Result
Pilot Systems engineers designed a standardized System Identification process to build linear plant models of dynamic systems using measurements from the system’s physical test bench input and output data. Using our System Identification Method, the modeling activity that previously required months of effort for our client was reduced to days or weeks. In turn, this helped our client significantly accelerate product development.
The Challenge
Reduce the time and effort our client required to develop and verify complex dynamic system plant models, often with uncertain or unknown model parameters, used to develop control algorithms.
Our client used conventional modeling techniques including finite element modelling and multibody dynamics to develop mathematical plant models of their complex dynamic systems. While this process gave a first principle understanding of the behavior of the system dynamics, it was time consuming due to uncertainty in parameter values. This created a bottleneck in the control algorithm development activity. To help our client accelerate their development process, we introduced them to System Identification Methods.
System Identification Methods allowed Pilot to build mathematical plant models of dynamic systems based on measured data. We adjust parameters within a given model until its output coincides with measured output data. We use either time-domain or frequency-domain input/output data to estimate continuous-time transfer function process and state-space models as needed.
Using our detailed knowledge of system identification methods and MathWorks toolboxes, Pilot developed this standardized process of building mathematical plant models of dynamic systems and we generated accurate high-order plant models. These models were used successfully to develop control algorithms. The performance of the controllers was evaluated in the laboratory (HIL testing) using dSPACE hardware and software. Results of closed-loop HIL testing revealed that our system identification process for building mathematical plant models significantly reduced the time required to develop control algorithms. By using our system identification process, our client can now accelerate their product development activities.
Areas of expertise
MATLAB, Simulink, MATLAB scripts, System Identification Toolbox, dSPACE hardware, dSPACE software, Signal Processing