We provide control engineering consultancy and fundamental and advanced control engineering courses.
We help you develop effective feedback control strategies and assess the 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.
Simplicity is a key objective in our solutions, providing performance criteria can be met. However, our solutions can range from simple classical feedback or feedforward, more advanced model-based methods like MPC, to incorporating into the control strategy the latest methods from the field of Artificial Intelligence (AI) and Machine Learning (ML).
AI can be used in control applications to enable analysis of big data for better informed management actions or initiatives. It can also be used to enhance control or signal processing systems at the machine or plant level. It offers opportunities to develop new adaptive controllers, filters, and condition monitoring tools that compensate for uncertainties in system or signal characteristics.
Our deep understanding of control technologies and diverse industrial experience uniquely positions us to deliver exceptional value to our clients.
We develop high-fidelity models or digital twins from first principles or plant data. This allows us to explore "what-if" scenarios and compare new control solutions against existing ones, ensuring deployment with confidence. High-fidelity models are valuable for improved control, prediction, and condition monitoring.
Our engineers use various modelling packages including MATLAB/Simulink, LabVIEW and CD&SIM Toolkit, and Hysys. We can also convert legacy models to new packages.
We excel in turning embedded control, signal processing, or data acquisition application into reality.
We can implement designs as real time applications using LabVIEW and CompactRIO, following proven software design steps of specification, design, implementation, test and deployment and include quality plans and extensive documentation.
We can develop bespoke algorithms and tools for control or data processing in languages including MATLAB, LabVIEW, Python, and C/C++. As an NI Alliance Partner, ISC has staff certified in using LabVIEW.
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ISC was a member of a consortium developing the Dive Control Simulator (DCS). DCS was 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 were: 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.