Optimal hardware and control co-design applied to an active car suspension setup

For complex systems, it is not easy to obtain optimal designs for the hardware architecture and control configurations. Every design aspect influences the final performance, and often the interactions of the different components cannot be clearly determined in advance. In this work, a novel co-design optimization method was applied that allows the optimal placement and selection of actuators and sensors to be performed simultaneously with the determination of the control architecture and associated controller tuning parameters. This novel co-design method was applied to a state-space model of a downscaled active car suspension laboratory setup. This setup mimics a car driving over a specific road surface while active components in the suspension have to increase the driver’s comfort by counteracting unwanted vibrations. The result of this co-design optimization methodology is a Pareto front that graphically represents the trade-off between the maximum performance and the total implementation cost; the co-design results were validated with measurements of the physical active car suspension setup. The obtained controller tuning parameters are compared herein with existing controller tuning methods to demonstrate that the co-design method is able to determine optimal controller tuning parameters

Towards a generic optimal co-design of hardware architecture and control configuration for interacting subsystems

In plants consisting of multiple interacting subsystems, the decision on how to optimally select and place actuators and sensors and the accompanying question on how to control the overall plant is a challenging task. Since there is no theoretical framework describing the impact of sensor and actuator placement on performance, an optimization method exploring the possible configurations is introduced in this paper to find a trade-off between implementation cost and achievable performance. Moreover, a novel model-based procedure is presented to simultaneously co-design the optimal number, type and location of actuators and sensors and to determine the corresponding optimal control architecture and accompanying control parameters. This paper adds the optimization of the control architecture to the current state-of-the-art. As an optimization output, a Pareto front is presented, providing insights on the optimal total plant performance related to the hardware and control design implementation cost. The proposed algorithm is not focused on one particular application or a specific optimization problem, but is instead a generally applicable method and can be applied to a wide range of applications (e.g., mechatronic, electrical, thermal). In this paper, the co-design approach is validated on a mechanical setup

A Gradient-based Approach for Optimal Plant Controller Co-Design

This paper proposes a gradient-based iterative algorithm for optimal co-design of a linear physical plant and a controller. The proposed algorithm does not rely on the common linear parameterization assumption, and thus is applicable to a broader class of problems. The convergence of the algorithm and the verification procedure for a local minimum are given. Numerical examples show that our algorithm is comparable to other complicated algorithms in terms of the performance, but can deal with a more general class of problems