Black-Box System Identification for Low-Cost Quadrotor Attitude at Hovering

The accuracy of dynamic modelling of unmanned aerial vehicles, specifically quadrotors, is gaining importance since strict conditionalities are imposed on rotorcraft control. The system identification plays a crucial role as an effective approach for the problem of the fine-tuning dynamic models for applications such control system design and as handling quality evaluation. This paper focuses on black-box identification, describing the quadrotor dynamics based on experimental setup through sensor preparation for data collection, modelling, control design, and verification stages

Mobile robot visual navigation based on fuzzy logic and optical flow approaches

This paper presents the design of mobile robot visual navigation system in indoor environment based on fuzzy logic controllers (FLC) and optical flow (OF) approach. The proposed control system contains two Takagi–Sugeno fuzzy logic controllers for obstacle avoidance and goal seeking based on video acquisition and image processing algorithm. The first steering controller uses OF values calculated by Horn–Schunck algorithm to detect and estimate the positions of the obstacles. To extract information about the environment, the image is divided into two parts. The second FLC is used to guide the robot to the direction of the final destination. The efficiency of the proposed approach is verified in simulation using Visual Reality Toolbox. Simulation results demonstrate that the visual based control system allows autonomous navigation without any collision with obstacles.Peer ReviewedPostprint (author's final draft

Vision Based Tracking and Interception of Moving Target by Mobile Robot Using Fuzzy Control

This paper presents a simple Fuzzy Logic Controllers (FLC) based control strategy to solve the tracking and interception problem of a moving target by a mobile robot equipped with a pan-tilt camera. Before sending commands to the mobile robot, video acquisition and image processing techniques are employed to estimate the target’s position in the image plane. The estimate coordinates are used by a fuzzy logic controller to control the pan-tilt camera angles. The objective is to ensure that the moving target is always at the middle of the camera image plane. A second FLC is used to control the robot orientation and to guarantee the tracking and interception of the target. The proposed pan-tilt camera and robot orientation controllers’ efficiency has been validated by simulation under Matlab using Virtual Reality Toolbox

Gyro-Accelerometer based Control of an Intelligent Wheelchair

This paper presents a free-hand interface to control an electric wheelchair using the head gesture for people with severe disabilities i.e. multiple sclerosis, quadriplegic patients and old age people. The patient head acceleration and rotation rate are used to control the intelligent wheelchair. The patient head gesture is detected using accelerometer and gyroscope sensors embedded on a single board MPU6050. The MEMS sensors outputs are combined using Kalman filter as sensor fusion to build a high accurate orientation sensor. The system uses an Arduino mega as microcontroller to perform data processing, sensor fusion and joystick emulation to control the intelligent wheelchair and HC-SR04 ultrasonic sensors to provide safe navigation.The wheelchair can be controlled using two modes. In the first mode, the wheelchair is controlled by the usual joystick. In the second mode, the patient uses his head motion to control the wheelchair. The principal advantage of the proposed approach is that the switching between the two control modes is soft, straightforward and transparent to the user

Hybrid type-2 fuzzy logic obstacle avoidance system based on horn-schunck method

This paper is concerned with a visual navigation method based on type-2 fuzzy logic controllers (T2FLC) and optical flow (OF) approach. A Takagi-Sugeno fuzzy logic controller is used for obstacle avoidance task based on video acquisition and image processing algorithm. To extract information about the environment, the captured image is divided into two parts, the control system uses optical flow values calculated by a Horn-Shunk algorithm to detect and estimate the positions of obstacles. The efficiency of the proposed structure is simulated using Visual Reality Toolbox. The obtained simulation results demonstrate the effectiveness of this autonomous visual navigation systemPeer ReviewedPostprint (author's final draft

Hybrid type-2 fuzzy logic obstacle avoidance system based on horn-schunck method

This paper is concerned with a visual navigation method based on type-2 fuzzy logic controllers (T2FLC) and optical flow (OF) approach. A Takagi-Sugeno fuzzy logic controller is used for obstacle avoidance task based on video acquisition and image processing algorithm. To extract information about the environment, the captured image is divided into two parts, the control system uses optical flow values calculated by a Horn-Shunk algorithm to detect and estimate the positions of obstacles. The efficiency of the proposed structure is simulated using Visual Reality Toolbox. The obtained simulation results demonstrate the effectiveness of this autonomous visual navigation system

Hybrid type-2 fuzzy logic obstacle avoidance system based on horn-schunck method

This paper is concerned with a visual navigation method based on type-2 fuzzy logic controllers (T2FLC) and optical flow (OF) approach. A Takagi-Sugeno fuzzy logic controller is used for obstacle avoidance task based on video acquisition and image processing algorithm. To extract information about the environment, the captured image is divided into two parts, the control system uses optical flow values calculated by a Horn-Shunk algorithm to detect and estimate the positions of obstacles. The efficiency of the proposed structure is simulated using Visual Reality Toolbox. The obtained simulation results demonstrate the effectiveness of this autonomous visual navigation systemPeer Reviewe

RELIABILITY MODELING BASED ON INCOMPLETE DATA: OIL PUMP APPLICATION

The reliability analysis for industrial maintenance is now increasingly demanded by the industrialists in the world. Indeed, the modern manufacturing facilities are equipped by data acquisition and monitoring system, these systems generates a large volume of data. These data can be used to infer future decisions affecting the health facilities. These data can be used to infer future decisions affecting the state of the exploited equipment. However, in most practical cases the data used in reliability modelling are incomplete or not reliable. In this context, to analyze the reliability of an oil pump, this work proposes to examine and treat the incomplete, incorrect or aberrant data to the reliability modeling of an oil pump. The objective of this paper is to propose a suitable methodology for replacing the incomplete data using a regression method

VIBRATIONS DETECTION IN INDUSTRIAL PUMPS BASED ON SPECTRAL ANALYSIS TO INCREASE THEIR EFFICIENCY

Spectral analysis is the key tool for the study of vibration signals in rotating machinery. In this work, the vibration analy-sis applied for conditional preventive maintenance of such machines is proposed, as part of resolved problems related to vibration detection on the organs of these machines. The vibration signal of a centrifugal pump was treated to mount the benefits of the approach proposed. The obtained results present the signal estimation of a pump vibration using Fourier transform technique compared by the spectral analysis methods based on Prony approach

FUZZY INFERENCE SYSTEMS OPTIMIZATION BY REINFORCEMENT LEARNING

Fuzzy rules for control can be effectively tuned via reinforcement learning. Reinforcement learning is a weak learningmethod wich only requires information on the succes or failure of the control application. In this paper a reinforcementlearning method is used to tune on line the conclusion part of fuzzy inference system rules. The fuzzy rules are tuned inorder to maximize the return function . To illustrate its effectivness, the learning method is applied to the well knownCart-Pole balancing system problem. The results obtained show significant improvements of the speed of learning