The control of autonomous vehicles has become an important topic and will result in significant societal changes. Far greater use of taxi or Uber type vehicles that are driven autonomously is predicted and significant effects on the heavy vehicle transport operations using techniques like platooning.
Optimal control solutions can range from simple classical feedback/feedforward to advanced control, such as model-based methods like MPC. In all cases, simplicity remains a key objective in our solutions, providing performance criteria can be met.
Both advanced linear and nonlinear MPC algorithms can be used for various aspects of the autonomous vehicle control problem. In addition there is also the role that artificial intelligence and machine learning might play.
Use of simulations enables us to evaluate features of algorithms, providing an effective approach to test prototype algorithms.
ISC has been contracted to study and evaluate possibilities given by advanced control algorithms to satisfy specifications and ensure physical, logical and safety constrains in autonomous vehicle control. By developing high-quality and realistic simulation system, ISC can test different methods to control autonomous vehicles in different critical operating conditions.
ISC provides independent, high quality control engineering consultancy and R & D services to the autonomous vehicle control and specialises in:
ISC engeers are experts in control and simulation systems development, and have worked in a wide range of projects studying application of modern control methods in autonomous vehicles.
ISC provides a wide set of training courses to the major automotive companies to introduce the last techniques to engineers and managers. Topics considered in previous training courses include:
We have powertrain control specific materials, including hands-on with simulations of engines where different methods of engine control can be studied to support our lectures.
We can adapt individual courses to suit your company's needs.
ISC has worked with a major US automotive client, researching the use of advanced linear and nonlinear predictive control algorithms for various aspects of the autonomous vehicle control problem. Lane merging, lane changing and junction crossing problems have been considered and both the tracking control and path planning algorithms have been produced.