A Novel Intelligent Control Approach for Precise Tracking of Autonomous Robots

2009 ◽  
pp. 227-254
Author(s):  
Sukumar Kamalasadan ◽  
Adel A. Ghandakly
Keyword(s):  
Intelligent Control ◽  
Autonomous Robots ◽  
Control Approach

Control of Autonomous Robots Using the Principles of Neuromodulation

2013 ◽  
Author(s):  
Akimul Prince ◽  
Biswanath Samanta
Keyword(s):  
Neural Network ◽  
Autonomous Robots ◽  
Neuronal Model ◽  
Autonomous Robot ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Control Approach ◽  
The Neural Network ◽  
Simple Neural Network ◽  
Interesting Behavior

The paper presents a control approach based on vertebrate neuromodulation and its implementation on an autonomous robot platform. A simple neural network is used to model the neuromodulatory function for generating context based behavioral responses to sensory signals. The neural network incorporates three types of neurons — cholinergic and noradrenergic (ACh/NE) neurons for attention focusing and action selection, dopaminergic (DA) neurons for curiosity-seeking, and serotonergic (5-HT) neurons for risk aversion behavior. The implementation of the neuronal model on a relatively simple autonomous robot illustrates its interesting behavior adapting to changes in the environment. The integration of neuromodulation based robots in the study of human-robot interaction would be worth considering in future.

Neuromodulation Based Control of Autonomous Robots

2013 ◽  
Author(s):  
Akimul Prince ◽  
Biswanath Samanta
Keyword(s):  
Neural Network ◽  
Sensory Input ◽  
Autonomous Robots ◽  
Behavioral Responses ◽  
Neuronal Model ◽  
Robotic Platform ◽  
Control Approach ◽  
The Neural Network ◽  
Input Signals ◽  
Interesting Behavior

The paper presents a control approach based on neuromodulation in vertebrate brains and its implementation on an autonomous robotic platform. The neuromodulatory function is modeled through a neural network for generating context based behavioral responses to sensory input signals from the environment. Three types of neurons are incorporated in the neural network model. The neurons are — cholinergic and noradrenergic (ACh/NE) for attention focusing and action selection, dopaminergic (DA) for curiosity-seeking, and serotonergic (5-HT) for risk aversion behaviors. The neuronal model was implemented on a relatively simple autonomous robot that demonstrated its interesting behavior adapting to changes in the environment.

scholarly journals An Electro-Hydraulic Servo with Intelligent Control Strategy

2018 ◽  
Vol 150 ◽  
pp. 01016 ◽  
Author(s):  
Saeed Mohammed ◽  
Chong Chee Soon ◽  
Rozaimi Ghazali ◽  
Ahmad Anas Yusof ◽  
Yahaya Md Sam ◽  
...  
Keyword(s):  
Intelligent Control ◽  
Control Strategy ◽  
High Speed ◽  
Assessment Process ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Control Effort ◽  
Control Approach ◽  
Hydraulic Servo ◽  
Hybrid Fuzzy Pid

Versatile engineering applications have been developed to assist, reduce, and avoid human being from any heavy or harmful manufacturing processes. The gradually increased demand in force and position controls have simultaneously increased the usage of Electro-Hydraulic Servo (EHS) system. However, the time varying characteristics such as high-speed, outburst starting and stopping dynamic have led the EHS system to suffer from uncertainties and nonlinearities effects. Therefore, in order to enhance the performance of an EHS to surmount the uncertain and nonlinear effects, a hybrid Fuzzy-PID control strategy is developed which particularly improve the accuracy of the system by enhancing the control performance during the positioning tracking. By measuring the performance of the proposed control approach, the transient response and steady-state analysis will be performed which taking linear and intelligent control strategies as the references in the assessment process. The finding indicates the capability of a hybrid Fuzzy-PID controller in reducing the control effort applied to the EHS system.

scholarly journals Fault-tolerant scheme for robotic manipulator—Nonlinear robust back-stepping control with friction compensation

