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.
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.
ISC provides independent, high quality control engineering consultancy and R & D services to the autonomous vehicle control and specialises in:
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.
Over the years, ISC has delivered many training courses to Ford, Cummins, Chrysler, Toyota and Jaguar, ranging from introductory level courses for groups who need an awareness of control applied to engines to advanced control topics for research teams.
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.
We provide standard, industry specific and bespoke courses on a range of fundamental and advanced control engineering topics.
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.