Top PDF A Fuzzy Logic Controller for Autonomous Wheeled Vehicles

A Fuzzy Logic Controller for Autonomous Wheeled Vehicles

A Fuzzy Logic Controller for Autonomous Wheeled Vehicles

As a backup to the primary steering fuzzy controller, which usually regulates obstacle avoidance steering, the Bug module is implemented. This module is not designed to emulate human behavior, but rather to reach the target when the steering fuzzy module is not able to. The Bug module is based on the Bug2 algorithm proposed by (Lumelsky & Stepanov ,1987, Lumelsky & Skewis, 1988, Lumelsky, 1991) whose research focused on the application of maze theory to the path planning of a “point automaton”. His research concluded that Bug2 had “unbounded worst-case performance”, i.e. in very rare cases, Bug2 can still drive the vehicle in an unending loop. (Sauerberger & Trabia, 1996) proposed a modified form of this algorithm for autonomous omnidirectional vehicles. Their algorithm, which produces shorter paths in many cases, triggered the Bug algorithm when the orientation of the vehicle was more than 360 degrees away, in either direction, from the angle of the ST line, Figure 10. This can be expressed as the following:
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Using Fuzzy Logic Self-Tuning PI Gain Controller Z-Source Inverter in Hybrid Electric Vehicles

Using Fuzzy Logic Self-Tuning PI Gain Controller Z-Source Inverter in Hybrid Electric Vehicles

With simulation and experimental results are given to verify the proposed a new algorithm to control the PDV in ZSI due to SFP controller by using fuzzy logic which it adapt to DIV sudden change, in order to improves transient response of PDV, to solve the problems of the output voltage stabilization in the inverter bridge. More importantly, this new algorithm is applied in closed loop speed control system of induction motor base on the DTC-MSVM control strategy with many exceptional features such as: fast torque response, low steady state torque ripple, increase accurate of speed motor, to increases robustness of speed motor control, enhances disturbance rejection and increase performance of the system. Therefor, the combination of SFP based control algorithm and DTC-MSVM control strategy is the best candidate for HEV applications.
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Fuzzy Logic Control Of An Autonomous Mobile Robot

Fuzzy Logic Control Of An Autonomous Mobile Robot

Therefore, in this project, based on the Fuzzy Logic Technique, this project will develop a tracking controller for the dynamic model of a unicycle mobile robot by integrating a kinematics controller and a torque controller. The tracking controller for the dynamic model will use a control law such that the mobile robot kinematics (velocity) reach the given velocity inputs and a fuzzy logic controller provided the required torques for the actual mobile robot. Computer simulations will be done using Matlab software, confirming the performance of the tracking controller and its application to different navigation problems. The Mamdani’s approach will also be applied and differentiate with Sugeno’s method.
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Fuzzy Logic Control Of An Autonomous Mobile Robot

Fuzzy Logic Control Of An Autonomous Mobile Robot

4.13 Rule Viewer of the Sugeno Fuzzy Logic Controller 36 4.14 Surface Viewer of the Sugeno Fuzzy Logic Controller 37 4.15 Mamdani’s Input and Output Fuzzy Logic Controller 37 4.16 Inputs of Membership Function for Mamdani Fuzzy Logic 38

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Dynamical formation control of wheeled mobile robots based on fuzzy logic

Dynamical formation control of wheeled mobile robots based on fuzzy logic

In this paper, a leader-follower based formation control of nonholonomic wheeled mobile robots has been studied via fuzzy logic and system dynamics. To this end, the dynamics of the system obtained and simulated in MATLAB software and then verified via ADAMS multi-body simulation software. Some fuzzy logic based controllers were then developed to adjust the wheels actuating torques. It was shown that the suggested dynamical controller is able to generate and keep the desired formation. Additionally, it was proved that it can handle formation change and also copes with the noisy data received from the leader robot. Finally, the performance of the suggested dynamical controller of this paper was compared against that of the kinematical controller previously presented by Amoozgar et al. [12]. The obtained computer simulations revealed the success of the suggested controller in terms of tracking and controlling effort as compared with those proposed by Amoozgar et al. [12].
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Steering Control For A Car Using Fuzzy Logic Controller

