Top PDF Implementation of Adaptive Neural Networks Controller for NXT SCARA Robot System

Implementation of Adaptive Neural Networks Controller for NXT SCARA Robot System

Implementation of Adaptive Neural Networks Controller for NXT SCARA Robot System

Nonlinear control system consumes an increasingly essential placement in the region of process control engineering [1]. Robotic Manipulators are widely used in different fields of industry: for the purpose of saving time, effort, and sometimes life.For these reasons and due to the vast applications of robotic manipulators, the design of controllers to optimize the tracking and speed performance of robots has become a necessity and an important research area [2]. Therefore, the simple P controller implemented in a dynamic model of flexible manipulator. (Adaptive, NN, and fuzzy logic have been used for tip position control, as they are very effective, but more complex and difficult to design and analyze) in [3]. Henceforth, intelligent control has a special cerebrate on Neuro-control that can be used successfully to fix problematic real control that is usually nonlinear, noisy and complex. Four degrees of freedom are controlled by neural networks with fuzzy controller for driving trajectory robot and handing processing with certain job [4].
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Trajectory Control of Robot Manipulators Using a Neural Network Controller

Trajectory Control of Robot Manipulators Using a Neural Network Controller

Many advanced methods proposed for control of robot manipulators are based on the dynamic models of the robot systems. Model-based control design needs a correct dynamic model and precise parameters of the system. Practically speaking, however, every dynamic model has some degrees of incorrectness and every parameter associates with some degrees of identification error. The incorrectness and errors eventually result positioning or trajectory tracking errors, and even cause the system to be unstable. In the past two decades, intensive research activities have been devoted on the design of robust control systems and adaptive control systems for the robot in order to overcome the control system drawback caused by the model errors and uncertain parameters, and a great number of research results have been reported, for example, (Hsia, 1989), (Kou, and Wang, 1989), (Slotine and Li, 1989), ( Spong, 1992), and (Cheah, Liu and Slotine, 2006). However, almost parts of results associate with complicated control system design approaches and difficulties in the control system implementation for industrial robot manipulators.
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A Neural Network Controller for Trajectory Control of Industrial Robot Manipulators

A Neural Network Controller for Trajectory Control of Industrial Robot Manipulators

For trajectory control of a robot manipulator, the model-based control design requires a correct dynamic model and precise parameters of the robot. Practically speaking, every dynamic model contains some degrees of incorrectness and every parameter associates with some degrees of error. The incorrectness and error eventually result positioning and/or trajectory tracking errors, and even cause instability of the system. In the past two decades, intensive research activities have been devoted on the design of robust control systems and adaptive control systems for the robot in order to overcome the control system drawback caused by the model errors and uncertain parameters, and a great number of research results have been reported, for example[1]~[5]. However, almost parts of them associate with complicated control system design approaches and difficulties in the control system implementation for industrial robot manipulators. Recent years, neural network technology attracts many attentions in the design of robot controllers. It has been
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Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot

Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot

To improve the applicability and performance of these robots, methods are being developed for setting parameter values in these networks. A major component of these methods focus on breaking the problem into several more easily solved subproblems. These subproblems are solved individually, and often in a specific order to build up the complexity of the network. Redbot uses a staged genetic algorithm process to set stepping frequency, gait, and finally joint angle profiles ( Russell et al., 2007 ). This controller, however, does not use sensory feedback, an important component for adaptive locomotion. The controller for MantisBot, and is formulated around steady state activity of the neural system, however, walking has not yet been demonstrated with this robot ( Szczecinski et al., 2015 ). In previous work, we developed a training process which utilizes many of the same tools as MantisBot ( Szczecinski et al., 2017 ) and sets parameter values in a locomotory network for forward locomotion of a rat simulation ( Hunt et al., 2015b ). In the work presented in this paper, we demonstrate the broader applicability of this process by applying the same procedure to a dog-like robot to generate adaptive, forward walking.
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Neural networks enhanced adaptive admittance control of optimized robot-environment interaction

Neural networks enhanced adaptive admittance control of optimized robot-environment interaction

Abstract—In this paper, an admittance adaptation method for robots to interact with unknown environment is developed. The environment to be interact with is modelled as a linear system in the state-space form. In the presence of the unknown environment dynamics, an observer in robot joint space is employed to estimate the interaction torque. Admittance control is adopted to regulate the dynamic behavior at the interaction point when the robot interacts with unknown environment. An adaptive neural controller using radial basis function is employed to guarantee trajectory tracking. A cost function is defined to achieve the interaction performance of torque regulation and trajectory tracking, which is minimised by adaptation of the admittance model. Simulation studies on a robot manipulator are carried out to verify the effectiveness of the proposed method.
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Design and Implementation for 3-DoF SCARA Robot based PLC

