Method, control apparatus and powertrain system controller for real-time, self-learning control based on individual operating style
View Patent ↗Method, control apparatus and powertrain system controller are provided for real-time, self-learning control based on individual operating style. The method calibrates powertrain system performance in a passenger vehicle in real-time based on individual operating style. The method includes powering the vehicle with the system and generating a sequence of system operating point transitions based on an operating style of an operator of the vehicle during the step of powering. The method further includes learning a set of optimum values of controllable system variables in real-time during the steps of powering and generating based on the sequence of system operating point transitions and predetermined performance criteria for the system. The method still further includes generating control signals based on the set of optimum values to control operation of the system.
1. A powertrain system controller comprising:
a processor; and
a storage device having processor-readable code embodied thereon for programming the processor to perform a method comprising:
generating a sequence of system operating point transitions based on an operating style of an operator of a drivable passenger vehicle while the system is powering the vehicle and while the operator is driving the vehicle, wherein the operating style is defined by at least one of an accelerator pedal position and a brake torque timing;
learning a set of predictive optimum values of controllable system variables in real-time during the step of generating based on the sequence of system operating point transitions and predetermined engine performance criteria for the system; and
generating control signals based on the set of optimum values to control operation of the system.
2. The controller as claimed in claim 1 , wherein the step of learning includes the step of learning first and second controllable system variables of the set in parallel phases.
3. The controller as claimed in claim 1 , wherein the storage device contains a processor-readable base set of system operating points and corresponding values of the controllable system variables wherein the step of learning is also based on the base set.
4. The controller as claimed in claim 1 , wherein the step of learning is at least partially performed utilizing a decentralized learning control algorithm.
5. The controller as claimed in claim 1 , wherein the step of learning is at least partially performed utilizing a predictive optimal stochastic control algorithm.
6. The controller as claimed in claim 1 , wherein the system includes an internal combustion engine and wherein the vehicle is one of a land traversing vehicle, watercraft and aircraft.
7. The controller as claimed in claim 1 , wherein the step of learning is at least partially performed utilizing a lookahead algorithm.