AU2023208068B1 - Machine learning based optimal control system for magnetic continuous variable transmission - Google Patents
Machine learning based optimal control system for magnetic continuous variable transmission Download PDFInfo
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- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
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Abstract
A machine learning based optimal control system used to optimally control a system of
magnetic variable transmission system for multiple sources of inputs and multiple outputs of
mechanical power is developed.
Description
[0001] It is related to the fields of:
(1) Machine Learning, Optimization, Control Engineering, Mechanical Engineering.
1.1 Contents of a Machine Learning Based Optimal Control System for Magnetic Continuous Variable Transmission (MLBOCS for M-CVT)
The Machine Learning Based Optimal Control System for Magnetic Continuous Variable Transmission (MLBOCS for M-CVT) is particularly appropriate for harvesting wind or wave energy as power supplied by waves or winds is variable in a wide range.
The MLBOCS for M-CVT basically contains the followings:
(1) A Magnetic Continuous Variable Transmission (M-CVT). The M-CVT has three rotors: the Mechanical Power Input Rotor (MPIR), the Electrical Power Input Rotor (EPIR) and the Mechanical Power Output Rotor (MPOR). Of the three rotors (the MPIR, the MPOR and the EPIR), there are two rotors called the First Pole Pair Rotor and the Second Pole Pair Rotor. Each of the First Pole Pair Rotor and the Second Pole Pair Rotor has a number of pole pairs. The number of pole pairs of the First Pole Pair Rotor and the number of pole pairs of the Second Pole Pair Rotor should be different. The last rotor is called the Pole Piece Rotor. The Pole Piece Rotor has a number of ferromagnetic pole pieces. The number of ferromagnetic pole pieces of the Pole Piece Rotor equals to the total number of pole pairs of both the First Pole Pair Rotorand the Second Pole Pair Rotor. In addition, gaps between the MPIR, the EPIR and the MPOR are small enough in order to create the best interactions between magnetic fields of the MPIR, the EPIR and the MPOR. The EPIR also has a number or coils which make the EPIR working either as an electric motor or a generator. When working as a motor, the EPIR uses a source of electrical power which is converted to mechanical powerfor rotatingthe EPIR. Then the mechanical power of the EPIR combines with the input mechanical power being transmitted through the MPIR. The combined mechanical power isthen outputted bythe MPOR at desired (variable) rotational speeds. The EPIR is controllable and has a role of changing gear ratios of the M-CVT. Whenever rotational speeds of the MPOR need to be increased, the EPIR works as an electric motor using extra electric power supplied. Otherwise, if the input mechanical power of the M-CVT is excessed, the
EPIR works as a generator to convert the excessed mechanical energy to electricity
while it maintains the MPOR rotating at desired (variable) rotational speeds.
(2) A Machine Learning Based Optimal Control System (MLBOCS).
1.2 The Machine Learning Based Optimal Control System (MLBOCS)
[0011] The Machine Learning Based Optimal Control System (MLBOCS) is used to
control the M-CVT optimally in order to obtain optimal power or energy output. The
MLBOCS integrates a Deep Learning Optimization Algorithm (DLOA) which is used to
solve the optimization problem for obtaining optimal solutions. The optimization
problem, of which the Objective Function is the total power or energy output of the M
CVT, is the Mathematical Model of the M-CVT. The combination of both the
optimization problem (the Mathematical Model) and a Deep Learning based algorithm,
which is the Deep Learning Optimization Algorithm (DLOA), to solve the optimization
problem using Artificial Neural Network (ANN) in order to obtain optimal solutions with
regards to time steps, is called the Numerical Model of the M-CVT. The MLBOCS
composes of the Numerical Model of the M-CVT and Controllers which require Control
Parameters derived from the optimal solutions of the optimization problem (the
Mathematical Model). As a result, poweroutputs of the M-CVT are optimally controlled.
[0012] States of the M-CVT are called the M-CVT-States which are expressed by a set of
time history M-CVT-State-Data. The M-CVT-State-Data are obtained from a system of
M-CVT-State-Sensors which compose the following systems of Sensors and related
components of the M-CVT:
(1) The Mechanical Power Input Rotor (MPIR) which has:
(a) a system of MPIR-State-Sensors used to collect the MPIR-State-Data. The system
of MPIR-State-Sensors has:
1. a system of MPIR-Torque-Sensors used to monitor torques of the MPIR.
II. a system of MPIR-Speed-Sensors used to monitor rotational speeds of the
III. an optional system of MPIR-Accelerators used to monitor rotational
accelerations of the MPIR.
IV. A system of MPIR-Pulse-State-Sensors used to monitor MPIR-Pulse-States
which express states of the MPIR whether it is pulsed.
(b) a set of time history MPIR-State-Data obtained from the MPIR-State-Sensors.
The MPIR-State-Data has:
1. a set of time history MPIR-Torque-Data obtained from the system of MPIR
Torque-Sensors.
II. a set of time history MPIR-Speed-Data obtained from the system of MPIR
Speed-Sensors.
III. an optional set of time history MPIR-Acceleration-Data obtained from the
system of MPIR-Accelerators.
IV. A set of time history MPIR-Pulse-State-Data obtained from the system of
MPIR-Pulse-State-Sensors.
(2) The Mechanical Power Output Rotor (MPOR) which has:
(a) a system of MPOR-State-Sensors used to collect the MPOR-State-Data. The
system of MPOR-State-Sensors has:
1. a system of MPOR-Torque-Sensors used to monitor torques of the MPOR.
