Design of Neural Network Mobile Robot Motion Controller

Velagić, Jasmin; Osmić, Nedim; Lačević, Bakir

doi:10.5772/7584

Cited by 37 publications

(37 citation statements)

References 13 publications

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“…This controller's structure is shown in Fig. 2: [5,12,13,15] Figure 2:The back-stepping controller structure The input error to this controller is defined as follows:…”

Section: The Nn-based Adaptive Back-stepping Controllermentioning

confidence: 99%

“…The proposed control algorithm provides the backstepping controller with adaptive gains which are variable and change according to the reference trajectory [10,11,18,16,20,21]. The NN-based adaptive back-stepping controller structure is shown in Substituting equations (11,12) in the above equation, we have: Therefore, the kinematic controller gains will change and adapt to make the cost function zero according to the gradient descent method as follows: …”

Section: The Nn-based Adaptive Back-stepping Controllermentioning

confidence: 99%

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Untitled

2016

IJERA

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This work primarily addresses the design and implementation of a neural network based controller for the trajectory tracking of a differential drive mobile robot. The proposed control algorithm is an NN-based adaptive controller which tunes the gains of the back-stepping controller online according to the robot reference trajectory and its initial posture. In this method, a neural network is needed to learn the characteristics of the plant dynamics and make use of it to determine the future inputs that will minimize error performance index so as to compensate the back-stepping controller gains. The advantages and disadvantages of theproposed control algorithms will be discussed in each section with illustrations.Comprehensive system modeling including robot kinematics and dynamics modeling has been done. The dynamic modeling is done using Newtonian and Lagrangian methodologies for nonholonomic systems and the results are compared to verify the accuracy of each method. Simulation of the robot model and different controllers has been done using Matlab and Matlab Simulink.

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“…This controller's structure is shown in Fig. 2: [5,12,13,15] Figure 2:The back-stepping controller structure The input error to this controller is defined as follows:…”

Section: The Nn-based Adaptive Back-stepping Controllermentioning

confidence: 99%

Section: The Nn-based Adaptive Back-stepping Controllermentioning

confidence: 99%

Untitled

2016

IJERA

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“…There are many fuzzy logic techniques using various implementations or in combination with other techniques [10][11][12][13][14]. Mobile robot path planning based on neural network approaches presented by many researchers [15][16][17][18]. Among the intelligent techniques ANFIS is a hybrid model which combines the adaptability capability of artificial neural network and knowledge representation of fuzzy inference system [19].…”

Section: Introductionmentioning

confidence: 99%

A New Intelligent Motion Planning for Mobile Robot Navigation using Multiple Adaptive Neuro-Fuzzy Inference System

Mohantyand¹,

Parhi²

2014

Appl. Math. Inf. Sci.

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Abstract:Nowadays intelligent tools such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as effective and suitable methods for modeling an engineering system. This paper presents a new hybrid technique based on the combination of fuzzy inference system and artificial neural network for addressing navigational problem of autonomous mobile robot. First we developed an adaptive fuzzy controller with four input parameters, two output parameters and three parameters each. Afterwards each adaptive fuzzy controller acts as a single takagi-sugeno type fuzzy inference system, where inputs are front obstacle distance (FOD), left obstacle distance (LOD), right obstacle distance (ROD) (from robot), heading angle (HA) (angle to target) and output corresponds to the wheel velocities ( Left wheel and right wheel) for the mobile robot. The effectiveness, feasibility and robustness of the proposed navigational controller have been demonstrated by means of simulation experiments. The real time experimental results were verified with simulation experiments, showing that the proposed navigational algorithm consistently performs better results to navigate the mobile robot safely in a completely or partially unknown environment.

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“…There are many fuzzy logic methods using various implementations or in combination with other techniques [10][11][12][13][14]. Mobile robot path planning based on neural network approaches presented by many researchers [15][16][17][18].Among the intelligent techniques ANFIS is a hybrid model which combines the adaptability capability of artificial neural network and knowledge representation of fuzzy inference system [19].Song and Sheen [20] developed a pattern recognition method based on fuzzy-neuro network for reactive navigation of a car-like robot. Li et al [21] suggested a neuro-fuzzy technique for behavior based control of a car-like robot that navigates among static obstacles.…”

Section: Introductionmentioning

confidence: 99%

A New Intelligent Approach for Mobile Robot Navigation

Mohanty

Parhi

2013

Lecture Notes in Computer Science

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Abstract. In recent times computational intelligent techniques such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as effective and suitable optimization methods for modeling an engineering system. In this paper an efficient hybrid technique has been applied for mobile robot navigation using multiple adaptive neuro-fuzzy inference system (MANFIS). ANFIS has taken the advantages of both fuzzy inference system and artificial neural network. First, we design an adaptive fuzzy controller with four input parameters, two types of output parameters and three parameters each. Next each adaptive fuzzy controller acts as a single Sugeno-Takagi type fuzzy inference system where inputs are the different sensor based information and output corresponds to the velocity of the mobile robot. The implementation of the proposed navigational controller is discussed via numerous simulation examples. It is found that such an adaptive neuro-fuzzy controller is successfully and quickly finding targets in an unknown or partially unknown environment.

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Design of Neural Network Mobile Robot Motion Controller

Cited by 37 publications

References 13 publications

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Untitled

A New Intelligent Motion Planning for Mobile Robot Navigation using Multiple Adaptive Neuro-Fuzzy Inference System

A New Intelligent Approach for Mobile Robot Navigation

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