, to show the anti-inference ability of the proposed controller, three common perturbed process are tested. Finally, simulation results show that the GT2FO-FPID controller outperforms other controllers in the presence of external perturbations on the PLI robot system.

Similarity-based non-singleton general type-2 fuzzy logic controller with applications to mobile two-wheeled robots

Similarity-based non-singleton general type-2 fuzzy logic controller with applications to mobile two-wheeled robots

Double Closed-Loop General Type-2 Fuzzy Sliding Model Control for Trajectory Tracking of Wheeled Mobile Robots

In this paper, a double-loop control scheme, combining general type-2 fuzzy logic controller (GT2FLC) and non-singular terminal sliding mode control (NTSMC), is proposed to stabilize the non-holonomic wheeled mobile robot. In the double-loop tracking controller, the outer ring adopts the exponential approach law used to track the position state quickly and the inner ring adopts the double-power approaching law, which makes the attitude convergence faster than the position to ensure the stability of the closed-loop system and deal with the random disturbances. NTSMC is designed for both loops to achieve fast convergence of the system in a finite time. The GT2FLC is used to adjust the gain of the approaching law, so as to enhance the adaptability to random disturbances and weaken the chattering of sliding mode input. The simulation results show that the proposed method can achieve small error and fast tracking of position and attitude under random disturbance, and its performance is better than other controllers.

Non-singleton General Type-2 Fuzzy Control for a Two-Wheeled Self-Balancing Robot

This paper presents several non-singleton general type-2 fuzzy logic controllers (NGT2FLCs) for an under-actuated mobile two-wheeled self-balancing robot to improve the anti-interference capability of the system. Four kinds of fuzzifiers, including singleton fuzzifier, type-1 non-singleton fuzzifier, interval type-2 non-singleton fuzzifier and general type-2 non-singleton fuzzifier, are considered to construct different general type-2 fuzzy logic controllers (GT2FLCs). In order to show the superiority of the GT2FLCs, three kinds of interval type-2 fuzzy logic controllers (IT2FLCs), including singleton IT2FLCs, type-1 non-singleton IT2FLCs (N1IT2FLCs) and interval type-2 non-singleton IT2FLCs (N2IT2FLCs), are also presented. A comparative study between singleton fuzzy controllers and non-singleton fuzzy controllers, and IT2FLCs and GT2FLCs is also shown. All simulation results show that the performance of non-singleton fuzzy logic controllers is better than that of singleton fuzzy logic controllers. The NGT2FLCs get the best performance in the presence of uncertainties and external disturbances.

A new and robust control strategy for a class of nonlinear power systems: Adaptive general type-II fuzzy

In this article, a robust direct adaptive general type-2 fuzzy logic controller is introduced for a class of nonlinear power systems. The proposed controller uses the advantages of general type-2 fuzzy logic system in handling dynamic uncertainties to approximate unknown nonlinear actions. Implementing general type-2 fuzzy system is computationally costly; however, by using a recently introduced α-plane representation, general type-2 fuzzy logic system can be seen as a composition of several interval type-2 fuzzy logic systems with a corresponding level of α for each. Linguistic rules are directly incorporated into the controller. In addition, an [Formula: see text] compensator is corrupted to attenuate external disturbance and fuzzy approximation error. General type-2 fuzzy adaptation laws are also derived using Lyapunov approach. It is worth noting that mathematical analysis proves the stability of the closed-loop system. In order to evaluate the performance of the proposed controller, the results are compared with those obtained by direct adaptive type-1 fuzzy logic controller, a direct adaptive interval type-2 fuzzy logic controller and adaptive proportional–integral–derivative, which are the latest researches in the problem in hand. The proposed controller is applied to a chaotic power system as a case study. Simulation reveals the effectiveness of the proposed controller in presence of dynamic uncertainties and external disturbances.

Direct adaptive general type‐2 fuzzy control for a class of uncertain non‐linear systems

In this study, a stable direct adaptive general type-2 fuzzy logic controller (DAG2FLC) is introduced for a class of non-linear systems. The proposed controller uses advantages of general type-2 fuzzy logic systems (GT2FLSs) in handling dynamic uncertainties to approximate unknown non-linear actions. Implementing general type-2 fuzzy systems is computationally costly; however, by using a recently introduced α-plane representation, a GT2FLS can be seen as composition of several interval type-2 fuzzy logic systems with a corresponding level of α for each. Linguistic rules are directly incorporated into the DAG2FLC controller and a H ∞ compensator is added to attenuate external disturbance and fuzzy approximation error. Also general type-2 fuzzy adaptation laws are derived using Lyapunov approach, and the stability of the closed-loop system has been proven by mathematical analysis. In order to evaluate the performance of the proposed controller, the results are compared with those obtained by direct adaptive type-1 fuzzy logic controller and a direct adaptive interval type-2 fuzzy logic controller, which are the latest researches in the problem in hand. The proposed controller is applied to a chaotic Gyro system as a case study. Simulation reveals the effectiveness of the proposed controller in presence of dynamic uncertainties and external disturbances.