In a free webinar to be hosted by Automotive World at 3pm BST (10am EST / 4pm CET) on 24th October, Asif Farooq and Peter Martin of Ricardo will provide an insight into the development of sophisticated vehicle powertrain control systems using a Model Predictive Control approach - saving significant development time and cost over conventional control and calibration methods and technology.
As automotive product line diversity grows together with the complexity and sophistication of each vehicle and powertrain combination, automakers are facing a very significant increase in the cost and engineering time required in the development and calibration of conventional Electronic Control Unit (ECU) based control systems.
In this 60-minute webinar, Asif Farooq, Senior Project Engineer, and Peter Martin, Technical Specialist - both of the Ricardo control and calibration team - will introduce Model Predictive Control (MPC). This approach embeds a model of the system being controlled into an ECU, which is used to predict system response. An online optimisation uses this model to find the best choice of control signal at each update, while operating within physical constraints found in any practical system.
Conventional controllers often require extensive calibration effort to achieve good performance within these limits. By contrast, MPC optimisation and constraint handling typically results in a shorter calibration process, with reduced development time and cost to achieve the same quality and performance thresholds.
Ricardo’s modelling approach helps to accelerate the development process by reducing complex plant models to simplified control-oriented models that can be deployed on an ECU. Control-oriented models are developed using system identification, physical equations or a combination of the two.
MPC can be applied to many systems within a vehicle, including engine control, thermal system control, cruise control, energy management and more. As vehicles become more connected, predictive controllers can also exploit available data on future operating conditions to further improve performance.
Additional insight will also be shared regarding Ricardo’s broad experience with MPC, describing where it is best to apply the technique and drawing on examples from a number of vehicle applications.