An Adaptive Evolutionary Switching Control for Robot Manipulators

2005 ◽  
Author(s):  
P. R. Ouyang ◽  
W. J. Zhang ◽  
Madan M. Gupta
Keyword(s):  
Trajectory Tracking ◽  
Control Method ◽  
Robot Manipulators ◽  
Industrial Robot ◽  
Switching Control ◽  
Learning Ability ◽  
Pd Control ◽  
Control Approach ◽  
Gain Switching ◽  
Control Scheme

In this paper, a new adaptive switching control approach, called adaptive evolutionary switching PD control (AES-PD), is proposed for iterative operations of robot manipulators. The proposed AES-PD control method is a combination of the feedback of PD control with gain switching and feedforward using the input torque profile obtained from the previous iteration. The asymptotic convergence of the AES-PD control method is theoretically proved using Lyapunov’s method. The philosophy of the switching control strategy is interpreted in the context of the iteration domain to increase the speed of the convergence for trajectory tracking of robot manipulators. The AES-PD control has a simple control structure that makes it easily implemented. The validity of the proposed control scheme is demonstrated for the trajectory tracking of robot manipulators through simulation studies. Simulation results show that the AES-PD control can improve the tracking performance with an increase of the iteration number. The EAS-PD control method has the adaptive and learning ability; therefore, it should be very attractive to applications of industrial robot control.

Theory and Experiments on the Compliance Control of Redundant Robot Manipulators

1990 ◽  
Vol 112 (4) ◽  
pp. 653-660 ◽  
Author(s):  
H. Kazerooni ◽  
K. G. Bouklas ◽  
J. Guo
Keyword(s):  
Degrees Of Freedom ◽  
Control Method ◽  
Robot Manipulators ◽  
Industrial Robot ◽  
Redundant Robot ◽  
Control Approach ◽  
Six Degrees Of Freedom ◽  
The Stability ◽  
Control Methodology ◽  
Theory And Experiments

This work presents a control methodology for compliant motion in redundant robot manipulators. This control approach takes advantage of the redundancy in the robot’s degrees of freedom: while a maximum six degrees of freedom of the robot control the robot’s endpoint position, the remaining degrees of freedom impose an appropriate force on the environment. To verify the applicability of this control method, an active end-effector is mounted on an industrial robot to generate redundancy in the degrees of freedom. A set of experiments are described to demonstrate the use of this control method in constrained maneuvers. The stability of the robot and the environment is analyzed.

ICMA: AN EFFICIENT INTEGRATED CONGESTION CONTROL APPROACH

2019 ◽  
Vol 14 ◽  
Author(s):  
Tayyab Khan ◽  
Karan Singh ◽  
Kamlesh C. Purohit
Keyword(s):  
Congestion Control ◽  
Packet Loss ◽  
Control Method ◽  
Multicast Communication ◽  
Bandwidth Utilization ◽  
File Transfer ◽  
Control Approach ◽  
Communication Technique ◽  
Control Scheme ◽  
Multiparty Video Conferencing

Background: With the growing popularity of various group communication applications such as file transfer, multimedia events, distance learning, email distribution, multiparty video conferencing and teleconferencing, multicasting seems to be a useful tool for efficient multipoint data distribution. An efficient communication technique depends on the various parameters like processing speed, buffer storage, and amount of data flow between the nodes. If data exceeds beyond the capacity of a link or node, then it introduces congestion in the network. A series of multicast congestion control algorithms have been developed, but due to the heterogeneous network environment, these approaches do not respond nor reduce congestion quickly whenever network behavior changes. Objective: Multicasting is a robust and efficient one-to-many (1: M) group transmission (communication) technique to reduced communication cost, bandwidth consumption, processing time and delays with similar reliability (dependability) as of regular unicast. This patent presents a novel and comprehensive congestion control method known as integrated multicast congestion control approach (ICMA) to reduce packet loss. Methods: The proposed mechanism is based on leave-join and flow control mechanism along with proportional integrated and derivate (PID) controller to reduce packet loss, depending on the congestion status. In the proposed approach, Proportional integrated and derivate controller computes expected incoming rate at each router and feedback this rate to upstream routers of the multicast network to stabilize their local buffer occupancy. Results: Simulation results on NS-2 exhibit the immense performance of the proposed approach in terms of delay, throughput, bandwidth utilization, and packet loss than other existing methods. Conclusion: The proposed congestion control scheme provides better bandwidth utilization and throughput than other existing approaches. Moreover, we have discussed existing congestion control schemes with their research gaps. In the future, we are planning to explore the fairness and quality of service issue in multicast communication.

scholarly journals Adaptive PD Control Based on RBF Neural Network for a Wire-Driven Parallel Robot and Prototype Experiments

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Yuqi Wang ◽  
Qi Lin ◽  
Xiaoguang Wang ◽  
Fangui Zhou
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Approximation Error ◽  
Wind Tunnel Test ◽  
Parallel Robot ◽  
Network Control ◽  
Pd Control ◽  
Control Scheme

An adaptive PD control scheme is proposed for the support system of a wire-driven parallel robot (WDPR) used in a wind tunnel test. The control scheme combines a PD control and an adaptive control based on a radial basis function (RBF) neural network. The PD control is used to track the trajectory of the end effector of the WDPR. The experimental environment, the external disturbances, and other factors result in uncertainties of some parameters for the WDPR; therefore, the RBF neural network control method is used to approximate the parameters. An adaptive control algorithm is developed to reduce the approximation error and improve the robustness and control precision of the WDPR. It is demonstrated that the closed-loop system is stable based on the Lyapunov stability theory. The simulation results show that the proposed control scheme results in a good performance of the WDPR. The experimental results of the prototype experiments show that the WDPR operates on the desired trajectory; the proposed control method is correct and effective, and the experimental error is small and meets the requirements.