II. a system of MPOR-Speed-Sensors used to monitor rotational speeds of the
III. an optional system of MPOR-Accelerators used to monitor rotational
accelerations of the MPOR.
(b) a set of time history MPOR-State-Data obtained from the MPOR-State-Sensors.
The MPOR-State-Data has:
1. a set of time history MPOR-Torque-Data obtained from the system of
MPOR-Torque-Sensors.
II. a set of time history MPOR-Speed-Data obtained from the system of
MPOR-Speed-Sensors.
III. an optional set of time history MPOR-Acceleration-Data obtained from the
system of MPOR-Accelerators.
(3) The Electrical Power Input Rotor (EPIR) which has:
(a) a system of EPIR-State-Sensors used to collect the EPIR-State-Data. The system
of EPIR-State-Sensors has:
1. a system of EPIR-Torque-Sensors used to monitor torques of the EPIR.
II. a system of EPIR-Speed-Sensors used to monitor rotational speeds of the
III. an optional system of EPIR-Accelerators used to monitor rotational
accelerations of the EPIR.
IV. a system of EPIR-Power Consumed-Sensors used to monitor time history
variations of a number of electric currents supplied to a number of coils of
the EPIR for the case that the EPIR works as an electric motor. Properties
of the electric currents need to include phases, voltages, amperages and
frequencies in time history. Time history variations of the electric currents
make the coils creating a variable rotating electromagnetic field which
makes the EPIR (or the electromagnetic field) rotating at variable
rotational speeds. Data collected by the EPIR-Power Consumed -Sensors
are the time history EPIR-Power Consumed -Data.
V. a system of EPIR-Power Generated-Sensors used to monitor electrical
power or energy generated by the EPIR for the case that the EPIR works as
a generator. Data collected by the EPIR-Power Generated-Sensors are the
time history EPIR-Power Generated-Data.
(b) a set of time history EPIR-State-Data obtained from the EPIR-State-Sensors.
The EPIR-State-Data has:
1. a set of EPIR-Torque-Data obtained from the system of EPIR-Torque
Sensors.
II. a set of EPIR-Speed-Data obtained from the system of EPIR-Speed-Sensors.
III. an optional set of EPIR-Acceleration-Data obtained from the system of
EPIR-Accelerators.
IV. a set of time history EPIR-Power Consumed-Data obtained from the system
of EPIR-Power Consumed- Sensors.
V. a set of time history EPIR-Power Generated-Data obtained from the system
of EPIR-Power Generated-Sensors.
(4) A set of M-CVT-Mechanical Power Input-Sensors used to collect time history M-CVT
Mechanical Power Input-Data which express mechanical input power (MIP) of the
M-CVT. It is particular preferred if the mechanical input power is harvested from
waves or winds. In this case, the MIP is a time history function, which is called the
MIP-Function, created by waves or winds. The MIP-Function can be handled either
separately or jointly with the MLBOCS using a machine learning based algorithm
(the Deep Learning Optimization Algorithm (DLOA)) to approximate the MIP
Function. The mechanical power input data of the M-CVT and the MPIR are the
same if the M-CVT has only one input rotor.
(5) A set of M-CVT-Mechanical Power Output-Sensors used to collect time history M
CVT-Mechanical Power Output-Data which express mechanical Output power of the
M-CVT. The Mechanical power outputs of the M-CVT can be variable. The purpose
of controllingthe M-CVT is to obtain time history mechanical outputs (of the M-CVT)
which are closest to a desired time history mechanical power required to supply a
device connected to the M-CVT with the most efficiency. If the M-CVT has only one
output rotor, mechanical power outputs of the M-CVT and the MPIR are the same.
(6) An optional M-CVT-Clutch with M-CVT-Clutch-States expressed by an optional set
of M-CVT-Clutch-State-Data which are obtained from an optional system of M-CVT
Clutch-State-Sensors. The system of M-CVT-Clutch-State-Sensors are used to collect
records of states of the M-CVT-Clutch whether it is engaged or disengaged.
(7) A number of optional M-CVT-Regulating System (M-CVT-RS)s. The M-CVT
Regulating Systems are used to regulate either mechanical power (such as a system
of flywheels) or electrical power (such as a system of Energy Storage). States of the
M-CVT-Regulating System (M-CVT-RS)s are called M-CVT-RS-States. Data of the M
CVT-RS-States, which are M-CVT-RS-State-Data, are collected from the M-CVT-RS
State-Sensors. The M-CVT-RS-State-Sensors optionally contains M-CVT-MPRS-State
Sensors and the M-CVT-EPRS-State-Sensors which belong to the following types of
the M-CVT-Regulating Systems:
(a) Mechanical Power Regulating System (MPRS)s which store and release energy
such as systems of flywheels or any other kinds of systems being used to store
and release mechanical energy. The MPRS applied for the M-CVT is called the
M-CVT-MPRS which has a system of sensors used to collect a set of M-CVT
MPRS-State-Data expressing the M-CVT-MPRS-States. The set of M-CVT-MPRS
State-Data is obtained from the system of M-CVT-MPRS-State- -Sensors. The M
CVT-MPRS-State-Data include M-CVT-MPRS-Control Parameter-Data used to
control operations of the MPRSs.
