"At the core of our control project successes is the
skillful modelling and simulation of the behaviour of the underlying systems
to fit a specific purpose, based on first principles or plant data."
We can provide nonlinear dynamic models and simulations of mechanical, hydraulic, electrical, combustion, process, and chemical systems for many industrial applications in various modelling and simulation tools, including MATLAB/Simulink, LabVIEW and Control Design and Simulation (CD&SIM) module, HYSYS, MATRIXx, and ADAMS.
Systems of every type and their operating conditions are subject to variations, such as changes in products (new operating points etc.) and raw material sources, plant modifications, wear and tear of actuators (e.g. valves and drives), and degradation of measurement sensors.
It is impossible to test the control systems for every possible condition prior to deployment. Dynamic modelling and simulation thus play an important role in control systems development:
One vital aspect of using models and simulations is that their usefulness depends greatly on the quality of the models which should be neither too simple nor too complex but fit for purpose. The judgement on this however comes from the experience of those who build the models.
For 25 years, we have modelled and simulated a diversity of systems and processes to aid the troubleshooting of existing control systems and the development of effective control strategies and shortening of the design to deployment cycle.
Our engineers have amassed rich experience in building well validated nonlinear dynamic models of mechanical, hydraulic, electrical, combustion, process, and chemical systems for applications from marine, power generation, automotive, to oil and gas and other process industries.
The heave, pitch and roll motions were compensated in the algorithm to enable safe transfer of personnel in the Offshore Wind Turbine Access system. Developed in MATLAB, the algorithm was implemented using LabVIEW and cRIO hardware.
A bespoke algorithm was developed and analysed for the online compensation of sensor nonlinearities. As well as accuracy and computational burden, data diversity issues were paramount in this safety critical application.)