Computed Torque Control of a Caterpillar Robot Manipulator Using Neural Network

2016 ◽  
Vol 15 ◽  
pp. 106-118 ◽  
Author(s):  
Mehran Rahmani ◽  
Ahmad Ghanbari
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Control Method ◽  
Robot Manipulator ◽  
Setting Time ◽  
Torque Control ◽  
Learning Ability ◽  
Control Effort ◽  
Better Than ◽  
Computed Torque

This paper presents a neural computed torque controller, which employs to a Caterpillar robot manipulator. A description to exert a control method application neural network for nonlinear PD computed torque controller to a two sub-mechanisms Caterpillar robot manipulator. A nonlinear PD computed torque controller is obtained via utilizing a popular computed torque controller and using neural networks. The proposed controller has some advantages such as low control effort, high trajectory tracking and learning ability. The joint angles of two sub-mechanisms have been obtained by using the numerical simulations. The discovered figures show that the performance of the neural computed torque controller is better than a conventional computed torque controller in trajectory tracking and reduction of setting time. Finally, snapshots of gain sequences are demonstrated.

Combining Two Control Techniques for the Fast Movement of a Two-Wheel Mobile Robot

2015 ◽  
Vol 12 (02) ◽  
pp. 1550020 ◽  
Author(s):  
Sung Taek Cho ◽  
Seul Jung
Keyword(s):  
Control Method ◽  
Experimental Studies ◽  
Forward Direction ◽  
Control Performance ◽  
Pd Control ◽  
Control Approach ◽  
Control Techniques ◽  
Fast Movement ◽  
Control Fusion ◽  
Fusion Approach

Control of two-wheel mobile robots (TWMRs) is quite a challenging subject for researchers and educators. Control performance of TWMRs is to satisfy both stable balancing and position tracking simultaneously. When the TWMR is moving fast in forward direction with a proportional-derivative (PD) control method, fast movement to the desired position can be achieved. However, larger oscillations in both the balancing angle and position occur. The time-delayed control (TDC) method reduces the oscillation, but its response is relatively slow. The goal of this paper is to provide a solution to satisfy both stable balancing and position for fast forward movements. This paper presents a control fusion approach between a PD control method and a TDC method to make the performance better. Two controllers are fused together with different weighting factors on the basis of a sigmoidal function to satisfy the control performance. Experimental studies are conducted to validate the proposed control approach.

scholarly journals Neuro-PID Control for Electric Vehicle

2011 ◽  
Vol 15 (7) ◽  
pp. 846-853 ◽  
Author(s):  
Shigeru Omatu ◽  
◽  
Michifumi Yoshioka ◽  
Toru Fujinaka ◽  
◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Pid Control ◽  
Control Method ◽  
Torque Control ◽  
Learning Ability ◽  
Control Problems ◽  
Process Industries ◽  
The Neural Network ◽  
Control Scheme ◽  
Self Tuning

In this paper we consider the neuro-control method and its application to control problems of an electric vehicle. The neuro-control methods adopted here is based on Proportional-plus-Integral-plus-Derivative (PID) control, which has been adopted to solve process control or intelligent control problems. In Japan about eighty four percent of the process industries have used the PID control. After deriving the self-tuning PID control scheme (neuro-PID) using the learning ability of the neural network, we will show the control results by using the speed and torque control of an electric vehicle.

CONTROL OF CHAOS THROUGH AN INSTANTANEOUS LYAPUNOV EXPONENT TARGETING CONTROL ALGORITHM

2008 ◽  
Vol 18 (08) ◽  
pp. 2319-2344
Author(s):  
AHMED OTEAFY ◽  
MOHAMED ZRIBI ◽  
NEJIB SMAOUI
Keyword(s):  
Lyapunov Exponents ◽  
Chaotic Dynamics ◽  
Control Algorithm ◽  
Control Method ◽  
Chaotic Oscillator ◽  
Oscillator System ◽  
Control Approach ◽  
First Case ◽  
Control Scheme ◽  
The Lorenz System

This paper presents an approach to control the chaotic dynamics of discrete-time (or discretizable) systems. The objective of the paper is to focus on the suppression of the chaotic dynamics and the restoration of order with a state feedback controller. The proposed control method works by targeting instantaneous measures of the Lyapunov exponents of a system and setting them to desired values. At first, the paper presents an instantaneous measure of the Lyapunov exponents; this measure is used to control the system's dynamics. Then, the formulation of the control algorithm to suppress chaos is presented. Two cases for the control structure are considered. The first case corresponds to the case when the number of control inputs is equal to the number of states; the second case corresponds to the case when the number of control inputs is less than the number of states. The Lorenz system, the smooth Chua Oscillator system, the Rössler-hyperchaos system and a fourth order chaotic oscillator system are used as examples to illustrate the proposed control scheme. The simulation results show the efficacy of the proposed control approach.

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