Optimal Type-3 Fuzzy System for Solving Singular Multi-Pantograph Equations

In this study a new machine learning technique is presented to solve singular multi-pantograph differential equations (SMDEs). A new optimized type-3 fuzzy logic system (T3-FLS) by unscented Kalman filter (UKF) is proposed for solution estimation. The convergence and stability of presented algorithm are ensured by the suggested Lyapunov analysis. By two SMDEs the effectiveness and applicability of the suggested method is demonstrated. The statistical analysis show that the suggested method results in accurate and robust performance and the estimated solution is well converged to the exact solution. The proposed algorithm is simple and can be applied on various SMDEs with variable coefficients.publishedVersio

Load frequency control for multi-area power systems : a new type-2 fuzzy approach based on Levenberg–Marquardt algorithm

In this study, a new fuzzy approach is proposed for load frequency control (LFC) of a multi-area power system. The main control system is constructed by use of interval type-2 fuzzy inference systems (IT2FIS) and fractional-order calculus. In designing the controller, there is no need for the system dynamics, therefore the system Jacobian is obtained by a multilayer perceptron neural network (MLP-NN). Uncertainties are modeled by IT2FIS, and for training fuzzy parameters, Levenberg Marquardt algorithm (LMA) is used, which is faster and more robust than gradient descent algorithm (GDA). The system stability is studied by Matignon’s stability method under time-varying disturbances. A comparison between the proposed controller with type-1 fuzzy controller on the New England 39-bus test system is also carried out. The simulations demonstrate the superiority of the designed controller

Optimal intelligent control for doubly fed induction generators

For the first time, a novel concept of merging computational intelligence (the type-2 fuzzy system) and control theory (optimal control) for regulator and reference tracking in doubly fed induction generators (DFIGs) is proposed in this study. The goal of the control system is the reference tracking of torque and stator reactive power. In this case, the type-2 fuzzy controller is activated to enhance the performance of the optimum control. For instance, in abrupt changes of the reference signal or uncertainty in the parameters, the type-2 fuzzy system performs a complementary function. Both parametric uncertainty and a perturbation signal are used to challenge the control system in the simulation. The findings demonstrate that the presence of a type-2 fuzzy system as an additional controller or compensator significantly enhances the control system. The root mean square error of the suggested method’s threshold was 0.012, quite acceptable for a control system

Optimal type-3 fuzzy system for solving singular multi-pantograph equations

In this study a new machine learning technique is presented to solve singular multi-pantograph differential equations (SMDEs). A new optimized type-3 fuzzy logic system (T3-FLS) by unscented Kalman filter (UKF) is proposed for solution estimation. The convergence and stability of presented algorithm are ensured by the suggested Lyapunov analysis. By two SMDEs the effectiveness and applicability of the suggested method is demonstrated. The statistical analysis show that the suggested method results in accurate and robust performance and the estimated solution is well converged to the exact solution. The proposed algorithm is simple and can be applied on various SMDEs with variable coefficients

A new task scheduling approach for energy conservation in Internet of Things

Internet of Things (IoT) and mobile edge computing (MEC) architectures are common in real-time application scenarios for improving the reliability of service responses. Energy conservation (EC) and energy harvesting (EH) are significant concerns in such architectures due to the self-sustainable devices and resource-constraint edge nodes. The density of the users and service requirements are further reasons for energy conservation and the need for energy harvesting in these scenarios. This article proposes decisive task scheduling for energy conservation (DTS-EC). The proposed energy conservation method relies on conditional decision-making through classification disseminations and energy slots for data handling. By classifying the energy requirements and the states of the mobile edge nodes, the allocation and queuing of data are determined, preventing overloaded nodes and dissemination. This process is recurrent for varying time slots, edge nodes, and tasks. The proposed method is found to achieve a high data dissemination rate (8.16%), less energy utilization (10.65%), and reduced latency (11.44%) at different time slots

