Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance

This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The modified Elman neural network model is trained off-line and on-line stages to guarantee the outputs of the model accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results show the effectiveness of the proposed adaptive neural predictive control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances

A brief network analysis of Artificial Intelligence publication

In this paper, we present an illustration to the history of Artificial Intelligence(AI) with a statistical analysis of publish since 1940. We collected and mined through the IEEE publish data base to analysis the geological and chronological variance of the activeness of research in AI. The connections between different institutes are showed. The result shows that the leading community of AI research are mainly in the USA, China, the Europe and Japan. The key institutes, authors and the research hotspots are revealed. It is found that the research institutes in the fields like Data Mining, Computer Vision, Pattern Recognition and some other fields of Machine Learning are quite consistent, implying a strong interaction between the community of each field. It is also showed that the research of Electronic Engineering and Industrial or Commercial applications are very active in California. Japan is also publishing a lot of papers in robotics. Due to the limitation of data source, the result might be overly influenced by the number of published articles, which is to our best improved by applying network keynode analysis on the research community instead of merely count the number of publish.Comment: 18 pages, 7 figure

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

Nonlinear UGV Identification Methods via the Gaussian Process Regression Model for Control System Design

In this paper, two identification methods are proposed for a ground robotic system. A Gaussian process regression (GPR) model is presented and adopted for a system identification framework. Its performance and features were compared with a wavelet-based nonlinear autoregressive exogenous (NARX) model. Both algorithms were compared and experimentally validated for a small ground robot. Moreover, data were collected throughout the onboard sensors. The results show better prediction performance in the case of the GPR method, as an estimation algorithm and in providing a measure of uncertainty

Sliding Mode Control for Trajectory Tracking of a Non-holonomic Mobile Robot using Adaptive Neural Networks

In this work a sliding mode control method for a non-holonomic mobile robot using an adaptive neural network is proposed. Due to this property and restricted mobility, the trajectory tracking of this system has been one of the research topics for the last ten years. The proposed control structure combines a feedback linearization model, based on a nominal kinematic model, and a practical design that combines an indirect neural adaptation technique with sliding mode control to compensate for the dynamics of the robot. A neural sliding mode controller is used to approximate the equivalent control in the neighbourhood of the sliding manifold, using an online adaptation scheme. A sliding control is appended to ensure that the neural sliding mode control can achieve a stable closed-loop system for the trajectory-tracking control of a mobile robot with unknown non-linear dynamics. Also, the proposed control technique can reduce the steady-state error using the online adaptive neural network with sliding mode control; the design is based on Lyapunov’s theory. Experimental results show that the proposed method is effective in controlling mobile robots with large dynamic uncertaintiesFil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Optimal Trajectory Tracking Control for a Wheeled Mobile Robot Using Fractional Order PID Controller

يقدم هذا البحث, المتحكم التناسبي التكاملي التفاضلي الكسري الامثل اعتمادا على خوارزمية اسراب الطيور للسيطرة على تتبع المسار للانسان الالي ذو العجلات. حيث يتم تقليل مشكلة تتبع المسار مع إعطاء السرعة المرجعية المطلوبة للحصول على المسافة وانحراف زاوية يساوي الصفر، لتحقيق الهدف من تتبع المسار يتم استخدام اثنين من وحدات المتحكم التناسبي التكاملي التفاضلي الكسري للتحكم في السرعة والزاوية لتنفيذ سيطرة تتبع المسار.  تستخدم أساليب تخطيط وتتبع المسارات لإعطاء مسارات تتبع مختلفة. تم استخدام خوارزمية اسراب الطيور لإيجاد المعلمات المثلى لوحدات المتحكم التناسبي التكاملي التفاضلي الكسري. وتم محاكاة النماذج الحركية والحيوية للانسان الالي ذو العجلات لتتبع المسار المطلوب مع خوارزمية أسراب الطيور في برنامج المحاكاة  ماتلاب. وتبين نتائج المحاكاة أن  وحدات المتحكم التناسبي التكاملي التفاضلي الكسري الأمثل هي أكثر فعالية ولها أداء ديناميكي أفضل من الطرق التقليدية.This paper present an optimal Fractional Order PID (FOPID) controller based on Particle Swarm Optimization (PSO) for controlling the trajectory tracking of Wheeled Mobile Robot(WMR).The issue of trajectory tracking with given a desired reference velocity is minimized to get the distance and deviation angle equal to zero, to realize the objective of trajectory tracking a two FOPID controllers are used for velocity control and azimuth control to implement the trajectory tracking control. A path planning and path tracking methodologies are used to give different desired tracking trajectories.  PSO algorithm is using to find the optimal parameters of FOPID controllers. The kinematic and dynamic models of wheeled mobile robot for desired trajectory tracking with PSO algorithm are simulated in Simulink-Matlab. Simulation results show that the optimal FOPID controllers are more effective and has better dynamic performance than the conventional methods

Navigation control of an automated mobile robot robot using neural network technique

Over recent years, automated mobile robots play a crucial role in various navigation operations. For any mobile device, the capacity to explore in its surroundings is essential. Evading hazardous circumstances, for example, crashes and risky conditions (temperature, radiation, presentation to climate, and so on.) comes in the first place, yet in the event that the robot has a reason that identifies with particular places in its surroundings, it must discover those spots. There is an increment in examination here due to the requisition of mobile robots in a solving issues like investigating natural landscape and assets, transportation tasks, surveillance, or cleaning. We require great moving competencies and a well exactness for moving in a specified track in these requisitions. Notwithstanding, control of these navigation bots get to be exceptionally troublesome because of the exceedingly unsystematic and dynamic aspects of the surrounding world. The intelligent reply to this issue is the provision of sensors to study the earth. As neural networks (NNs) are described by adaptability and a fitness for managing non-linear problems, they are conceived to be useful when utilized on navigation robots. In this exploration our computerized reasoning framework is focused around neural network model for control of an Automated motion robot in eccentric and unsystematic nature. Hence the back propagation algorithm has been utilized for controlling the direction of the mobile robot when it experiences by an obstacle in the left, right and front directions. The recreation of the robot under different deterrent conditions is carried out utilizing Arduino which utilizes C programs for usage