Steering Control For A Car Using Fuzzy Logic Controller

For Jurgen Ackerman with his paper about damping of vehicle roll dynamic by gain scheduling active steering is about Active steering is applied to robustly reduce the rollover risk of vehicles with an elevated center of gravity. An actuator sets an auxiliary steering angle which is mechanical added to the steering angle commanded by the driver. The control law presented is based on feedback of the roll rate and the roll acceleration. The controller gain are scheduled with the speed and the vehicle's CG height the controller gains are found by the parameter spec approach and constrained optimization in frequency domain. Robust reduction of transient rollover risk is show by evaluation of the sensitivity function at various operating points. Simulation of a double lane change maneuver illustrates the benefit in time domain.
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Fuzzy logic controller for DC motor position control

Fuzzy logic controller for DC motor position control

An electric motor is an electric machine that converts electrical energy into mechanical energy. The electric motor can be powered by Direct Current (DC) sources such as batteries, motor vehicles or rectifiers, or by an Alternating Current (AC) sources, such as from power grid, inverters, or generators. DC motor has been selected in this project because it is widely used in industrial applications, robot manipulators and home appliances where speed and position control are required. The DC motors can come in many shapes and sizes, makes the development of dc motor application quite easy and flexible. It also has high reliabilities and low cost [1].
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Eigen Value Analysis of Optimal Controller Design of Wheeled Autonomous Mobile Robot

Eigen Value Analysis of Optimal Controller Design of Wheeled Autonomous Mobile Robot

developed to carry out the study by simulation of the system aswell as the surrounding conditions [5].There have been research and in the field of mapping of dynamic environment of the robotic system so that it can navigate in free space with minimum human intervention [4,13,15,16]. So it is not only the best experimental tool but also an ideal experimental platform. The research of inverted pendulum has profound meaning in theory and methodology, and has valued by various countries’ scientists and different strategies to control the movement either limited or free [17,18].The constraints of the inverted pendulum robot are same as the constraint of two wheeled mobile robot, which produced the model error and it effects to the position and orientation errorsA MOEA based LQR weighting matrices design approach is proposedin [21]. The multi-objective optimization model of LQR weighting matrices is established[19].Then optimal control for trajectory tracking and stability of the system using LQR and PID controllers is described in [20]. The dynamical model of a TWIP mobile robot can be derived using various methods but most popular is Euler Lagrange approach[20-21],modified Lagrange multiplier method [22]. Comparisons between model based and non-model base controllers and also conventional PID controller for a balancing the robot is a common research interest and has been presented by various researchers. Fuzzy Logic Controllers are non-model based performs better than the LQR and PID controllers in terms faster response and less overshoot, but has higher energy consumption than the other two[2,7,23]. A detailed description for derivation of dynamic mathematical model is discussed and employed in [24].
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Circumventing the fuzzy type reduction for autonomous vehicle controller

Circumventing the fuzzy type reduction for autonomous vehicle controller

Fuzzy type-2 controllers can easily deal with systems nonlinearity and utilise humans’ expertise to solve many complex control problems; they are also very good at processing uncertainty, which exists in many robotic systems, such as autonomous vehicles. However, their computational cost is high, especially at the type reduction stage. In this research, it is aimed to reduce the computation cost of the type reduction stage, thus to facilitate faster performance speed and increase the number of actions able to be operated in one microprocessor. Proposed here are adaptive integration principles with a binary successive search technique to locate the straight or semi-straight segments of a fuzzy set, thus to use them in achieving faster weighted average computation. This computation is very important because it runs frequently in many type reductions. A variable adaptation rate is suggested during the type reduction iterations to reduce the computation cost further. The influence of the proposed approaches on the fuzzy type-2 controller’s error has been mathematically analysed and then experimentally measured using a wall-following behaviour, which is the most important action for many autonomous vehicles. The resultant execution time-gain of the proposed technique has reached to 200%. This evaluated with respect to the execution time of the original, unmodified, type reduction procedure.
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Autonomous Hybrid Generation Scheme Based on PV Array and Wind  Driven Induction Generators with Fuzzy Logic Controller