(b) Electrical Power Regulating System (EPRS)s which store and release energy
generated by the M-CVT such as systems of Energy Storages or any other kinds
of Electrical Energy Storages which are used to store and release electrical
energy. The EPRS applied for the M-CVT is called the M-CVT-EPRS which has a
system of sensors used to collect a set of M-CVT-EPRS-State-Data expressing the
M-CVT-EPRS-States. The set of M-CVT-EPRS-State-Data is obtained from the
system of M-CVT-EPRS-State-Sensors. The M-CVT-EPRS-State-Data include M
CVT-EPRS-Control Parameter-Data used to control operations of the EPRSs.
(8) A system of M-CVT-Power Consumed-Control System-Sensors used to collect time
history M-CVT-Power Consumed-Control System-Data which express electrical
energy consumed for controlling the M-CVT. The system of M-CVT-Power
Consumed-Control System-Sensors contains the system of EPIR-Power Consumed
Sensors and the set of M-CVT-Power Consumed-Control System-Data contains the
set of EPIR-Power Consumed-Data.
(9) A system of M-CVT-Electrical Power Output-Sensors used to collect time history
data of electrical power output of the M-CVT. The data is called the M-CVT-Electrical
Power Output-Data. The electrical power is generated while the EPIR works as a
generator.
(10) An optional system of Temperature-State-Sensors used to collect
Temperature-State-Data.
The MLBOCS also contains a Master Optimal Control System (MOCS) which is a Control
Software with a Deep Learning Optimization Algorithm (DLOA) integrated. The MOCS
includes the followings:
(1) Input Data, which are in time histories, are collected from different types of
Sensors at a series of time. Some of these data may be derived from the collected
data basing on mechanical or physical relations. The Input Data, which are used to
train the DLOA, are described below. Depending on costs of the systems of sensors
for data collections and how quick solutions need to be obtained, some of these
Input Data may be optional. The Input Data are:
(a) a set of time history MPIR-State-Data which include a set of MPIR-Torque-Data,
a set of MPIR-Speed-Data, a set of MPIR-Acceleration-Data and a set ofMPIR
Pulse-State-Data.
(b) a set of time history MPOR-State-Data which include a set of MPOR-Torque
Data, a set of MPOR-Speed-Data and a set of MPOR-Acceleration-Data.
(c) a set of time history EPIR-State-Data which include a set of EPIR-Torque-Data,
a set of EPIR-Speed-Data, a set of EPIR-Acceleration-Data, a set of EPIR-Power
Consumed-Data and a set of EPIR-Power Generated-Data.
(d) a set of time history M-CVT-Mechanical Power Input-Data.
(e) a set of time history M-CVT-Mechanical Power Output-Data.
(f) an optional set of time history M-CVT-Clutch-State-Data.
(g) an optional set of time history M-CVT-RS-State-Data which includes an optional
set of time history M-CVT-MPRS-State-Data and an optional set of time history
M-CVT-EPRS-State-Data. The M-CVT-MPRS-State-Data includes M-CVT-MPRS
Control Parameter-Data and the M-CVT-EPRS-State-Data includes M-CVT
EPRS-Control Parameter-Data.
(h) A set of time history M-CVT-Power Consumed-Control System-Data which
include the set of EPIR-Power Consumed-Data.
(i) A set of time history M-CVT-Electrical Power Output-Data.
(j) an optional set of time history Temperature-State-Data.
(k) an optional set of time history Variable-Time Interval-Data. It is optional to
either include or exclude time interval in computation of the Deep Learning
Optimization Algorithm (DLOA). Time interval, which equal to (t(I+1)-t(I)), is a
period of time between two adjacent time steps which are t(I) and t(+1)
(where i is the index of the time step). If the DLOA optimises energy output,
which reflects total energy generated over a period of time (time interval),
then time intervals are required for the computation. Otherwise, if the DLOA
optimises power output, time intervals are not required for the computation
although the time interval does actuallyexist asthe time history Input Data are
values at the series of time. Thus, the DLOA can apply either fixed or variable
time intervals. The Variable Time Interval is recommended to be used for
maximizing energy output and controls. The Variable-Time Interval-Data is
obtained from outputs of the DLOA for every time step: the DLOA determines
(predicts) how long the predicted optimal time interval of the Predicted
Optimal Time Step is with the condition that, at the end of the predicted
optimal time interval, the energy output obtained over the predicted optimal
time interval is maximized.
(2) Output data, which are obtained from the optimal solution of the Objective
Function at a Predicted Optimal Time Step (POTS) t(I+1). The Output Data are
required for the Control Policy of the MLBOCS. The Output Data include:
(a) a predicted Optimal Time Interval (POTI), which is (t(I+1)-t()), or alternatively,
the Predicted Optimal Time Step (POTS) t(I+1) at which the Objective Function
is optimal. The predicted optimal time interval is derived from the optimal
solution. The predicted optimal time interval is a control Parameter which
requires the M-CVT shifted to its predicted optimized state of the M-CVT at the
Predicted Optimal Time Step t(I+1). The set of time history Predicted Optimal
Time Steps are called the Predicted Optimal Time Step-Data.
(b) a set of predicted M-CVT-Mechanical Power Input-Data (at the Predicted
Optimal Time Step) expressing the MIP-Function approximated by the Deep
Learning Optimization Algorithm (DLOA). If the M-CVT has only one input,
mechanical power input of the M-CVT and the MPIR are the same.
(c) a set of predicted M-CVT-Mechanical Power Output-Data (at the Predicted
Optimal Time Step). If the M-CVT has only one output, mechanical power
output of the M-CVT and the MPOR are the same.
