Semiactive Virtual Control Method for Robots with Regenerative Energy-Storing Joints

Richter, Hanz; Simon, Daniel J.; Bogert, Antonie J. van den

doi:10.3182/20140824-6-za-1003.00332

Cited by 20 publications

(9 citation statements)

References 10 publications

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“…1. Human hip and thigh motion are emulated by a prosthesis test robot [13,14,35]. The vertical degree of freedom represents human vertical hip motion, the first rotational axis represents human angular thigh motion, and the second rotational axis represents prosthetic angular knee motion.…”

Section: Prosthetic Leg Modelmentioning

confidence: 99%

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Stable Robust Adaptive Impedance Control of a Prosthetic Leg

Azimi

Simon

Richter

2015

Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Cont

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We propose a nonlinear robust model reference adaptive impedance controller for an active prosthetic leg for transfemoral amputees. We use an adaptive control term to consider the uncertain parameters of the system, and a robust control term so the system trajectories converge to a sliding mode boundary layer and exhibit robustness to variations of ground reaction force (GRF). The boundary layer not only compromises between control chattering and tracking performance, but also bounds the parameter adaptation to prevent unfavorable parameter drift. We also prove the stability of the controller for the robotic system in the case of non-scalar boundary layer trajectories using Lyapunov stability theory and Barbalat’s lemma. The acceleration-free regressor form of the system removes the need to measure the joint accelerations, which would otherwise introduce noise in the system. We use particle swarm optimization (PSO) to optimize the design parameters of the controller and the adaptation law. The PSO cost function is comprised of control signal magnitudes and tracking errors. PSO achieves a 8% improvement in the objective function. Finally, we present simulation results to validate the effectiveness of the controller. We achieve good tracking of joint displacements and velocities for both nominal and perturbed values of the system parameters. Variations of ±30% on the system parameters result in an increase of the cost function by only 3%, which confirms the robustness of the controller.

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Section: Prosthetic Leg Modelmentioning

confidence: 99%

“…Researchers have recently concentrated on the design and control of these and other active prostheses [7][8][9][10][11]. Recent years have witnessed numerous advancements in control and modeling for prosthetic legs [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Stable Robust Adaptive Impedance Control of a Prosthetic Leg

Azimi

Simon

Richter

2015

Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Cont

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“…Furthermore, as with any system with finite on-board power storage, operation must be stopped once charge (indicated by V cap ) drops below an acceptable threshold and the system recharged. It is important to note that self-sustained operation or even charge buildup can occur, depending on system parameters and trajectories [4], [33], [34], [36].…”

Section: Semi-active Virtual Control Strategymentioning

confidence: 99%

“…The direct collocation method [35] is used to find the optimal trajectories. The optimal trajectories are then implemented on a PUMA 560 robot using a semiactive virtual control (SVC) strategy [4], [36] and a standard robust passivity based controller [37]. An experimental setup is prepared to evaluate the effectiveness of energy regeneration by measuring power flows at key locations.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Optimization of Robots With Regenerative Drive Systems: Numerical and Experimental Results

Khalaf

Richter

2020

IEEE Trans. Robot.

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We investigate energy-optimal control of robots with ultracapacitor based regenerative drive systems. Based on a previously introduced framework, a fairly generic model is considered for the robot and the drive system. An optimal control problem is formulated to find point-to point trajectories maximizing the amount of energy regenerated and stored in the capacitor. The optimization problem, its numerical solution and an experimental evaluation are demonstrated using a PUMA 560 manipulator. A comprehensive experimental setup was prepared to evaluate power flows and energy regeneration. Tracking of optimal trajectories was enforced on the robot using a standard robust passivity based control approach. Experimental results show that when following optimal trajectories, a reduction of about 13% in energy consumption can be achieved for the conditions of the study.

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“…3. Hardware addition: The addition of components for storing and recovering energy [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Robot Typementioning

confidence: 99%

A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems

2017

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Abstract:In the last decades, increasing energy prices and growing environmental awareness have driven engineers and scientists to find new solutions for reducing energy consumption in manufacturing. Although many processes of a high energy consumption (e.g., chemical, heating, etc.) are considered to have reached high levels of efficiency, this is not the case for many other industrial manufacturing activities. Indeed, this is the case for robotic and automatic systems, for which, in the past, the minimization of energy demand was not considered a design objective. The proper design and operation of industrial robots and automation systems represent a great opportunity for reducing energy consumption in the industry, for example, by the substitution with more efficient systems and the energy optimization of operation. This review paper classifies and analyses several methodologies and technologies that have been developed with the aim of providing a reference of existing methods, techniques and technologies for enhancing the energy performance of industrial robotic and mechatronic systems. Hardware and software methods, including several subcategories, are considered and compared, and emerging ideas and possible future perspectives are discussed.

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Semiactive Virtual Control Method for Robots with Regenerative Energy-Storing Joints

Cited by 20 publications

References 10 publications

Stable Robust Adaptive Impedance Control of a Prosthetic Leg

Stable Robust Adaptive Impedance Control of a Prosthetic Leg

Trajectory Optimization of Robots With Regenerative Drive Systems: Numerical and Experimental Results

A Review on Energy-Saving Optimization Methods for Robotic and Automatic Systems

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