scholarly journals Adaptive Control of Space Robot Despinning Tumbling Target Using Flexible Brushes

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shengxin Sun ◽  
Cheng Wei ◽  
Zhuoran Huang ◽  
Hao Wu ◽  
Haibo Zhang ◽  
...  
Keyword(s):  
Optimal Control ◽  
Adaptive Control ◽  
Rotation Speed ◽  
Degree Of Freedom ◽  
Robotic Arm ◽  
Dynamics Model ◽  
Absolute Nodal Coordinate ◽  
Coordinate Method ◽  
Global Robustness ◽  
Tumbling Target

A flexible brush mechanism is designed and mounted at the end of a seven-degree-of-freedom robotic arm to despin a tumbling target. The dynamics model of the flexible brush is established using the absolute nodal coordinate method (ANCF), and its contact collision with the solar wing of the tumbling target is analysed. The H ∞ optimal control is proposed for a seven-degree-of-freedom robotic arm during despinning of a tumbling target while ensuring the global robustness and stability. Simulations verify that the despinning strategy can successfully eliminate the rotation speed and is feasible and effective.

Robotic Arm Movement Optimization Using Soft Computing

2017 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Surender Kumar ◽  
Kavita Rani ◽  
V. K. Banga
Keyword(s):  
Optimal Control ◽  
Genetic Algorithms ◽  
Soft Computing ◽  
Target Position ◽  
Arm Movement ◽  
Degree Of Freedom ◽  
Robotic Arm ◽  
Robot Arm ◽  
Soft Computing Techniques ◽  
Simulation Time

<p class="Text">Robots are commonly used in industries due to their versatility and efficiency. Most of them operating in that stage of the manufacturing process where the maximum of robot arm movement is utilized. Therefore, the robots arm movement optimization by using several techniques is a main focus for many researchers as well as manufacturer. The robot arm optimization is This paper proposes an approach to optimal control for movement and trajectory planning of a various degree of freedom in robot using soft computing techniques. Also evaluated and show comparative analysis of various degree of freedom in robotic arm to compensate the uncertainties like movement, friction and settling time in robotic arm movement. Before optimization, requires to understand the robot's arm movement i.e. its kinematics behavior. With the help of genetic algorithms and the model joints, the robotic arm movement is optimized. The results of robotic arm movement is optimal at all possible input values, reaches the target position within the simulation time.</p>

scholarly journals Stability and Manoeuvrability Simulation of a Semi-Autonomous Submarine Free-Running Model SUBOFF with an Autopilot System

2021 ◽  
Vol 11 (1) ◽  
pp. 410
Author(s):  
Yu-Hsien Lin ◽  
Yu-Ting Lin ◽  
Yen-Jun Chiu
Keyword(s):  
Optimal Control ◽  
Experimental Tests ◽  
Degree Of Freedom ◽  
Line Of Sight ◽  
Pd Controller ◽  
Free Running ◽  
Simulation Results ◽  
Guidance Algorithm ◽  
Logic Operations ◽  
Six Degree Of Freedom

On the basis of a full-appendage DARPA SUBOFF model (DTRC model 5470), a scale (λ = 0.535) semi-autonomous submarine free-running model (SFRM) was designed for testing its manoeuvrability and stability in the constrained water. Prior to the experimental tests of the SFRM, a six-degree-of-freedom (6-DOF) manoeuvre model with an autopilot system was developed by using logic operations in MATLAB. The SFRM’s attitude and its trim polygon were presented by coping with the changes in mass and trimming moment. By adopting a series of manoeuvring tests in empty tanks, the performances of the SFRM were introduced in cases of three sailing speeds. In addition, the PD controller was established by considering the simulation results of these manoeuvring tests. The optimal control gains with respect to each manoeuvring test can be calculated by using the PID tuner in MATLAB. Two sets of control gains derived from the optimal characteristics parameters were compared in order to decide on the most appropriate PD controller with the line-of-sight (LOS) guidance algorithm for the SFRM in the autopilot simulation. Eventually, the simulated trajectories and course angles of the SFRM would be illustrated in the post-processor based on the Cinema 4D modelling.

Discrete-Time LQR and H 2 Damping Control of Flexible Payloads Using a Robot Manipulator With a Wrist-Mounted Force/Torque Sensor

2000 ◽  
Author(s):  
Chunhao Joseph Lee ◽  
Constantinos Mavroidis
Keyword(s):  
Optimal Control ◽  
Discrete Time ◽  
Robot Manipulator ◽  
Control Design ◽  
Degree Of Freedom ◽  
Flexible Beam ◽  
Torque Sensor ◽  
Damping Control ◽  
Control Feedback ◽  
Six Degree Of Freedom

Abstract This paper presents robust and optimal control methods to suppress vibrations of flexible payloads carried by robotic systems. A new improved estimator in discrete-time H2 optimal control design based on the Kalman Filter predictor form is developed here. Two control design methods using state-space models, LQR and H2 Optimal Design, in discrete-time domain are applied and compared. The manipulator joint encoders and the wrist-mounted six-degree-of-freedom force/torque sensor provide the control feedback. A complete dynamic model of the robot/payload system is taken into account to synthesize the controllers. Experimental verifications of both methods are performed using a Mitsubishi five-degree-of-freedom robot manipulator that carries a flexible beam. It is shown that both methods damp out the vibrations of the payload very effectively.

Adaptive Control of a Sliding Seat Using Magnetorheological Energy Absorbers

2010 ◽  
Author(s):  
Min Mao ◽  
Norman M. Wereley ◽  
Alan L. Browne
Keyword(s):  
Adaptive Control ◽  
Degrees Of Freedom ◽  
Adaptive Controller ◽  
Degree Of Freedom ◽  
Lumped Mass ◽  
Adaptive Controllers ◽  
Bingham Number ◽  
Control Objective ◽  
Payload Mass ◽  
Energy Absorbers

Feasibility of a sliding seat utilizing adaptive control of a magnetorheological (MR) energy absorber (MREA) to minimize loads imparted to a payload mass in a ground vehicle for frontal impact speeds as high as 7 m/s (15.7 mph) is investigated. The crash pulse for a given impact speed was assumed to be a rectangular deceleration pulse having a prescribed magnitude and duration. The adaptive control objective is to bring the payload (occupant plus seat) mass to a stop using the available stroke, while simultaneously accommodating changes in impact velocity and occupant mass ranging from a 5th percentile female to a 95th percentile male. The payload is first treated as a single-degree-of-freedom (SDOF) rigid lumped mass, and two adaptive control algorithms are developed: (1) constant Bingham number control, and (2) constant force control. To explore the effects of occupant compliance on adaptive controller performance, a multi-degree-of-freedom (MDOF) lumped mass biodynamic occupant model was integrated with the seat mass. The same controllers were used for both the SDOF and MDOF cases based on SDOF controller analysis because the biodynamic degrees of freedom are neither controllable nor observable. The designed adaptive controllers successfully controlled load-stroke profiles to bring payload mass to rest in the available stroke and reduced payload decelerations. Analysis showed extensive coupling between the seat structures and occupant biodynamic response, although minor adjustments to the control gains enabled full use of the available stroke.

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