We provide control engineering consultancy services and training courses.
We can help you develop effective feedback control strategies and assess a whole process including effects on neighbouring/coupled systems and processes. Control objectives, disturbances, nonlinearities, uncertainties, robustness and noise immunity are all considered when a control strategy is developed. Solutions can range from simple classical feedback or feedforward to advanced control, such as model-based methods like MPC or methods like Fuzzy Logic control. However, simplicity is a key objective in our solutions, providing performance criteria can be met.
An in-depth understanding of control technologies together with first-hand experience in diverse industrial applications uniquely positions us to deliver exceptional value to our clients.
Many of our projects involve the development of high-fidelity and properly validated models, derived from first principles or plant data. This enables "what-if" questions to be asked and allows a new control solution to be compared against the existing to quantify benefits before deploying with confidence.
Our engineers utilise a wide variety of modelling packages such as MATLAB/Simulink, LabVIEW and CD&SIM Toolkit, and Hysys. We can also undertake re-coding of legacy models into new modelling packages, including model conversion from Simulink to LabVIEW.
Our in-house expertise comes to the fore when an embedded control, signal processing or data acquisition application needs to be turned into reality.
Designs can be implemented as real time applications using LabVIEW and CompactRIO. Our engineers follow proven software design steps of specification, design, implementation, test and deployment, and include software quality plans and extensive documentation.
We can undertake development of bespoke algorithms and tools for control or data processing to meet your company specific needs. Software can be developed in a variety of languages including C/C++, MATLAB, LabVIEW. As an NI Alliance Partner, ISC has staff certified in using LabVIEW.
As a broad generalisation it seems that directors of companies are keen to explore the benefits of artificial intelligence (AI), mainly because of the possibility of reducing staff numbers by exploiting the abundance of data the company gathers in more useful ways. It is very likely that the general management of a business can be advanced by using data more effectively. However, for control engineers the focus is more on improving the control of machinery or processes at a plant level. The question is therefore whether machine learning is likely to improve future systems.
ISC and NXP Semiconductors ran a project applying digital twin and machine learning technologies to the estimation of remaining useful lifetime and predictive maintenance of electric vehicles. A paper presenting the results of the project has been accepted by the IEEE 20th International Conference on Automation Science and Engineering (CASE).
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ISC is a member of a consortium developing the Dive Control Simulator (DCS). DCS is equipped with stunning, real-life looking graphics and dynamic simulation to train diving supervisors on various normal operations and emergency scenarios.
Diving supervisors are part of a dive support vessel crew, in charge of a diving operation. They monitor the operation and manage the panels controlling critical parameters (such as oxygen, pressure, temperature) for the divers to perform underwater tasks.
Our project partners are: PaleBlue (Stavanger, Norway) and Norsk Yrkesdykkerskole NYD (Oslo, Norway).
We were initially contracted to assess the performance attainable for a hydraulically actuated gangway, when compensating against boat motions. This involved modelling the various sources of errors (from sensing, actuation and control performance) to achieve the target performance whilst minimising cost and complexity. We were subsequently engaged to implement the entire control system, including MMI using touch panel computers, real-time control and I/O, solving kinematics, and extensive monitoring and safety logic. Full factory testing and Sea Trials were carried out and the system was installed and on active service.
This is a rolling programme of research undertaken for a major automotive company, to investigate the modelling, estimation and model based control techniques to enhance engine control systems while meeting the increasingly tight emission regulations. Methods explored include MPC (linear and nonlinear), hierarchical strategies and hybrid control of mixed variable problems (e.g. changing number of cylinders in use). Several of the developed schemes have been tested on real engine drive cycles.