Kinematics and workspace analysis of a 3DOF parallel reconfigurable robot

Parallel robots are widely used in many industrial and medical applications. Reconfigurable parallel robots could be defined as a group of parallel robots that can have different geometries, thus obtaining different degrees of freedom derived from the basic structure. These robots have some disadvantages like having erratic workspace and singular points in the workspace. These limitations should be studied for proper usage of parallel manipulators. This paper presents the kinematics and workspace analysis of a 3DOF parallel reconfigurable robot. This robot has two different configurations. The first configuration is a Tricept robot (3UPS-PU) and the second is a fully Spherical robot (3UPS-S). The kinematic equations are derived based on the geometry of the system and then Jacobian matrices are determined via velocity loop closure analysis. The kinematic model is verified by the results obtained from robot simulation in ADAMS software. Then, the workspace of the robot is determined by considering the kinematic constraints

A non-singleton type-2 fuzzy neural network with adaptive secondary membership for high dimensional applications

This paper develops a non-singleton type-2 fuzzy neural network (NT2FNN) with type-2 3-dimensional membership functions (MFs) and adaptive secondary membership. A new approach based on the squareroot cubature quadrature Kalman filter (SR-CQKF) is proposed for the training the level of the secondary membership and the centers of membership functions. The consequent parameters are learned by using rule-ordered extended Kalman filter (EKF). To show the applicability and effectiveness of proposed NT2FNN in high dimensional problems, four real-world datasets with 4, 7, 13 and 32 input variables are considered. Additionally, the performance of NT2FNN with the proposed learning algorithm is compared with other well-known neural networks and learning algorithms. The simulations demonstrate that the developed method results in high performance in contrast to the other methods. (C) 2019 Elsevier B.V. All rights reserved

Adaptive Robust Terminal Sliding Mode Control with Integral Backstepping Synthesized Method for Autonomous Ground Vehicle Control

Autonomous ground vehicles (AGVs) operating in complex environments face the challenge of accurately following desired paths while accounting for uncertainties, external disturbances, and initial conditions, necessitating robust and adaptive control strategies. This paper addresses the critical path-tracking task in AGVs through a novel control framework for multilevel speed AGVs, considering both structured and unstructured uncertainties. The control system introduced in this study utilizes a nonlinear adaptive approach by integrating integral backstepping with terminal sliding mode control (IBTSMC). By incorporating integral action, IBTSMC continuously adjusts the control input to minimize tracking errors, improving tracking performance. The hybridization of the terminal sliding mode method enables finite time convergence, robustness, and a chatter-free response with reduced sensitivity to initial conditions. Furthermore, adaptive control compensators are developed to ensure robustness against unknown but bounded external disturbances. The Lyapunov stability theorem is employed to guarantee the global asymptotic stability of the closed-loop system and the convergence of tracking errors to the origin within finite time. To validate the effectiveness of the proposed control scheme, high-fidelity cosimulations are conducted using CarSim and MATLAB. Comparative analysis is performed with other methods reported in the literature. The results confirm that the proposed controller demonstrates competitive effectiveness in path-tracking tasks and exhibits strong efficiency under various road conditions, parametric uncertainties, and unknown disturbances

A novel fractional-order type-2 fuzzy control method for online frequency regulation in ac microgrid

In this paper, a novel adaptive fractional-order fuzzy control method is developed for frequency control in an ac microgrid (MG). A sequential general type-2 fuzzy system based on the radial basis neural network is presented for online modeling of the frequency response of the MG. Then, the parameters of the type-2 fuzzy controller based on the online estimated model are online tuned, such that the frequency deviation is minimized. The consequent parameters, i.e., centers of membership functions (MFs), the values of α-cuts, and the type-reduction parameters are optimized based on the proposed algorithm, which is inspired from the particle swarm optimization and artificial bee colony algorithm (PSO-ABC). The simulation results and comparison with other methods show that the proposed control scheme is effective, and results in a good and robust performance in the presence of variation of solar radiation, wind speed, load disturbance, and time-varying dynamics of the other units of MG. Moreover, the effectiveness of the proposed fuzzy system and the learning algorithm are examined by using white noise as the control input, and it is shown that the proposed identification scheme results in good performance even in the noisy environment