Model Reference Predictive Adaptive Control for Large-Scale Soft Robots

Hyatt, Phillip; Johnson, Christine; Killpack, Marc D.

doi:10.3389/frobt.2020.558027

Cited by 21 publications

(15 citation statements)

References 40 publications

(48 reference statements)

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“…A different approach uses an energy-shaping approach to develop the control law for a soft continuum manipulator [120]. MPC is another technique that was employed for the control of largescale soft robots [121]. The MPC algorithm relies on a PCC model alongside a kinematic representation for efficient state prediction.…”

Section: Controlmentioning

confidence: 99%

Underwater Soft Robotics: A Review of Bioinspiration in Design, Actuation, Modeling, and Control

et al. 2022

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Nature and biological creatures are some of the main sources of inspiration for humans. Engineers have aspired to emulate these natural systems. As rigid systems become increasingly limited in their capabilities to perform complex tasks and adapt to their environment like living creatures, the need for soft systems has become more prominent due to the similar complex, compliant, and flexible characteristics they share with intelligent natural systems. This review provides an overview of the recent developments in the soft robotics field, with a focus on the underwater application frontier.

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Section: Controlmentioning

confidence: 99%

Underwater Soft Robotics: A Review of Bioinspiration in Design, Actuation, Modeling, and Control

et al. 2022

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“…An analytical equation of motion of the form shown in Equation (1) can be derived using principles of Lagrangian mechanics by modeling the joint as an infinite set of infinitesimally thin disks and integrating along the length of a piecewise constant curvature (PCC) arc. This method was developed in Hyatt et al ( 2020a ), which includes a detailed derivation of this model.…”

Section: First Principles and Deep Learningmentioning

confidence: 99%

“…While previous work (Hyatt et al, 2020a ) demonstrated that this formulation of the dynamic model was accurate enough for model-based control, improvements are needed in order to control soft robots in uncertain environments or during highly dynamic movements. Certainly, further system identification would improve this model; however, because of the complexities and uncertainties inherent in soft robots and the processes to manufacture them, system identification techniques scale poorly with high degree-of-freedom systems and do not necessarily generalize well between platforms.…”

Section: First Principles and Deep Learningmentioning

confidence: 99%

“…What is most impressive in this case is that by incorporating the combined DNN with NEMPC, we achieve very low steady-state error with no integral control at all. All of our prior work (and most of the soft robot control literature) has required some sort of integral or adaptive control to compensate for this steady-state error (see Hyatt et al, 2020a for an example of model-reference adaptive control (MRAC) which essentially exhibits integral action to achieve low steady-state error).…”

Section: Controlmentioning

confidence: 99%

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Using First Principles for Deep Learning and Model-Based Control of Soft Robots

et al. 2021

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Model-based optimal control of soft robots may enable compliant, underdamped platforms to operate in a repeatable fashion and effectively accomplish tasks that are otherwise impossible for soft robots. Unfortunately, developing accurate analytical dynamic models for soft robots is time-consuming, difficult, and error-prone. Deep learning presents an alternative modeling approach that only requires a time history of system inputs and system states, which can be easily measured or estimated. However, fully relying on empirical or learned models involves collecting large amounts of representative data from a soft robot in order to model the complex state space–a task which may not be feasible in many situations. Furthermore, the exclusive use of empirical models for model-based control can be dangerous if the model does not generalize well. To address these challenges, we propose a hybrid modeling approach that combines machine learning methods with an existing first-principles model in order to improve overall performance for a sampling-based non-linear model predictive controller. We validate this approach on a soft robot platform and demonstrate that performance improves by 52% on average when employing the combined model.

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“…An approach based on sliding mode and fuzzy control is applied in [29] for a rotary actuator with elastic chambers. Model predictive control based on learned nonlinear models as applied in [30] can also be combined with ideas from adaptive control to compensate for model deviations, as presented in [31]. The authors of [32] use reinforcement learning to control a system that is jointly actuated with tendons and pneumatic actuators.…”

Section: Related Workmentioning

confidence: 99%

Design and Control of an Inflatable Spherical Robotic Arm for Pick and Place Applications

2021

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We present an inflatable soft robotic arm made of fabric that leverages state-of-the-art manufacturing techniques, leading to a robust and reliable manipulator. Three bellow-type actuators are used to control two rotational degrees of freedom, as well as the joint stiffness that is coupled to a longitudinal elongation of the movable link used to grasp objects. The design is motivated by a safety analysis based on first principles. It shows that the interaction forces during an unexpected collision are primarily caused by the attached payload mass, but can be reduced by a lightweight design of the robot arm. A control allocation strategy is employed that simplifies the modeling and control of the robot arm and we show that a particular property of the allocation strategy ensures equal usage of the actuators and valves. The modeling and control approach systematically incorporates the effect of changing joint stiffness and the presence of a payload mass. An investigation of the valve flow capacity reveals that a proper timescale separation between the pressure and arm dynamics is only given for sufficient flow capacity. Otherwise, the applied cascaded control approach can introduce oscillatory behavior, degrading the overall control performance. A closed form feed forward strategy is derived that compensates errors induced by the longitudinal elongation of the movable link and allows the realization of different object manipulation applications. In one of the applications, the robot arm hands an object over to a human, emphasizing the safety aspect of the soft robotic system. Thereby, the intrinsic compliance of the robot arm is leveraged to detect the time when the robot should release the object.

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Model Reference Predictive Adaptive Control for Large-Scale Soft Robots

Cited by 21 publications

References 40 publications

Underwater Soft Robotics: A Review of Bioinspiration in Design, Actuation, Modeling, and Control

Underwater Soft Robotics: A Review of Bioinspiration in Design, Actuation, Modeling, and Control

Using First Principles for Deep Learning and Model-Based Control of Soft Robots

Design and Control of an Inflatable Spherical Robotic Arm for Pick and Place Applications

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