Design and Implementation for 3-DoF SCARA Robot based PLC

There are several literatures are available to present research, Pouyaand Kourosh,(2012) [5] presented, hypotheticl prototype model, simulated and optimized of a 3 DOF industrial SCARA robot with PID controller after optimization with Genetic algorithm. Mohammad and Mahdi ,(2012) [6]a trditional PD controller was used then a comparison method with a neural network controller system to reach the exact position control and motion characteristics of SCARA arm. Firas and Ahmed , (2013) [7] proposed a control system to the four degree of freedom (4DOF) Feedback Instruments IVAX SCARA robot manipulator using field programmable gate array(FPGA) kit to implemented the inverse kinematic calculations processing. Jian and Wei, (2013) [8] studied Four degrees of freedom SCARA robot kinematics modeling, using the Robotics Toolbox forward kinematics of the robot inverse kinematics simulation in the MATLAB environment. Through simulation the observed movement of each joint SCARA robotic state verification the proposed modeling was corrected to reach the desirable goal by enter the desirable joint angles, and the system output end of the robotic is to reach the space coordinates. Sudhaet al., (2013) [9] concentrated on the construction of a Robotic arm or a manipulator with SCARA configuration. The manipulator consists of two revolute joints are rotating joints which create rotation about their axis fitted with two DC Motor are interfaced with the ATMEGA 16 Microcontroller board using a L293D IC, and one prismatic joint. Yousif, (2013) [10] focused in this research on The SCARA Robot which had four degrees of freedom (DOFs), with three (shoulder, elbow, wrist) controlling by servo motors and one by pneumatics. It is realized by a 3D hypothetical reality (VR) modeling, which builds and receives
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Indirect Neural Adaptive Control for Wheeled Mobile Robot

Indirect Neural Adaptive Control for Wheeled Mobile Robot

Abstract: For a precise trajectory tracking of a wheeled mobile robot, accurate control of the position along a reference trajectory is essential. Therefore, this paper proposes an indirect neural adaptive controller for a nonholonomic mobile robot based on its dynamical model. This controller takes into account the approximation error. The use of the Lyapunov stability theorem and dynamical neural networks is indeed for deriving respectively stable learning laws for control and identification of a complex nonlinear dynamics system. The global tracking error is incorporated to adjust the neural weight learning laws to ensure the robustness of the system against approximation inaccuracy. Hence, the designed intelligent controller guarantees the convergence of both tracking and identification errors to zero. Simulation results illustrate the ability of the intelligent controller to assure the asymptotic stability of the closed-loop nonlinear uncertain system.
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Stable Adaptive Neural Control of a Robot Arm

Stable Adaptive Neural Control of a Robot Arm

In contrast to the above, the result presented in this paper avoids these drawbacks by establishing a stable adaptive control scheme based on the use of recurrent neural networks. In order to prove the industrial interest of the method for engineers, the application and valida- tion of IDNC have been applied to robot manipulator which is a nonlinear system, multivariable with time- varying and/or inaccurately-known parameters. The con- trolled process is considered as a “black box” whose op- erating model is completely unknown. The main advan- tage of the proposed method is that models of the sys- tems do not have to be known. Starting from zero values, weights, updating rates and time parameters of both adaptive instantaneous neural model and controller one, adapt themselves in order to track continuously the plant dynamics.
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Reinforcement Learning Adaptive PID Controller for an Under-actuated Robot Arm

Reinforcement Learning Adaptive PID Controller for an Under-actuated Robot Arm

Abstract: An adaptive PID controller is used to control of a two degrees of freedom under actuated manipulator. An actor-critic based reinforcement learning is employed for tuning of parameters of the adaptive PID controller. Reinforcement learning is an unsupervised scheme wherein no reference exists to which convergence of algorithm is anticipated. Thus, it is appropriate for real time applications. Controller structure and learning equations as well as update rules are provided. Simulations are performed in SIMULINK and performance of the controller is compared with NARMA-L2 controller. The results verified good performance of the controller in tracking and disturbance rejection tests.
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IX-NNN / NNC. Arm Length 120 mm / 150 mm / 180 mm. Ultra Compact SCARA Robot Ultra Compact Cleanroom SCARA Robot

IX-NNN / NNC. Arm Length 120 mm / 150 mm / 180 mm. Ultra Compact SCARA Robot Ultra Compact Cleanroom SCARA Robot

This unit converts regenerative current produced when the motor decelerates, into heat. You need one or more regenerative units according to the total output of single-axis motors connected to the controller. (No regenerative unit is required for SCARA robots.) Refer to the table at right for the rough guideline on how to determine if your system needs a regenerative unit(s).

