The scope of the project was to control three actuators, throttle, wastegate, and variable valve timing (VVT). There are many multi variabel control strategies available, but only Model Predictive Control (MPC) was studied. MPC is an optimal control strategy which is able to consider constraints, such as physical limitations on actuators.
MPC requires a model of the system. To the left is a picture of the motor lab at the university. A model of the depicted engine was available, but didn't inlcude models of the VVT, which therefore had to be developed and implemented.
MPC consists of solving an optimal control problem. Development of the MPC controller consisted of defining the control problem, tuning parameters, setting constraints, ensuring stability etc.
One of the goals was to be able to control the real engine in the motor lab. In order to run the controller on the Electronic Control Unit, an optimization solver had to be developed.
In the bottom left figure, the simulation model of the combustion engine is given.
A litterture study was conducted, and several potential candidates were evaluted. A model based on a report from Michigan University and Bosch was choosen, since it was succesfully implemented on a turbo charged SI-engine.
Validation plots the VVT model is shown to the left, where data generated by the model and measurements from the actual engine cell are compared. As seen in the figure the model corresponds well with the measurements.
The final controller consists of an MPC, which uses a linearized engine model with five states. Due to model errors, a PI-controller was neccesary in order to achive desired reference tracking.
The MPC will control all actuators simultaneously, in order follow the reference trajectory of air mass flow, whilst also trying to minimize pressure losses and usage of turbo. The latter objectives are included in order to minimize fuel consumption.
MPC will solve an optimal control problem in each sample time. The problem can be formulated as a quadratic programming (QP) problem. The developed solver converts the QP-problem to an NNLS-problem, and then solves the NNLS-problem instead.
The developed solver can be compiled and used by the ECU in the motor lab, and it also shows great computational efficiency. To the left the developed solver is compared to Matlab's quadprog algorithm.
To the left, plots from a step response simulation are given. To the far left, the reference tracking can be seen, and the second plot shows the control signals for the three actuators. In the second plot it can be seen that the actuators are indeed controlled simultaneously, which isn't the case in most vechicles today.
Part of the project scope was also to evalute the controller's performance when one of the actuators are defective, i.e. cannot be moved. To the left, plots from a step response simulation are given, where the wastegate is fixed. It can be seen that the controller is still able to achieve satisfactory reference tracking.
Applied physics and electrical engineering,
Master in Control and Signal processing.
Mechanical engineering,
Master in Mechatronics.
Applied physics and electrical engineering,
Master in Control and Signal processing.
Applied physics and electrical engineering,
Master in Mechatronics.
Applied physics and electrical engineering,
Master in Mechatronics.
Mechanical engineering,
Master in Mechatronics.
Applied physics and electrical engineering,
Master in Control and Signal processing.
Mechanical engineering,
Master in Mechatronics.
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