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256491
Author(s):  
Khurram Ali ◽  
Adeel Mehmood ◽  
Jamshed Iqbal
Keyword(s):  
Hybrid Control ◽  
Autonomous Robots ◽  
Fault Tolerant ◽  
Robot Manipulator ◽  
Robotic Manipulator ◽  
Friction Model ◽  
Fault Estimation ◽  
Friction Compensation ◽  
Control Law ◽  
Control Approach

Emerging applications of autonomous robots requiring stability and reliability cannot afford component failure to achieve operational objectives. Hence, identification and countermeasure of a fault is of utmost importance in mechatronics community. This research proposes a Fault-tolerant control (FTC) for a robot manipulator, which is based on a hybrid control scheme that uses an observer as well as a hardware redundancy strategy to improve the performance and efficiency in the presence of actuator and sensor faults. Considering a five Degree of Freedom (DoF) robotic manipulator, a dynamic LuGre friction model is derived which forms the basis for design of control law. For actuator’s and sensor’s FTC, an adaptive back-stepping methodology is used for fault estimation and the nominal control law is used for the controller reconfiguration and observer is designed. Fault detection is accomplished by comparing the actual and observed states, pursued by fault tolerant method using redundant sensors. The results affirm the effectiveness of the proposed FTC strategy with model-based friction compensation. Improved tracking performance as well robustness in the presence of friction and fault demonstrate the efficiency of the proposed control approach.

Intelligent control of DC motor driven mechanical systems: a robust learning control approach

2002 ◽  
Vol 13 (1) ◽  
pp. 71-90 ◽  
Author(s):  
Tae-Yong Kuc ◽  
Seung-Min Baek ◽  
Kyung-Oh Sohn ◽  
Jin-Oh Kim
Keyword(s):  
Intelligent Control ◽  
Mechanical Systems ◽  
Learning Control ◽  
Dc Motor ◽  
Control Approach ◽  
Robust Learning

scholarly journals Autonomous multirobot excavation for lunar applications

Robotica ◽  
2017 ◽  
Vol 35 (12) ◽  
pp. 2330-2362 ◽  
Author(s):  
Jekanthan Thangavelautham ◽  
Kenneth Law ◽  
Terence Fu ◽  
Nader Abu El Samid ◽  
Alexander D. S. Smith ◽  
...  
Keyword(s):  
Life Support ◽  
Autonomous Robots ◽  
Lunar Regolith ◽  
Three Dimensional ◽  
Field Testing ◽  
Radiation Shielding ◽  
Control Approach ◽  
Lunar Mission ◽  
Human Habitat ◽  
First Time

SUMMARYIn this paper, a control approach called Artificial Neural Tissue (ANT) is applied to multirobot excavation for lunar base preparation tasks including clearing landing pads and burying of habitat modules. We show for the first time, a team of autonomous robots excavating a terrain to match a given three-dimensional (3D) blueprint. Constructing mounds around landing pads will provide physical shielding from debris during launch/landing. Burying a human habitat modules under 0.5 m of lunar regolith is expected to provide both radiation shielding and maintain temperatures of −25 °C. This minimizes base life-support complexity and reduces launch mass. ANT is compelling for a lunar mission because it does not require a team of astronauts for excavation and it requires minimal supervision. The robot teams are shown to autonomously interpret blueprints, excavate and prepare sites for a lunar base. Because little pre-programmed knowledge is provided, the controllers discover creative techniques. ANT evolves techniques such as slot-dozing that would otherwise require excavation experts. This is critical in making an excavation mission feasible when it is prohibitively expensive to send astronauts. The controllers evolve elaborate negotiation behaviors to work in close quarters. These and other techniques such as concurrent evolution of the controller and team size are shown to tackle problem of antagonism, when too many robots interfere reducing the overall efficiency or worse, resulting in gridlock. Although many challenges remain with this technology, our work shows a compelling pathway for field testing this approach.

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