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Implementation of Artificial Neural Network Training Data in Micro-Controller Based Embedded System

Implementation of Artificial Neural Network Training Data in Micro-Controller Based Embedded System

Matlab still produces the test patterns and then sends them via the serial port to the microcontroller. This simulates data the microcontroller would gain from another source like the analog to digital converter. Once the embedded network has all of the inputs it needs it then does the neural network forward calculations and produces one or more outputs. These outputs would typically drive an external source such as a pulse width modulator, but in this instance are transmitted back to the PC via the serial port. This allows the user to send and receive data from the microcontroller in real time. In addition the user can verify the hardware calculations and the amount of time being required. The data can easily be verified by using Matlab’s graphing tools. This mode can be used for embedded networks using assembly or C. The difference is the format of the test data being sent and received, but both operate under the same principle. This is the final step before the microcontroller is configured to operate
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A New Approach to Implementation of an Open Architecture Controller for a PUMA Robot

A New Approach to Implementation of an Open Architecture Controller for a PUMA Robot

The Unimate PUMA 500 series Robots mainly uses DEC LSI 11 processor running VAL robot control software [1]. Methods of bypassing VAL are discussed in literature, including Unimation technical reports [2] [3]. However, most of these procedures have been confined to replacing the LSI 11 with another DEC computer, leaving peripheral hardware intact. In fact, it is far more cost effective to develop new hardware using less specific interfaces. The shift towards personal computer open architecture robot controller and the impact of using these newer controllers for system integration is discussed in [4]. An improved PC- based design for Puma robot was presented in [5], but this hardware configuration purely depends on in-house built
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Robot Calibration Using Artificial Neural Networks

Robot Calibration Using Artificial Neural Networks

Generally kinematic model-based calibration is considered as a global calibration method which improves robot accuracy across the whole volume of robot space, while non-parame[r]

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Smartphone based robot controller for teleoperated system

Smartphone based robot controller for teleoperated system

DC type motor is selected due to its convenience control, clean, quiet and the most popular type in mobile robot design. . The model is formed by quadrature Hall Effect encoder board which is designed to fit on the rear shaft of the DC motor as shown in the previous figure. Two Hall Effect sensors are placed 90° apart to sense and produce two outputs signal named A and B which is; 90° out of phase and allowing the direction of rotation to be determined. For further understanding, the tables show the state diagram while the figure shows the waveform signal that will be produced by the encoder; based on the rotational movement of the DC motor.
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System Development Of Parallel Robot With Industrial Controller

System Development Of Parallel Robot With Industrial Controller

Rodney Brooks, the director of the Massachusetts Institute of Technology computer science and artificial intelligence laboratory, state a robot is something that has some physical effect on the world, but it does it based on how it senses the world and how the world changes around it. Gregory Dudek, the director of the Centre for Intelligent Machines at McGill University in Montreal, sets three criteria for robots, they have to have a way of making measurements of the world, and they have to have a way of making decisions. In other words, something like a computer, you could call that thinking informally and they have to have a way taking actions. For Joseph Engelberger has been called the father of robotics said that "I can't define a robot, but I know one when I see one."
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GPU-based implementation of real-time system for spiking neural networks

GPU-based implementation of real-time system for spiking neural networks

Implementation exhibits good scalability and is targeted for large network simulations with a large number of synaptic connections. Simulation accuracy plays a secondary role. As a consequence, the 1 st order Euler integration technique is applied. As it is known, Euler method is prone to be numerically unstable with slow converging error, and as a rule, is not often used in scientific computations. However, it depends on application and its requirements to the error tolerance. The choice of synchronous system allows exploiting synaptic parallelism in this case because spike events are tight to the time grid and can be processed in parallel. At the same time, a clock-driven system introduces quantization error due to the time grid. Warp divergence is minimized using buffering technique: tasks are serialized; however, each task is computed using parallel domain decomposition. Thus, thread utilization for computation of each task is maximized.
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Intelligent Robot Based on Synaptic Plasticity
and Neural Networks