(d) an optional set of predicted M-CVT-Clutch-State-Data (at the Predicted
Optimal Time Step).
(e) a set of predicted MPIR-Pulse-State-Data (at the Predicted Optimal Time Step)
corresponding to predicted MPIR-Pulse-States which are optionally required
for the Control Policy.
(f) a set of predicted M-CVT-Electrical Power Output-Data (at the Predicted
Optimal Time Step).
(g) a set of predicted Optimal Control Parameter (POCP)s applied at the Predicted
Optimal Time Step: predicted optimal values of variables of the Objective
Function at the Predicted Optimal Time Step t(+1) where the Objective
Function is optimal. These predicted optimal values of variables, from which
the predicted Optimal Control Parameter (POCP)s are derived for:
1. controlling the EPIR: the EPIR has a set of time history EPIR-Power
Consumed-Data and a set of time history EPIR-Power Generated-Data. The
values of predicted EPIR-Power Consumed-Data and predicted EPIR-Power
Generated-Data at the Predicted Optimal Time Step t(+1), which are
obtained from the optimal solution, are the optimal control Parameters
used to control the EPIR at the Predicted Optimal Time Step t(I+1). The EPIR
is controlled to work as an electric motor or a generator.
II. controlling the M-CVT-Clutch: The M-CVT-Clutch, which is optional, has a set
of M-CVT-Clutch-Control Parameter-Data. The values of predicted M-CVT
Clutch-Control Parameter-Data at the Predicted Optimal Time Step t(+1),
which are obtained from the optimal solution, are the optimal control
Parameters used to control the M-CVT-Clutch at the Predicted Optimal Time
Step. In addition, the set of predicted MPIR-Puse-State-Data at the
Predicted Optimal Time Step can also be taken into account for controlling
the M-CVT-Clutch. It is notable that the EPIR can also be used as a Clutch.
III. Controlling the (controllable) M-CVT-Regulating Systems: Control
Parameters for the (controllable) M-CVT-Regulating Systems are obtained
from the predicted Optimal Control Parameter (POCP)s. The values of predicted M-CVT-MPRS-Control Parameter-Data and predicted M-CVT
EPRS-Control Parameter-Data at the Predicted Optimal Time Step are used
to control the M-CVT-MPRSs and the M-CVT-EPRSs which belong to the M
CVT-Regulating Systems.
(3) The Optional Additional Strategies for the Deep Learning Optimization Algorithm:
The DLOA deploys the following DLOA-Additional Strategies:
1. Variable Time Interval Method: The optimization problem is solved (or the
Objective Function is optimized) at a series of time, which are called time
steps (..., t(I-2), t(I-1), t(I), t(+1), ... ), in which "I" is index, to obtained
optimal solutions. The time steps where the Objective Function is optimal
is called the optimal time steps (..., t(I-2), t(I-1), t(1), t(1+1), ... ). A period of
time between two adjacent time steps is called a time interval. For
example, time interval of the two adjacent time steps t(1) and t(1+1) is Ti(I)
= (t(I+1)-t(I+1)). If the two adjacent time steps are optimal (optimal time
steps), the time interval Ti(l) is called the optimal time interval. If the
control system of the M-CVT has just passed the time step t(l) but has not
reached the time step t(1+1) then t(l) is called the current time step and
t(I+1) is called the predicted time step. In the optimization problem, which
is the Mathematical Model of the M-CVT, time is treated as a variable of
the Objective Function. There are two options, which are related to time
intervals, to solve the optimization problem. The first option is to treat the
time interval (and time step) as Parameters: the time step is predefined
and, as a result, the time interval is fixed. In this case, the Objective
Function is optimized at a known values of the time step and the time
interval. The second option, which is the Variable Time Interval Method, is
to treat the time step and, as a result, the time interval to be variable. In
this case, values of both the time step and the time interval are derived
from the optimal solution of the optimization problem. As a result, values
of the time intervals vary with respect to time. The second option of using
Variable Time Interval is recommended because if these time intervals are fixed, more time steps may be required for obtaining converged solutions. As a results, more computational tasks and more control operations are executed at every time steps. So, it is optional to optimize the Objective
Function either at a predefined time step (the time to is regarded to be a
Parameter with its value to be the predefined time step of the Objective
Function) or at an optimal time step (the time to is regarded to be a
variable of the Objective Function) which is obtained from the optimal solution of the Objective Function. The time interval derived from the optimal time step, which is obtained using Variable Time Interval Method, is called the optimal time interval. So, the purpose of the optimization problem is to obtained the optimal solution which is occurred at the Predicted Optimal Time Step. Data derived from the optimal solution are predicted data which reflect the next state of the components or portions of the M-CVT. Control Parameters, which are used to progress (or control) the M-CVT to the next state, are derived from the predicted data. The predicted optimal time interval can be obtained as follow: Firstly, the time (t) is also treated as a variable of the Objective Function. As the Input Data reflect values at the series of time, the value of these series of time are also used in training the DLOA with respect to the variable time (t) of the Objective Function. The optimal solution of the Objective Function provides the value of the Predicted Optimal Time Step, from which the predicted optimal time interval is derived. It is also mentioned that the max/min value of the Objective Function over the period of time [t(), t(1+1)] equal to max/min (Objective Function (t(l)), Objective Function (t(1+1)), Objective Function (Optimal Time (t))). II. Preselected Time Interval Method: It is possible to select a value for time interval of the next time step t(1+1). As the time interval of the next time step t(1+1) is known, the Objective Function now can be optimized with time to to be a constant instead of a variable. In this case, the optimization process for the Objective Function using Machine Learning (or Deep
Learning) is still the same as presented above.