Autonomous Hybrid Generation Scheme Based on PV Array and Wind Driven Induction Generators with Fuzzy Logic Controller

supply the required load even without battery. However in this work, a settled resistive load has been considered for the controller configuration and additionally unbalance in load has not been considered. Further to this, hybrid scheme in view of PV and IG revealed in the writing, require an utility grid for its operation. The vast majority of them utilize a doubly encouraged induction generator, which is at the end of the day costly. It is endeavored to build up a vigorous and dependable control conspire for self-ruling hybrid system in view of PV source and wind driven induction generator that can give consistent controlled three phase output voltage for a wide range of load with or without unbalance. In the current work, an improved controller for battery less mode operation has been produced for a PV fed Boost Converter encouraged Inverter energized wind driven IG conspire (PVEWIG) to direct the inverter DC connect without battery. In this scheme, a three phase variable resistive and additionally inductive load with or without unbalance has been considered. The proposed controller guarantees voltage regulation of DC connect and enhances the power quality parameters at purpose of normal coupling (PCC) under shifting illumination, temperature of PV array and wind speed variety in the wind generator. The proposed plot has been actualized in equipment utilizing a 2.4 kW PV cluster and a 2.25 kW Wind turbine emulator driven SCIG. This paper is sorted out as takes after. Area II depicts the power circuit topology of the PVEWIG conspire. The nitty gritty clarification of the control plot is displayed in area III. The displaying and simulation comes about are arrayed in area IV and equipment approval comes about are introduced in segment V.
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Fuzzy Logic Controller Based Performance Analysis of Autonomous Pv-Array Excited Wind-Driven Induction Generator

Fuzzy Logic Controller Based Performance Analysis of Autonomous Pv-Array Excited Wind-Driven Induction Generator

scheme can operate to supply the required load even in the absence of battery [11]. However in this work, a fixed resistive load has been considered for the controller design as well as unbalance in load has not been considered. Further to this, hybrid scheme based on PV and IG reported in the literature [15]-[16], need a utility grid for its operation. Most of them employ a doubly fed induction generator [17]-[18], which is once again expensive [19]-[21]. It is attempted to develop a robust and reliable control scheme for autonomous hybrid system based on PV source and wind driven induction generator that can provide continuous regulated three phase output voltage for all types of load with or without unbalance. In the present work, a simplified controller for battery less mode operation has been developed for a PV fed Boost Converter fed Inverter excited wind driven IG scheme (PVEWIG) to regulate the inverter DC link in the absence of battery. In this scheme, a three phase variable resistive as well as inductive load with or without unbalance has been considered. The proposed controller ensures voltage regulation
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Fuzzy Logic Controller design for 
		autonomous underwater vehicle (AUV) yaw control

Fuzzy Logic Controller design for autonomous underwater vehicle (AUV) yaw control

The conventional Proportional Integral Derivative (PID) controller is based on feedback as shown in Figure 2. For instance for a yaw control, the output signal from a device or the process itself will be measured and compared with the desired value or commonly known as set point. The output will be continuously measured to recalculate the needed correction. Nevertheless, not every controller uses PID controller. It depends on the suitability of some other processes. Some process is more suitable to use only the proportional-integral (PI) controller or proportional- derivative (PD). Yet, for satisfactory control that require elimination of overshoot, addition of derivative element is required. But, still cannot eliminate 100 % overshoot on the system response. Solution on that problem, Artificial Intelligence (AI) controller will be introduced.
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Adaptive Speed Control for Autonomous Mobile Robot using Fuzzy Logic Controller

Adaptive Speed Control for Autonomous Mobile Robot using Fuzzy Logic Controller

The research on fuzzy logic for mobile robot has been immensely proposed since two decades ago. Vamsi et al.[8] have developed a fuzzy logic controller to control the robot motion along a predefined path. They found that if the Fuzzy Logic is appropriately being designed, then it could give better performance of convergence. Another work were reported by M.K Singh et al. whose proposed a fuzzy control scheme that considered two inputs of the mobile robot which are the heading angle and the relative distance between mobile robot and any landmark. The study considered three different scenario of environment, and are analyzed both by simulation and experiments to determine the consistency of their proposed technique. A study on the effect of different fuzzy sets member- ship to the mobile robot navigation also has been proposed by R.Rashid et al[9]. They suggested the designer must choose the fewer membership in the fuzzy logic controller to achieve better and faster time reaching a target. They used triangular membership function to demonstrate their results as it is the com- monly applied membership used for analysis. This result is acceptable as if fewer membership are applied then the processing time will be reduced which finally result in faster time of task completion.
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The Development of Self Balancing Controller for One Wheeled Vehicles

The Development of Self Balancing Controller for One Wheeled Vehicles

Nowadays, although a lot of references engaging in the studies of two-wheeled balancing carriers can be found [2-6], the ones for OWV studies are still insuffi- cient [10,11]. Trevor Blackwell [12], an American engi- neer, has proposed an OWV design on his personal web- site, and the concept OWV, EMBRIO, is published by BRP [13], a motorcycle manufacturer in Canada. Besides, [10] is the newest one based on fuzzy controls for bal- ancing.

