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.

In recent years, more and more attention has been paid to the simultaneous optimization of hardware and control to achieve an optimal design of complex and interacting systems. To efficiently carry out this co-design optimization, there is a need for flexible methods to apply the variable hardware and control co-design aspects while also allowing a faster system response calculation compared to traditional methods. These time savings in the response calculations are of significant importance when using iterative optimization algorithms that typically require a large number of simulations to arrive at a solution.That is why this paper proposes a general methodology to create a closed-loop state-space model consisting of an open-loop process with an observer and extensive control loop structures. These structures comprise a combination of cascaded decentralized and distributed controllers, synchronizing controllers, and feedforward controllers while taking into account reference trajectories and input disturbances. It is shown that with the proposed methodology, response calculations for a motion application are much faster compared to traditional graphical programming tools that enable to apply flexible control architecture features. This shows that the presented methodology permits the efficient co-design optimization of hardware en control aspects.

This paper presents an automatic model based predictive control tuning and operation of a hydrostat drive train system under nominal conditions. An industrial-friendly version of the predictive control strategy is applied whereas the model is simplified as an approximation of first order plus dead time and the control design parameters are automatically tuned. Implementation on a SISO drive train is experimentally achieved with successful performance in closed loop tests. From the identified characteristics of the real life drive train, a simulated MIMO system is extracted and automatic tuning is again performed. A limitation of the present work is that constraints are not yet included, only safety limits.