III. Time Sub-Step: The predicted Optimal Time Interval (of the Predicted
Optimal Time Step) is divided into a numberof Sub-Steps. Based on results
obtained at the current optimal time step t(I) and at the Predicted Optimal
Time Step t(I+1), the predicted M-CVT-State-Data of the M-CVT-States at
each sub-step can be approximated. At the meantime, as the time being,
data obtained from the system of M-CVT-State-Sensors for each time sub
step are collected. Then the predicted M-CVT-State-Data and the collected
data of each time sub-step are compared to evaluate accuracies of the
progress from the current optimal time step t(I) to the Predicted Optimal
Time Step t(I+1). If the accuracies are not good enough, the Predicted
Optimal Time Step t(I+1) should be re-established with a new optimization.
IV. Interactive Learning Control Method (ILCM):
As Data collected from the M-CVT-State-Sensors become more and more,
the DLOA can learn newly collected Data and find optimal solutions
interactively.
V. Untrained Initialized Method (UIM): The Master Optimal Control System of
the MLBOCS for M-CVT can be started without prior learning. Firstly, the
MLBOCS for M-CVT starts working with a simple control policy which does
not need to include optimization. In this case, although the energy
generated is not optimized, the control system can be configured to be
able to work without optimization while data are being collected and the
DLOA is being trained. Once the DLOA is fully trained, it is ready to work
fully. In this case, Interactive Learning Control Method (ILCM) should be
integrated.
[0014] Construction of the Mathematical Model of the M-CVT
[0015] The Mathematical Model of the M-CVT (the optimization problem) applied to
find maximum power or energy output is an optimization problem. Assuming that mechanical power outputs of the M-CVT is the function MOut(t(I)), of which t(I) is time step and I is index. If the M-CVT includes Energy Storages used as regulators, the function of power output of the Energy Storages (SOut(t(l))) is also taken into account.
In addition, power consumed by the Master Optimal Control System (MOCS), including
power used for changing gear ratios by rotating the EPIR, is expressed with the Function
EConsumed(t()). Furthermore, power generated by the EPIR in the case it works as a
generator is expressed with the function EGenerated(t(l)). Thus, we have the following
functions of total power output and total energy output at the time step t(l):
(1) Total power output of the M-CVT at the time step t() is PowerOut(t(l))=
MOut(t(l)) + SOut(t(l)) - EConsumed(t()) + EGenerated(t()), where "I" is the index
of the time step t(l).
(2) Total energy output of the M-CVT over a time interval Ti(), which equals (t(+1)
t(l)), is the function EnergyOut(Ti()) = (Ti()) x (PowerOut(t(l))) where Time
Interval is the period of time between two adjacent time steps t() and t(+1). Total
energy output of the M-CVT over multiple Time Intervals is the sum of each total
energy output of the M-CVT over each Time Interval.
(3) Total energy output of the M-CVT over a period of time, which is the sum of
multiple consecutive time interval Ti(l), is the function TotalEnergyOut() where
TotalEnergyOut() equals to the sum of multiple EnergyOut(Ti()).
[0016] Variables of the above functions are time (t(l) and varieties of variables
represented in series of time history Input Data. It is notable that, among variables of
the Objective Function, there are variables which have constraints. For example, the
constraint of gear ratio (equals to ratios of rotational speeds of the MPIR and the
MPOR), which is a type of variables of the Objective Function, is min (gear ratio) < V
Gear-Ratio < max (gear ratio) where V-Gear-Ratio is the variable which represents a gear
ratio.
[0017] Why it is an optimization problem applied to maximize power output?
Depending on devices connected to the output of the M-CVT, requirements for
mechanical outputs may varies. For example, if the M-CVT is connected with a
Generator, the mathematical model of the DLOA is to solve an optimization problem
which maximizes either power or energy output by maximizing the Objective Function
of the M-CVT using Deep Learning Optimizers such as Gradient Descent or Conjugate
Gradient. The Objective Function of the M-CVT is chosen from one of the following
options:
(1) PowerOut(t(I)).
(2) EnergyOut(Ti(I)).
(3) TotalEnergyOut(.
(4) DOutput() minus (PowerOut(t(I)) or EnergyOut(Ti(I)) or TotalEnergyOut() where
DOutput() is a required time history variable mechanical inputs (for best
performance) of a device rotated by the M-CVT.
[0018] Equivalently, the optimization problem can be solved by minimizing the minus
value of the Objective Function. The process of optimization of the Objective Function
can be alternatively done by minimizing either the loss function or the cost function of
the Objective Function. If a device connected to the output of the M-CVT requires a
time history variable mechanical inputs (for its best performance) expressed by a
function called DOutput(, it is possible to minimize the function which equals to
DOutput() minus PowerOut(t(I)) or EnergyOut(Ti(I)) or TotalEnergyOut(.
[0019] Solving the optimization problem (the Mathematical Model of the M-CVT): Why
Machine Learning or Deep Learning are used to solve this optimization problem which
is the mathematical model of complex physical interactions? As described above, the
maximization of the power or energy outputs lead to solving an optimization problem.
In order to solve this optimization problem, Machine Learning or Deep Learning are
applied to adapt complex interactions between devices of the M-CVT while being
subjected by variable mechanical power inputs and temperatures.
