Abstract

Research in soft robot hardware has led to the development of platforms that allow for safer performance when working in uncertain or dynamic environments. The potential of these platforms is limited by the lack of proper dynamic models to describe or controllers to operate them. A common difficulty associated with these soft robots is a representation for torque, the common electromechanical relation seen in motors does not apply. In this thesis, several different torque models are presented and used to construct linear state-space models. The control limitations on soft robots are induced by natural compliance inherent to the hardware. This inherent compliance results in soft robots that are commonly underdamped and present significant oscillations when accelerated quickly. These oscillations can be mitigated through model-based controllers which can anticipate these oscillations. In this thesis, multiple model predictive controllers are implemented with the torque models produced and results are presented for an inflatable single-DoF pneumatically actuated soft robot. Larger, multi-DoF, soft robots present additional issues with control, where flexibility in one joint impacts control in others. In this thesis a preliminary method and results for controlling multiple joints on an inflatable multi-DoF pneumatically actuated soft robot are presented. While model predictive controllers are capable, their control commands are defined by solving an optimization constrained by model dynamics. This optimization relies on minimizing the cost of a user-defined objective function. This objective function contains a series of weights, which allow the user to tune the importance of each component in the objective function. As there are no calculations that can be performed to tune model predictive controllers to achieve superior control performance, they often need to be tuned tediously by a skilled operator. In this thesis, a method for automated discrete performance identification and model predictive controller weight tuning is presented. This thesis constructs multiple state-space models for single- and multi-DoF underdamped, antagonistic, pneumatically actuated soft robots and shows that these models can be used with model predictive control, tuned for performance, to achieve accurate joint position control.

Degree

MS

College and Department

Ira A. Fulton College of Engineering and Technology; Mechanical Engineering

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2016-08-01

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd8889

Keywords

inflatable robot, robotics, dynamic model, MPC, model predictive control, thesis

Share

COinS