Analysis, Development and Evaluation of Electro-Hydrostatic Technology for Lower Limb Prostheses Applications

Tessari, Federico; Galluzzi, Renato; Tonoli, Andrea; Amati, Nicola; Laffranchi, Matteo; Michieli, Lorenzo De

doi:10.1109/iros45743.2020.9341512

Cited by 5 publications

(3 citation statements)

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“…A situation can be seen in [14], where the maximum power of the ankle was 552 W, but it cannot provide enough torque compared with the biomechanical data. The load profiles were also not matched in comparable active EHA prostheses [22,23].…”

Section: Discussionmentioning

confidence: 99%

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The Design and Testing of a PEA Powered Ankle Prosthesis Driven by EHA

Huang

2022

Biomimetics

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Several studies have shown that actuation concepts such as Serial elastic actuator (SEA) can reduce peak power and energy consumption in ankle prostheses. Proper selection and design of the actuation concepts is important to unlock the power source potential. In this work, the optimization design, mechanical design, control scheme, and bench experiments of a new powered ankle–foot prosthesis is proposed. The actuation concept of this prosthesis is parallel elastic actuator (PEA) composed of electro-hydrostatic actuator (EHA) as the power kernel and a unidirectional parallel spring as the auxiliary energy storage element. After the appropriate motor and transmission ratio was selected, a dynamic model of the PEA prosthesis was built to obtain the appropriate spring parameters driven by biological data. The design of the hydraulic and mechanical system and the controller were provided for the implementation of the designed system. Bench experiments were performed to verify the performance. The results showed that the designed prosthesis meets the biomechanical dynamics requirements. This result emphasizes the feasibility of the EHA as a power source and actuator and provides new ideas for the design of ankle–foot prostheses.

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Section: Discussionmentioning

confidence: 99%

“…The simulation results show that the accumulator can provide 150 W peak power. Tessari designed a prototype of the EHA knee prosthesis [ 23 ] and he demonstrated the potential of the EHA system for high efficiency.…”

Section: Introductionmentioning

confidence: 99%

The Design and Testing of a PEA Powered Ankle Prosthesis Driven by EHA

Huang

2022

Biomimetics

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“…Note that the EHSS has been widely used in the fields of medical equipment and defence industry that need high and strict requirements of the safety and control performance (e.g., exoskeleton robots for rehabilitation and missile launching systems), due to its powerful abilities of high power-to-weight ratio and fast and smooth response. 29,30 However, a large increase of unknown parameters, complex nonlinear and safety constraints need to be simultaneously dealt with in the EHSS control of these sophisticated fields, which is a difficult challenge for the classical control methods (e.g., PID controller and backstepping controller). Therefore, it's meaningful and reasonable to apply the proposed control method with powerful learning ability and safety to learn an optimal safety polity for the EHSS constrained control.…”

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confidence: 99%

Safe reward‐based deep reinforcement learning control for an electro‐hydraulic servo system

Liu

et al. 2022

Intl J Robust & Nonlinear

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In this article, a safe deep reinforcement learning (DRL) control method based on a safe reward shaping method is proposed and applied to the constrained control for an electro-hydraulic servo system (EHSS). The proposed control method improves the safety of the constrained control for a nonlinear system with the minimal intervention to the optimization of the performance objective, while the convergence speed of the DRL process has accelerated. By introducing control barrier functions (CBFs) to the reward shaping, a CBF-based potential difference term is designed to shape the safe reward, which not only provides the safe guidance for the DRL process by encoding the safety constraints of the nonlinear system, but also considers effects of the complex safety transformation on the convergence process in the DRL. Then the safe reward-based DRL control method is presented to learn the optimal safety policy of position tracking for the EHSS with position error constraints by planning and optimizing the safety together with the performance objective. Theoretical analysis is given to demonstrate that the proposed control method with the safe reward can achieve the optimal safety performance for the constrained control system.Experimental results of the constrained control for the EHSS with system uncertainties and perturbations are also exhibited, to show that the proposed control method converges fast and performs safer and better than the conventional control methods. K E Y W O R D Sconstrained control, control barrier function, deep reinforcement learning, electro-hydraulic servo system, safe reward shaping INTRODUCTIONDeep reinforcement learning (DRL) has been well used for nonlinear systems with uncertainties and complex dynamics, such as controlling robots 1-3 and self-driving vehicles, 4,5 because of its powerful ability to capture features from high-dimensional data and learn complicated control policies. 6 Several classical DRL algorithms including asynchronous advantage actor-critic (A3C), 7 deep Q-network, 8 and deep deterministic policy gradient (DDPG), 9 have been developedMinling Wu and Lijun Liu are co-first authors.

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