[0020] Optimal control of the M-CVT: Why can the M-CVT be controlled optimally?
Optimal control Parameters, which are derived from the optimal solution, are deployed
for the control policy which is used to optimally control the M-CVT. All controllable
devices are started then completed progressing to their new states within allowable
periods of time which are also derived from the optimal solution. It is notable that, if
more accurate optimal solutions are required, the state of the M-CVT for the period of
time while it is being shifted from its current state to the new optimal state is also taken
into account when optimizing the Objective Function.
[0021] How is the Deep Learning Optimization Algorithm (DLOA)? The DLOA is
composed of:
(1) popular deep learning algorithms used to solve the optimization problem (the
Mathematical Model of the M-CVT) to find out optimal solutions with respect to
optimal time steps. The deep learning algorithms includes artificial neural network
(ANN)s with an input layer, multi deep layers and an output layer. As deep learning
algorithms are popular, they are not presented in this document. The deep
learning algorithms are used to find (predicted) approximated functions of the
power or energy outputs of the M-CVT. In other words, the deep learning
algorithm is used to solve the optimization problem which is the Mathematical
Model of the M-CVT.
(2) enhancements of the deep learning algorithm by applying the DLOA-Additional
Strategies.
Claims (6)
1. a Machine Learning Based Optimal Control System for Magnetic Continuous Variable Transmission (MLBOCS for M-CVT) comprising: a Magnetic Continuous Variable Transmission (M-CVT); Wherein: • The M-CVT comprising: o A Mechanical Power Input Rotor (MPIR); wherein the MPIR receives mechanical power inputs such as power created by waves or by winds; o a Mechanical Power Output Rotor (MPOR); wherein the MPOR delivers mechanical power outputs; o an Electrical Power Input Rotor (EPIR); wherein: • the EPIR has a number of coils; • the EPIR is controllably rotated by a source of variable electric power getting through the coils; • and wherein: o of the three rotors (the MPIR, the MPOR and the EPIR), there are two rotors called the First Pole Pair Rotor and the Second Pole Pair Rotor; wherein: • each of the First Pole Pair Rotor and the Second Pole Pair Rotor has a number of pole pairs; • the number of pole pairs of the First Pole Pair Rotor and the number of pole pairs of the Second Pole Pair Rotor should be different; o the last rotor is called the Pole Piece Rotor; wherein: • the Pole Piece Rotor has a number of ferromagnetic pole pieces; • the number of ferromagnetic pole pieces of the Pole Piece Rotor equals to the total number of pole pairs of both the First Pole Pair Rotor and the Second Pole Pair Rotor; • and wherein: o the three rotors (the MPIR, the MPOR and the EPIR) can be either coaxial or noncoaxial; " the MPOR has a MPOR-Magnetic Field created by its pole pairs or pole pieces; o the variable rotating (electro)magnetic field created jointly by the pair of MPIR and EPIR interacts with the MPOR-Magnetic Field making the MPOR rotated; a Machine Learning Based Optimal Control System (MLBOCS) comprising:
• a system of M-CVT-State-Sensors;
• a Master Optimal Control System (MOCS);
• wherein the system of M-CVT-State-Sensors comprising:
o a system of MPIR-State-Sensors further comprising:
• a system of MPIR-Torque-Sensors used to monitor torques of the MPIR;
• a system of MPIR-Speed-Sensors used to monitor rotational speeds of the
MPIR;
• an optional system of MPIR-Accelerators used to monitor rotational
accelerations of the MPIR; • a system of MPIR-Pulse-State-Sensors used to monitor MPIR-Pulse-States;
o a system of MPOR-State-Sensors further comprising: • a system of MPOR-Torque-Sensors used to monitor torques of the MPOR;
• a system of MPOR-Speed-Sensors used to monitor rotational speeds of the
MPOR; • an optional system of MPOR-Accelerators used to monitor rotational
accelerations of the MPOR;
o a system of EPIR-State-Sensors further comprising:
• a system of EPIR-Torque-Sensors used to monitor torques of the EPIR;
• a system of EPIR-Speed-Sensors used to monitor rotational speeds of the
EPIR; • an optional system of EPIR-Accelerators used to monitor rotational
accelerations of the EPIR; • a system of EPIR-Power Consumed-Sensors used to monitor time history
variations of a number of electric currents supplied to a number of coils of
the EPIR working as an electric motor; • a system of EPIR-Power Generated-Sensors used to monitor electrical
power or energy generated by the EPIR working as a generator; o a system of M-CVT-Mechanical Power Input-Sensors used to monitor mechanical power input of the M-CVT; o a system of M-CVT-Mechanical Power Output-Sensors used to monitor mechanical power output of the M-CVT; o an optional system of M-CVT-Clutch-State-Sensors used to monitor M-CVT
Clutch-States.