Intelligent Robot Based on Synaptic Plasticity and Neural Networks

A very simple example of learning at the organism level which has been worked out a neural level (Fig 3.) is that of sensitization of the gill withdrawal reflex in Alyssa. In the sea mollusk aplysia, a light touch to the animal's siphon results in gill withdrawal. Hebbian learning rule: The formulation of associative learning that has gathered the most attention for those studying the brain was due to Donald Hebb (see quote above). This proposition has led to a number of mathematical rules, the simplest of which is:

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Adaptive Neural Network Controller for Modeling Link Quality in WSANs

Adaptive Neural Network Controller for Modeling Link Quality in WSANs

There are essentially three elements in the WSAN architecture: sensors, actuators and base station (sometimes refereed as controller). The role of each element has been discussed previously. There are two schemes to build the network, automated and semi automated [3]. Figure.1 shows the architecture of semi automated which will be used to implement the approach of the MRANNC. In this scheme, there is no explicit controller in the WSANs. The controller and the actuator A are connected directly. The controller receives the measured data of the output from the sensor S. After doing some processing, the signal is sent to the actuator to drive the system according to some specified algorithm. The error signal is given as
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Adaptive dynamic programming-based controller with admittance adaptation for robot–environment interaction

Adaptive dynamic programming-based controller with admittance adaptation for robot–environment interaction

In admittance control system, force and the admittance model are two important parts. When robot–environment interaction exists, the force can be detected and measured by the sensors installed on the end-effector of the robot arm. But, how to derive optimal parameters of the admittance model is non-trivial. On the one hand, it is usually difficult to derive the desired admittance model because of the com- plexity of environmental dynamics; on the other hand, a fixed admittance model cannot satisfy all cases. Taking human–robot cooperation as an example, variable admit- tance control is necessary to ensure more efficient perfor- mance. 11 To solve these problems, iterative learning has been studied in robot intelligent control area. It has been investigated to obtain admittance parameters to adapt to unknown environment. The aim of this approach is to intro- duce human learning skills into the robot and improve con- trol performance by repeating a task. Cohen and Flash 12 proposed an impedance learning control scheme using an associative search network to complete a wall-following work. Neural network (NN) is introduced into the impedance control to regulate the parameters. 13 However, the iterative learning method requires the robot to operate repeatedly, which brings inconvenience in practical process and is not feasible in many situations. Love and Book, 14 Uemura and Kawamura, 15 Gribovskaya et al., 16 Stanisic and Fern´an- dez, 17 Landi et al. 18 and Yao et al. 19 have proposed to utilize adaptation approaches to address the problems stated above. Robotic motion control is a challenging task as it is difficult to obtain accurate model concerning that the robot is a non-linear and highly coupled system. Proportional– integral–derivative (PID) control, NN control, adaptive control and other control methods have been applied to the robot system. 20–27 As a classical control method, PID con- trol is employed to the robot system and can track the given reference trajectory well. 28 It is acknowledged that PID control has some advantages, such as simple structure and good robustness, but it is not easy to select suitable PID parameters if the controlled plant is complex. In addition, when dynamic uncertainties exist in the system, PID con- trol cannot satisfy the performance requirements for the magnitude of overshoot, the rising and settling time and so on. NN has the fundamental characteristics of human brain and can simulate human behaviour for information processing, therefore it is widely used in the control field
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Force control for a 3 finger adaptive robot gripper by using PID controller

Force control for a 3 finger adaptive robot gripper by using PID controller

In the earlier research, position control has been widely used over force control to produce fast, accurate and repeatable motion. Moreover, position control works best in a well- organized and controlled work space because the controlled robots operate repeatedly in the same working area. Typical examples of tasks can be found in the automation manufacturing industry such as polishing, deburring, machining and assembly. However, position control will not suffice to extend the application of the robot outside of the controlled working environment. The use of pure position control can result in fluctuation of the contact force ultimately leading to dangerous behaviors such as breakage or instability. Therefore, many compliance controls for robotic hands were introduced by researchers in order to replicate safe human grasping during the interaction. Compliance control can be defined as a measure of the ability of a manipulator to react to interaction forces [1]. One way to attain compliance control is via an active compliance control by devising a suitable interaction control strategy. Force control is the key element of active compliance control. In force control, the desired force trajectory is commanded, and force is measured in real time to realize the feedback control [1].
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