Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

[1]  A. Sedigh,et al.  Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints , 2018 .

[2]  Ying Zhang,et al.  Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks , 2017, Sensors.

[3]  Luc Steels,et al.  The artificial life route to artificial intelligence : building embodied , 1995 .

[4]  Kaspar Althoefer,et al.  Fuzzy Obstacle Avoidance and Navigation for Omnidirectional Mobile Robots , 2000 .

[5]  Salvatore Calcagno,et al.  Innovative Fuzzy Techniques for Characterizing Defects in Ultrasonic Nondestructive Evaluation , 2015 .

[6]  Antonio Jesús Yuste-Delgado,et al.  EUDFC - Enhanced Unequal Distributed Type-2 Fuzzy Clustering Algorithm , 2019, IEEE Sensors Journal.

[7]  Yuichi Motai,et al.  Human Behavior-Based Target Tracking With an Omni-Directional Thermal Camera , 2019, IEEE Transactions on Cognitive and Developmental Systems.

[8]  Viet-Thanh Pham,et al.  Robot Motion Planning in an Unknown Environment with Danger Space , 2019, Electronics.

[9]  Catalin Dumitrescu,et al.  Aircraft Trajectory Tracking Using Radar Equipment with Fuzzy Logic Algorithm , 2020, Mathematics.

[10]  M. V. Bobyr,et al.  Fuzzy control system of robot angular attitude , 2016, 2016 2nd International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).

[11]  Cosmin Copot,et al.  MIMO fuzzy control for autonomous mobile robot , 2015 .

[12]  Min Tan,et al.  Robust target tracking and following for a Mobile robot , 2018, Int. J. Robotics Autom..

[13]  Mohammad Javad Mahmoodabadi,et al.  An optimal adaptive robust PID controller subject to fuzzy rules and sliding modes for MIMO uncertain chaotic systems , 2017, Appl. Soft Comput..

[14]  Amitava Chatterjee,et al.  An adaptive fuzzy strategy for motion control of robot manipulators , 2005, Soft Comput..

[15]  Dayal R. Parhi,et al.  Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review , 2017, ICRA 2017.

[16]  George C. Karras,et al.  Model Predictive Fault Tolerant Control for Omni-directional Mobile Robots , 2020, J. Intell. Robotic Syst..

[17]  Francisco G. Rossomando,et al.  Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique , 2020 .

[18]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[19]  Roland Siegwart,et al.  Introduction to Autonomous Mobile Robots , 2004 .

[20]  Nikola Kasabov,et al.  Neuro-Fuzzy Techniques for Intelligent Information Systems , 1999 .

[21]  Chih-Hui Chiu,et al.  Design of Takagi-Sugeno Fuzzy Control Scheme for Real World System Control , 2019, Sustainability.

[22]  Tong Heng Lee,et al.  Design and Implementation of a Takagi–Sugeno-Type Fuzzy Logic Controller on a Two-Wheeled Mobile Robot , 2013, IEEE Transactions on Industrial Electronics.

[23]  Richa Sharma,et al.  Design of two-layered fractional order fuzzy logic controllers applied to robotic manipulator with variable payload , 2016, Appl. Soft Comput..

[24]  Alan MacLennan,et al.  The artificial life route to artificial intelligence: Building embodied, situated agents , 1996 .

[25]  R.M. Namal Bandara,et al.  Fuzzy logic controller design for an Unmanned Aerial Vehicle , 2016, 2016 IEEE International Conference on Information and Automation for Sustainability (ICIAfS).

[26]  Vishank Bhatia,et al.  Application of a novel fuzzy logic controller for a 5-DOF articulated anthropomorphic robot , 2015, 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN).

[27]  Mingming Lu,et al.  Design of Robust Adaptive Fuzzy Controller for a Class of Single-Input Single-Output (SISO) Uncertain Nonlinear Systems , 2020 .

[29]  Mohamed Boumehraz,et al.  Hybrid type-2 fuzzy logic obstacle avoidance system based on horn-schunck method , 2019 .

[30]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[31]  J.J. Jassbi,et al.  A Comparison of Mandani and Sugeno Inference Systems for a Space Fault Detection Application , 2006, 2006 World Automation Congress.

[32]  Oh,et al.  Application of Fuzzy Logic for Problems of Evaluating States of a Computing System , 2019, Applied Sciences.

[33]  Arturo de la Escalera,et al.  An Appearance-Based Tracking Algorithm for Aerial Search and Rescue Purposes † , 2019, Sensors.

[34]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[35]  寺野 寿郎,et al.  Fuzzy systems theory and its applications , 1992 .

[36]  Wen-June Wang,et al.  Design and Implementation of Fuzzy Control on a Two-Wheel Inverted Pendulum , 2011, IEEE Transactions on Industrial Electronics.

[37]  P. Ghanooni,et al.  Robust precise trajectory tracking of hybrid stepper motor using adaptive critic-based neuro-fuzzy controller , 2020, Comput. Electr. Eng..

[38]  Mohammad Mehdi Fateh,et al.  Model-free control of electrically driven robot manipulators using an extended state observer , 2020, Comput. Electr. Eng..

[39]  Xiaoqi Wang,et al.  Impedance with Finite-Time Control Scheme for Robot-Environment Interaction , 2020 .

[40]  Francisco Herrera,et al.  Linguistic decision analysis: steps for solving decision problems under linguistic information , 2000, Fuzzy Sets Syst..

[41]  Meng Wu,et al.  Fuzzy Controller for Autonomous Vehicle Based on Rough Sets , 2019, IEEE Access.

[42]  Soroush Sadeghnejad,et al.  Extended fuzzy logic controller for uncertain teleoperation system , 2016, 2016 4th International Conference on Robotics and Mechatronics (ICROM).

[43]  Larbi El Bakkali,et al.  Multi-input Multi-output Fuzzy Logic Controller for Complex System: Application on Two-links Manipulator , 2015 .