o An optional system of M-CVT-RS-State-Sensors comprising:
• a system of M-CVT-MPRS-State-Sensors used to monitor M-CVT-MPRS
States; • a system of M-CVT-EPRS-State-Sensors used to monitor system of M-CVT
EPRS-States;
o a system of M-CVT-Power Consumed-Control System-Sensors used to monitor
electrical energy consumed for controlling the M-CVT; wherein the M-CVT
Power Consumed-Control System-Sensors comprising a system of EPIR-Power
Consumed-Sensors;
o a system of M-CVT-Electrical Power Output-Sensors used to collect time history
data of power output of the M-CVT;
o an optional system of Temperature-State-Sensors;
• and wherein the Master Optimal Control System (MOCS) comprising:
o a set of time history Input Data, known as the M-CVT-State-Data;
o a set of time history Output Data; wherein: • the Output Data comprising a set of time history Predicted Optimal
Control Parameters (POCP); • the POCP is used to construct the Control Policy of the MOCS;
• the Control Policy is used to control operations of controllable devices of
the M-CVT;
o a Controlling Software; wherein: • the Controlling Software reads the set of Input Data;
• the Controlling Software generates the set of Output Data;
• the Controlling Software instructs the controllable devices of the M-CVT
to progress to new states specified by the set of Predicted Optimal
Control Parameters (POCP);
• the Controlling Software comprising the Deep Learning Optimization
Algorithm (DLOA) using the set of Input Data and generating the set of
Output Data;
o a Deep Learning Optimization Algorithm (DLOA); wherein the DLOA
comprising an algorithm of Deep Learning using Artificial Neural Networks to
solve an Optimization Problem; wherein: • the Deep Learning Optimizers applied can be any kind of popular
Optimizers such as Gradient Descent or Conjugate Gradient;
• the Optimization Problem is a Mathematical Model of the Multiphysics
System of the M-CVT;
• the Multiphysics System incorporates complex interactions between a
number of multi physical portions of the M-CVT such as temperatures,
magnetic fields, Regulating Systems and motions of mechanical devices;
• the Objective Function of the Optimization Problem of the M-CVT is
optionally chosen from the following:
> power output of the M-CVT expressed as the function Power
Out(t(I+1)); wherein t(I) is the time step at the time indexed (I) and
t(I+1) is the time step at the time indexed (1+1);
> energy output of the M-CVT expressed as the function Energy
Out(Ti()); wherein Ti(I) is the time interval between the time step
t(I) and the time step t(+1);
> total energy output of the M-CVT over a period of time covering
multiple time intervals, expressed as the function Total-Energy-Out(;
> Desired Function() minus Power-Out(t(I+1)) or Energy-Out(Ti(I)) or
Total-Energy-Out; wherein the Desired Function() expresses time
history mechanical power inputs required by a device rotated by the
MPOR;
• the solutions of the Optimization Problem are used to construct the set
of Predicted Optimal Control Parameters (POCP);
o and wherein the Deep Learning Optimization Algorithm (DLOA) further optionally comprising one or more of the following strategies: • an optional Variable Time Interval Method applied for optimizing the Objective Function with variable time intervals; • an optional Preselected Time Interval Method applied for optimizing the Objective Function with preselected time intervals; • an optional Interactive Learning Control Method (ILCM) applied for learning and optimizing phases of the Deep Learning Optimization Algorithm (DLOA). • an optional Untrained Initialized Method (UIM) applied for the initialization of the Master Optimal Control System (MOCS) with an untrained Deep Learning Optimization Algorithm (DLOA). o and wherein the set of time history Input Data comprising: • a set of time history MPIR-State-Data further comprising: a set of time history MPIR-Torque-Data obtained from the system of MPIR-Torque-Sensors; a set of time history MPIR-Speed-Data obtained from the system of MPIR-Speed-Sensors; a set of time history MPIR-Acceleration-Data obtained from the system of MPIR-Accelerators; a set of time history MPIR-Pulse-State-Data obtained from the system of MPIR-Pulse-State-Sensors; • a set of time history MPOR-State-Data further comprising: a set of time history MPOR-Torque-Data obtained from the system of MPOR-Torque-Sensors; a set of time history MPOR-Speed-Data obtained from the system of MPOR-Speed-Sensors; a set of time history MPOR-Acceleration-Data obtained from the system of MPOR-Accelerators;
• a set of time history EPIR-State-Data further comprising: a set of time history EPIR-Torque-Data obtained from the system of EPIR-Torque-Sensors; a set of time history EPIR-Speed-Data obtained from the system of EPIR-Speed-Sensors; a set of time history EPIR-Acceleration-Data obtained from the system of EPIR-Accelerators; a set of time history EPIR-Power Consumed-Data obtained from the system of EPIR-Power Consumed-Sensors; a set of time history EPIR-Power Generated-Data obtained from the system of EPIR-Power Generated-Sensors; • a set of time history M-CVT-Mechanical Power Input-Data obtained from the system of M-CVT-Mechanical Power Input-Sensors; • a set of time history M-CVT-Mechanical Power Output-Data obtained from the system of M-CVT-Mechanical Power Output-Sensors; • an optional set of time history M-CVT-Clutch-State-Data obtained from the optional system of M-CVT-Clutch-State-Sensors; • an optional set of time history M-CVT-RS-State-Data further comprising: an optional set of time history M-CVT-MPRS-State-Data further comprising a set of time history M-CVT-MPRS-Control Parameter Data; an optional set of time history M-CVT-EPRS-State-Data further comprising a set of time history M-CVT-EPRS-Control Parameter-Data; • a set of time history M-CVT-Power Consumed-Control System-Data further comprising the set of EPIR-Power Consumed-Data; • a set of time history M-CVT-Electrical Power Output-Data; • an optional set of time history Temperature-State-Data; • an optional set of time history Variable-Time Interval-Data; o and wherein the set of time history Output Data comprising: • a set of time history Predicted Optimal Time Step-Data; • a set of predicted M-CVT-Mechanical Power Input-Data;
• a set of predicted M-CVT-Mechanical Power Output-Data;
• an optional set of predicted M-CVT-Clutch-State-Data;
• a set of predicted MPIR-Pulse-State-Data;
• a set of predicted M-CVT-Electrical Power Output-Data;
• a set of predicted Optimal Control Parameter (POCP)s applied at the
Predicted Optimal Time Step; wherein the POCPs comprising:
a set of predicted EPIR-Power Consumed-Data and a set of predicted
EPIR-Power Generated-Data containing parameters for controlling the
EPIR at the Predicted Optimal Time Step; wherein:
/ the EPIR is switched between states of working as an electric
motor or a generator;
as an electric motor, using the predicted EPIR-Power Consumed
Data, the EPIR is variably rotated by handling the controllable
electric currents consumed by the EPIR in order to handle required
rotations of the MPOR;
/ as a generator, while mechanical input power (MIP) being
excessed, the EPIR is controlled for both handling required
rotations of the MPOR and generating electricity using the
excessed MIP;
an optional set of time history M-CVT-Clutch-Control Parameter-Data
and an optional set of predicted MPIR-Pulse-State-Data containing
parameters for controlling the optional M-CVT-Clutch at the Predicted
Optimal Time Step;
a set of predicted M-CVT-MPRS-Control Parameter-Data and a set of
predicted M-CVT-EPRS-Control Parameter-Data containing
parameters for controlling the M-CVT-MPRSs and the M-CVT-EPRSs at
the Predicted Optimal Time Step.
