Heuristic Fuzzy Controller Research Articles

Modelling and control of a non‐holonomic pendulum‐driven spherical robot moving on an inclined plane: simulation and experimental results

This study deals with mathematical modelling and motion control of a pendulum-driven spherical robot (PDSR) moving on an inclined plane. Firstly, the model of a PDSR on an inclined plane is developed using a Lagrangian formulation. Afterwards, a PID controller is tuned to stabilise the robot on the inclined plane. A heuristic fuzzy controller and a PID controller are also designed to move the robot upward or downward the inclined plane and approach the target point. A switched control, i.e. a combination of the designed controllers, is finally suggested to control the motion of the robot on the inclined plane. Simulations are conducted to investigate and compare the performance of the proposed controllers. Experiments are also conducted for different cases to validate the simulations and also evaluate the control strategies via image processing.

Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties

This paper addresses a three-dimensional (3D) path following control problem for underactuated autonomous underwater vehicle (AUV) subject to both internal and external uncertainties. A two-layered framework synthesizing the 3D guidance law and heuristic fuzzy control is proposed to achieve robust adaptive following along a predefined path. In the first layer, a 3D guidance controller for underactuated AUV is presented to guarantee the stability of path following in the kinematics stage. In the second layer, a heuristic adaptive fuzzy algorithm based on the guidance command and feedback linearization Proportional-Integral-Derivative (PID) controller is developed in the dynamics stage to account for the nonlinear dynamics and system uncertainties, including inaccuracy modelling parameters and time-varying environmental disturbances. Furthermore, the sensitivity analysis of the heuristic fuzzy controller is presented. Against most existing methods for 3D path following, the proposed robust fuzzy control scheme reduces the design and implementation costs of complicated dynamics controller, and relaxes the knowledge of the accuracy dynamics modelling and environmental disturbances. Finally, numerical simulation results validate the effectiveness of the proposed control framework and illustrate the outperformance of the proposed controller as well.

Application of Flywheel Energy Storage System to Enhance Transient Stability of Power Systems

This article aims to develop a self-organizing control scheme for the flywheel energy storage system (FESS) to enhance transient stability of power systems. Due to the model of transient stability control being a nonlinear dynamic equation, a self-organizing neural network-based fuzzy controller is introduced to implement in the FESS control system. The membership functions and control rules of fuzzy controller are expressed as the processing nodes in the neural network (NN). Then, the heuristic fuzzy control parameters can be optimally tuned from training examples due to excellent organizing capability of NN. For robustness consideration, a multilayer neural network is used to learn the relation of operation conditions and optimal parameters of the controller. To demonstrate the effectiveness of the proposed controller, some simulations are conducted on a single-machine-infinite-bus (SMIB) power system. The results show that the FESS with the proposed controller has produced significant improvement in the power system stability.

Neural network-based self-organizing fuzzy controller for transient stability of multimachine power systems

An efficient self-organizing neural fuzzy controller (SONFC) is designed to improve the transient stability of multimachine power systems. First, an artificial neural network (ANN)-based model is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the ANN model. With the excellent learning capability inherent in the ANN, the traditional heuristic fuzzy control rules and input/output fuzzy membership functions can be optimally tuned from training examples by the backpropagation learning algorithm. Considerable rule-matching times of the inference engine in the traditional fuzzy system can be saved. To illustrate the performance and usefulness of the SONFC, comparative studies with a bang-bang controller are performed on the 34-generator Taipower system with rather encouraging results.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>