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Design of Brushless DC Motor with Fuzzy Logic Controller

Design of Brushless DC Motor with Fuzzy Logic Controller

A small BLDC motor is used in hard disk drives and large BLDC motor is used in electric vehicles. In BLDC motor physical commutator is not necessary because the electric current powers the permanent magnet that causes rotation in motor. So current commutation takes place because of solid state switches. In this case commutation happens electronically. Most commonly used BLDC motors are three phases rather than two phase BLDC motor.

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Performance Analysis of Fuzzy Logic Based PI Controller

Performance Analysis of Fuzzy Logic Based PI Controller

using compensation technique. Shunt Active Power Filter (SAPF) is used to eliminate harmonic current and also it compensates reactive power. In this work, Fuzzy Logic based PI Controller based three-phase shunt active filter is employed for a three-phase four wire systems. The advantage of fuzzy control is that it provides linguistic values such as low, medium, high that are useful in case where the probability of the event to occur is needed. It does not require an accurate mathematical model of the system. A MATLAB/SIMULINK has been used to perform the simulation. Simulink model is developed for three phase four wire system under balanced source condition. The performance of balanced source is done by using Fuzzy Logic based PI controller. Simulation results are obtained and compared by applying different rules i.e. 9, 25, 49 and 81.
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Fuzzy logic control of MPPT controller for PV systems

Fuzzy logic control of MPPT controller for PV systems

The main purpose of this thesis study was to devise an approach to extract maximum energy from a photovoltaic (PV) power system. To that end, we presented PV module maximum power point tracking, along with several approaches to deal with current unresolved problems. Specifically, we proposed a fuzzy logic-based algorithm for tracking optimal power, together with a system model for developing and applying the algorithm. Several different components and subsystems were analyzed and modeled in our work. The models were then tested towards validation and combined to create the optimum power point tracker model. We also performed hardware implementation in order to measure the algorithm’s performance in real-life situations. Analyses of the various dc-dc converter topologies pointed to buck-boost topology as the most promising approach for the maximum power tracker. Therefore, we modeled the PV module and buck-boost converter, and validated them in Simulink. We then used the Fuzzy Logic Toolbox in MATLAB to formulate the fuzzy logic algorithm.
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Fuzzy Logic Controller For Active-Vehicle Suspension System

Fuzzy Logic Controller For Active-Vehicle Suspension System

The suspension system is the automotive system that connects the wheels of the automobile to the body, in such that the body is cushioned from jolts resulting from driving on uneven pavement or rough road surfaces [1]. The best suspension system should reduce the effect of road disturbances. When the body has large oscillation because of road disturbances, the controller applied should dissipate it quickly. Nowadays, most of the car was applied with conventional controller for the suspension system, but cannot give best impact for road comfort and handling stability. There are always been conflict between them.
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Development of an adaptive fuzzy logic controller for HVAC system

Development of an adaptive fuzzy logic controller for HVAC system

Table 8.2-3: Comparison of total actuator travel distance/cycle between chilled water valve on AHUA (AFLC-GA) and AHUB (PIDL) for variation in SATSPT test. Comparison test results for [r]

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ABSTRACT : Fuzzy logic controller (FLC) is the most widely used applications of fuzzy set theory. A Fuzzy Logic

ABSTRACT : Fuzzy logic controller (FLC) is the most widely used applications of fuzzy set theory. A Fuzzy Logic

FLC is the most important control method for nonlinear systems. The two input of fuzzy logic are the error and the change in error. The error was calculated by comparing the output voltage with reference voltage. The change in error was calculated by the present error with previous error. The output voltage of a buck-boost converter is controlled by the fuzzy logic control. The FLC uses linguistic variables as input instead of numerical variables.

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