2. a Machine Learning Based Optimal Control System for Magnetic Continuous
Variable Transmission (MLBOCS for M-CVT) according to Claim 1; wherein:
E the M-CVT is further simplified; wherein:
• the EPIR is combined with either the MPIR or the MPOR or ground structure; wherein: o coils of the EPIR are attached to either the MPIR, the MPOR or the ground structure; o the electric currents used for the coils are multi phases; o the electric currents of multi phases and the coils create a variable rotating electromagnetic field for further rotatingthe MPOR; E all remaining features of the MLBOCS for M-CVT remain unchanged.
3. a Machine Learning Based Optimal Control System for Magnetic Continuous Variable Transmission (MLBOCS for M-CVT) according to Claim 1 and Claim 2 further comprising a Multiple Contained M-CVT (MCM-CVT) in lieu of the M-CVT; wherein: E the MCM-CVT comprises a number of M-CVTs; wherein: • rotors of each M-CVT are coaxial and the M-CVTs are coaxial; • the most inner M-CVT is contained by an outer M-CVT; • the outer M-CVT becomes the next inner M-CVT; • the next inner M-CVT is contained by the next outer M-CVT; • the process is continued until reaching the most next outer M-CVT; • the most next outer M-CVT is the largest one; • gear ratios of the MCM-CVT equal to multiplications of all gear ratios of the M CVTs; • each M-CVT of each pair of adjacent M-CVTs shares a common rotor; wherein: o the common rotor is the MPOR of a M-CVT of the pair; o the common rotor is the MPIR of the other M-CVT of the pair; • each M-CVT has systems of sensors and sets of data like the M-CVT comprised in the Claim 1 and Claim 2; E all remaining features of the MLBOCS for M-CVT remain unchanged.
4. a Machine Learning Based Optimal Control System for Magnetic Continuous Variable Transmission (MLBOCS for M-CVT) according to Claim 1 and Claim 2 further comprising a Multiple Bevelled M-CVT (MBM-CVT) in lieu of the M-CVT; wherein: E the MBM-CVT comprises a number of M-CVTs; wherein:
• at least one of the M-CVTs has noncoaxial rotors; • each rotor of each M-CVT have magnetic contacts with at least another rotor of the same M-CVT; • each M-CVT has magnetic contacts with at least one of the remaining M-CVTs; • torque capacity of the MBM-CVT might be less in comparison with coaxial M-CVTs; • at least a MPIR rotor of a M-CVT is used for mechanical power input and at least a MPOR of another M-CVT is used for mechanical power output of the MBM-CVT; • each M-CVT has systems of sensors and sets of data like the M-CVT comprised in the Claim 1 and Claim 2; E all remaining features of the MLBOCS for M-CVT remain unchanged.
5. a Machine Learning Based Optimal Control System for Magnetic Continuous Variable Transmission (MLBOCS for M-CVT) according to Claim 3 and Claim 4 with further enhancements for multiple mechanical power inputs and outputs; where in: EA number of MPIRs of a numberof M-CVTs are used for mechanical power inputs from a number of different external sources; EA number of MPORs of a number of M-CVTs are used for mechanical power outputs to a number of different external devices; EA number of M-CVTs do not have EPIRs if these have all three rotors used for either mechanical power inputs or outputs; E each M-CVT has systems of sensors and sets of data like the M-CVT comprised in the Claim 1 and Claim 2; E all remaining features of the MLBOCS for M-CVT remain unchanged.
6. a Machine Learning Based Optimal Control System for Magnetic Continuous Variable Transmission (MLBOCS for M-CVT) according to Claim from 1 to 5 applied for transmitting energy harvested from waves and winds.
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| US20060166784A1 (en) * | 2004-04-27 | 2006-07-27 | Atsushi Tabata | Control device for vehicular drive system |
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| US20060166784A1 (en) * | 2004-04-27 | 2006-07-27 | Atsushi Tabata | Control device for vehicular drive system |
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