Mobile Robot Path Following Controller Based On the Sirms Dynamically Connected Fuzzy Inference Model

This paper presents a simple and effective way to implement a path following controller for a differential drive wheeled mobile robot based on the single input rule modules (SIRMs) dynamically connected fuzzy inference model. The control of the mobile robot is divided into two control actions performed in parallel; the heading and the velocity controller. For the heading controller, each input item is assigned with a SIRM and a dynamic importance degree (DID). The velocity controller structure was modified to simplify the design and to fulfill the requirements of the path following method. Here, a common DID is used. The SIRMs and the dynamic importance degrees are designed such that the angular velocity control takes the highest priority over the linear velocity control of the mobile robot. By using the SIRMs and the dynamic importance degrees, the priority orders of the controls are automatically adjusted according to navigation situations. The proposed fuzzy controller has a simple and intuitively understandable structure, and executes the two control actions entirely in parallel. Simulation results show that the proposed fuzzy controller can drive a mobile robot smoothly with a high precision through a series of waypoints to attain its final target in short time

Mobile Robot Path Following Controller Based On the Sirms Dynamically Connected Fuzzy Inference Model

This paper presents a simple and effective way to implement a path following controller for a differential drive wheeled mobile robot based on the single input rule modules (SIRMs) dynamically connected fuzzy inference model. The control of the mobile robot is divided into two control actions performed in parallel; the heading and the velocity controller. For the heading controller, each input item is assigned with a SIRM and a dynamic importance degree (DID). The velocity controller structure was modified to simplify the design and to fulfill the requirements of the path following method. Here, a common DID is used. The SIRMs and the dynamic importance degrees are designed such that the angular velocity control takes the highest priority over the linear velocity control of the mobile robot. By using the SIRMs and the dynamic importance degrees, the priority orders of the controls are automatically adjusted according to navigation situations. The proposed fuzzy controller has a simple and intuitively understandable structure, and executes the two control actions entirely in parallel. Simulation results show that the proposed fuzzy controller can drive a mobile robot smoothly with a high precision through a series of waypoints to attain its final target in short time

Optimal control of wind energy conversion systems with doubly-fed induction generators

Wind energy conversion systems (WECSs) have become the interesting topic over recent years for the renewable electrical power source. They are a more environmentally friendly and sustainable resource in comparison with the fossil energy resource. The WECS using a doubly-fed induction generator (DFIG) to convert mechanical power into electrical power has a significant advantage. This WECS requires a smaller power converter in comparison with a squirrel cage induction generator. Efficiency of the DFIG-WECS can be improved by a suitable control system to maximise the output power from WECS. A maximum power point tracking (MPPT) controller such as tip-speed ratio (TSR)control and power signal feedback (PSF) control is use to maximise mechanical power from wind turbine and a model-based loss minimisation control (MBLC) is used to minimise electrical losses of the generator. However, MPPT and MBLC require the parameters of the wind turbine and the generator for generating the control laws like optimal generator speed reference and d-axis rotor current reference. The Efficiencies of the MPPT and MBLC algorithms deteriorate when wind turbine and generator parameters change from prior knowledge. The field oriented control for a DFIG in the WECS is extended by introducing a novel control layer generating online optimal generator speed reference and d-axis rotor current reference in order to maximise power produced from the WECS under wind turbine and DFIG parameter uncertainties, which is proposed. The single input rule modules (SIRMs) connected fuzzy inference model is applied to the control algorithm for optimal power control for variable-speed fixed-pitch wind turbine in the whole wind speed range by generating an online optimal speed reference to achieve optimal power under wind turbine parameter uncertainties. The proposed control combines a hybrid maximum power point tracking (MPPT) controller, a constant rotational speed controller for below-rated wind speed and a limited-power active stall regulation by rotational speed control for above-rated wind speed. The three methods are appropriately organised via the fuzzy controller based SIRMs connected fuzzy inference model to smooth transition control among the three methods. The online parameter estimation by using Kalman filter is applied to enhance model-based loss minimisation control (MBLC). The d-axis rotor current reference of the proposed MBLC can adapt to the accurate determination of the condition of minimum electrical losses of the DFIG when the parameters of the DFIG are uncertain. The proposed control algorithm has been verified by numerical simulations in Matlab/Simulink and it has been demonstrated that the energy generated for typical wind speed profiles is greater than that of a traditional control algorithm based on PSF MPPT and MBLC

Composite state variable based nonlinear backstepping design for the underactuated TORA system

A nonlinear vibration controller is proposed for the translational oscillators with rotating actuator (TORA) system with the recursive technology. A composite state variable (CSV) is defined for the TORA system to start the recursive process. The design procedure treats the some state variables as virtual control inputs to design the virtual controllers step by step until the nonlinear vibration controller is obtained. The system stability is studied via a stability theorem and simulation results show the validity of the proposed controller

Optimal fuzzy control using hedge algebras of a damped elastic jointed inverted pendulum

In this paper, three controllers including OFCHA (optimal fuzzy control using hedge algebras-HAs), FCHA (fuzzy control using HAs) and CFC (conventional fuzzy control) are designed. Our attention is paid to the stability in the vertical position of a damped-elastic-jointed inverted pendulum subjected to a time-periodic follower force. Different values of the pendulum length are considered. Simulation results are exposed to illustrate the effect of OFCHA in comparison with FCHA and CFC

Nonlinear backstepping design for the underactuated TORA system

The nonlinear feedback cascade model of the underactuated translational oscillators with rotating actuator is obtained through a collocated partial feedback linearization and a global change of coordinates. A nonlinear controller is designed with the backsteping technology, which treats the state variables as virtual control inputs to design the virtual controllers step by step. The system stability is proved with the Lyapunov stability theorem. The simulation results show the system under any initial states can be asymptotically stabilized to the origin and the controller has a good control performance

Artificial Hydrocarbon Networks Fuzzy Inference System

This paper presents a novel fuzzy inference model based on artificial hydrocarbon networks, a computational algorithm for modeling problems based on chemical hydrocarbon compounds. In particular, the proposed fuzzy-molecular inference model (FIM-model) uses molecular units of information to partition the output space in the defuzzification step. Moreover, these molecules are linguistic units that can be partially understandable due to the organized structure of the topology and metadata parameters involved in artificial hydrocarbon networks. In addition, a position controller for a direct current (DC) motor was implemented using the proposed FIM-model in type-1 and type-2 fuzzy inference systems. Experimental results demonstrate that the fuzzy-molecular inference model can be applied as an alternative of type-2 Mamdani’s fuzzy control systems because the set of molecular units can deal with dynamic uncertainties mostly present in real-world control applications