Trajectory tracking control of a group of cooperative planar space robot systems

Dalla, Vijay Kumar; Pathak, Pushparaj Mani

doi:10.1177/0959651815605016

Cited by 7 publications

(8 citation statements)

References 15 publications

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“…It preserves power (effort 3 flow) and transmits the factor of power with proper scaling; hence, it is known as the transformer modulus, which is denoted as m. A transformer relates effort-to-effort and flow-to-flow. [28][29][30][31] By using equations (4) and 5, the bond-graph model is developed (Appendix 1). The Jacobian and controller designs are discussed further in Appendix 1.…”

Section: Kinematic Analytical Modelmentioning

confidence: 99%

Power-optimized motion planning of reconfigured redundant space robot

Dalla

Pathak

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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Main advantage of redundant space manipulator is that it can be configured differently by actuating various joints. Space manipulators are designed to be deployed in harsh surroundings, which in turn would increase the possibility of manipulator failure. Thus, the enhancement of the capabilities of the robots and the failures thereafter are a major concern. This article presents three aspects of hyper-redundant space robot. First, the control strategies based on minimum power criterion with all healthy joints are proposed. Second, hyper-redundancy is put upon to reconfigure the space robot with the failure of locked joints. Third, the optimization of workplace trajectory of the manipulator is obtained by genetic algorithm and a minimum power criterion has been adopted in all three cases. The bond-graph technique is employed for robot modeling with the use of SYMBOLS Shakti software, which generates system equations in C++ code. This code is subsequently converted into a MATLAB program. The result obtained through simulation shows that 37% of the total power consumption was reduced by the actuators through an optimization approach.

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Section: Kinematic Analytical Modelmentioning

confidence: 99%

Power-optimized motion planning of reconfigured redundant space robot

Dalla

Pathak

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…The power bond along with causal stroke affords dual information (i.e., it gives the information of flow and effort (analogous to the velocity and force in the mechanical domain) at the two different ends). Bond graph modeling of robotic arms and mobile robots has been gaining interest among many robotics researchers [17][18][19] due to its algorithmic nature in deriving system dynamic equations and its physical modeling nature.…”

Section: Bond Graph Modelingmentioning

confidence: 99%

“…Two control strategies, namely only manipulator arm control (OMAC) and simultaneous manipulator and base control (SMBC) have been proposed in this work. PID control coupled with amnesia recovery 18 is implemented in both these strategies.…”

Section: Control Strategymentioning

confidence: 99%

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Trajectory control of a mobile manipulator in the presence of base disturbance

2018

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A mobile manipulator is typically an assembly of a mobile robot base and an on-board manipulator arm. As the manipulator arm is mounted over the mobile robot base, the controller has the additional task of taking care of the disturbances of the mobile robot due to the dynamic interactions between the mobile robot base and manipulator arm. In the present work, dynamic models for the manipulator arm and an omni-wheeled mobile robot base were developed separately and then both were combined. Two control strategies, namely only manipulator arm control (OMAC) and simultaneous manipulator and base control (SMBC) were developed for the effective control of tip trajectory. In both strategies, an amnesia recovery coupled with classical proportional integral and derivative (PID) control was used. The bond graph methodology was used for the development of the dynamic model and control for the mobile manipulator. Simulation results are presented to illustrate the efficacy of the two control strategies.

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“…4 A lot of research on the trajectory tracking control has been done in the past two decades. 5 Hayakawa et al 6 proposed a novel path tracking method aiming to maintain steering smoothness from high to low vehicle speed, and the multiple look-ahead points are introduced, which allows to guidance the vehicle with minimum steering corrections. However, this control law has poor robustness to the variations of cornering stiffness.…”

Section: Introductionmentioning

confidence: 99%

Adaptive coordinated collision avoidance control of autonomous ground vehicles

Guo

Luo

2018

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article presents a novel coordinated nonlinear adaptive backstepping collision avoidance control strategy for autonomous ground vehicles with uncertain and unmodeled terms. A nonlinear vehicle collision avoidance vehicle model which describes the coupled lateral and longitudinal dynamic features of autonomous ground vehicles is constructed. Then, a modified artificial potential field approach which can ensure that the total potential field of the target is goal minimum, is proposed to produce a collision-free trajectory for autonomous ground vehicles in real-time. Furthermore, in order to handle with the features of coupled and parameter uncertainties of autonomous ground vehicles, an adaptive neural network-based backstepping trajectory tracking control approach is proposed for collision avoidance control system of autonomous ground vehicles, and the stability of this proposed control system is proven by the Lyapunov theory. Finally, the co-simulation and experimental tests are implemented and the results show that the proposed collision avoidance control strategy has excellent tracking performance.

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Trajectory tracking control of a group of cooperative planar space robot systems

Cited by 7 publications

References 15 publications

Power-optimized motion planning of reconfigured redundant space robot

Power-optimized motion planning of reconfigured redundant space robot

Trajectory control of a mobile manipulator in the presence of base disturbance

Adaptive coordinated collision avoidance control of autonomous ground vehicles

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