Say we have a vehicle (a car or a boat or a helicopter or an airplane etc) which reacts to control inputs from a driver/pilots in a particular way defined by its current state and control input.
Assume that we have a car travelling on flat road. Our control input would be a vector of Ctrl_Input = [Steering Angle, Gear, Accelerator Value, Brake Value] and current state would be a vector of Current_State = [Velocity X, Velocity Y, Position X, Position Y, Acceleration X, Acceleration Y] the next state of the vehicle after a small time increment "dt" would be Next_State = [Velocity X, Velocity Y, Position X, Position Y, Acceleration X, Acceleration Y]
In another word we have a system with following input and output
Inputs: Ctrl_Inputs Current_State Output: (obtained after "dt" time) Next_State
Assume that we have as a set of Ctrl_Inputs, Current_State and Next_State data obtained in an experiment inwhich a human driver driving the vehicle on a flat road.
Given these data sets we should be able to train a neural network so that it relate inputs to outputs. My questions are as follows; 1- Which neural network topology would be good for learning vehicle behaviour (dynamic model of the vehicle) from above mentioned set of data ? 2- Number of neurons and number of layers required? 3- If we know our current state and our desired state (next state) how we can use the neural network to find required Control_Input vector? 4- Is there any tutorial/demo/code of this kind ? 5- Thanks.