Simultaneous Optimal System and Controller Design for Multibody Systems with Joint Friction using Direct Sensitivities

TL;DR

This paper presents a gradient-based optimization method for multibody systems with joint friction, enabling simultaneous optimal design and control, validated through numerical case studies using a new Julia-based software implementation.

Contribution

It introduces a direct sensitivity approach for joint friction modeling in multibody system optimization, supporting co-design of system and controller.

Findings

Effective gradient computation for systems with friction.

Successful application to three case studies.

Enhanced software implementation in Julia.

Abstract

Real-world multibody systems are often subject to phenomena like friction, joint clearances, and external events. These phenomena can significantly impact the optimal design of the system and its controller. This work addresses the gradient-based optimization methodology for multibody dynamic systems with joint friction using a direct sensitivity approach for gradient computation. After a thorough review of various friction models developed over the years, the Brown McPhee model has been found to be the most suitable for the study due to its accuracy for dynamic simulation and its compatibility with sensitivity analysis. The methodology supports co-design of the system and its controller, which is especially relevant for applications like robotics and servo-mechanical systems where the actuation and the design are highly dependent on each other. Numerical results are obtained using a new implementation of the MBSVT (Multi-Body Systems at Virginia Tech) software package; MBSVT 2.0 is reprogrammed in Julia for ease of implementation while maintaining high computational efficiency. Three case studies are provided to demonstrate the attractive properties of simultaneous optimal design and control approach for certain applications.