Data processing: measuring – calibrating – or testing – Measurement system – Measured signal processing
Reexamination Certificate
1999-06-10
2004-01-13
Wachsman, Hal (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Measured signal processing
C702S181000, C702S183000, C706S025000
Reexamination Certificate
active
06678640
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to a parameter estimation apparatus and a parameter estimation method and, more particularly, to improvement of stability when estimating parameters by using a neural network which stores information and operates adaptively to an object or environment.
The invention also relates to a parameter estimation control device and a parameter estimation control method and, more particularly, to those estimating parameters relating to control of a control object by using a neural network, and controlling the object in accordance with the estimated parameters.
Furthermore, the invention relates to a learning control device and a learning control method and, more particularly, to learning control which enables highly precise follow-up control to a target value when calculating a learning control quantity by using an output from a neural network.
BACKGROUND OF THE INVENTION
Current digital computers used for calculation or control are stored program computers and consecutive sequence computers which are called “von Neumann architecture”. On the other hand, there have been many studies on “neural networks” based on models of connected neurons which manage the function of human brain. Applications of neural networks for estimation or control have been proposed in various fields, for example, the field where pattern processing, which is von Neumann computer's week subject, is required, or the field where an object has strong non-linearity and so is hard to be analyzed. In some fields, neural networks have been put to practical use.
That is, even when it is difficult to theoretically derive a causal relation between an input and an output in a physical or chemical system, a neural network enables estimation of an output value from an input value according to its learning function. Taking this advantage, in recent years, neural networks have been applied to control devices for controlling complicated control systems, especially, control devices for controlling objects of strong non-linearity.
A neural network has a plurality of multi-input and multi-output elements called “units” which are neurons simplified as model systems, and generates or changes the interconnections of the units by learning. These units form a feed-forward type hierarchical network or a feed-back type interconnection network.
FIG. 52
is a diagram for explaining a hierarchical network in which units form a multi-layer structure. In such neural network, a plurality of intermediate layers reside between an input layer to which an object to be processed by the neural network is input and an output layer from which the processing result is output. The units included in each layer form connections with the units in the adjacent layer, and these connections are represented by connection weights or connection coefficients. The construction of these connections is formed by learning to output a desired signal with respect to a specific input. As a learning method useful for a hierarchical neural network as shown in
FIG. 52
, there is a back propagation method. This method attracts attention as it is able to provide a neural network which can be constructed with technologically realizable number of units. Furthermore, there are two learning methods, “supervised learning” and “unsupervised learning”. In the former, an output (signal) is given from the user, and in the latter, the neural network forms its own construction according to the statistical characteristics of an input signal. One of these methods is selected according to the application of the neural network.
Generally, in the hierarchical neural network shown in
FIG. 52
, control using the neural network is performed as follows. A region where control is to be executed is defined as a learning domain. Parameters required for control are estimated by using the neural network which has learned within the learning domain, and control is performed using the estimated parameters.
FIG. 53
is a diagram for explaining a method of calculating an estimate value (parameter) in the conventional neural network. With reference to
FIG. 53
,
1501
is an object to be controlled (hereinafter referred to as a control object), and
1502
denotes a neural network (NN) operation unit. An input and an output to/from the control object
1501
are U and Y, respectively. An operation parameter Z including time series data of these input and output is input to the NN operation unit
1502
, and the processing result is obtained as an output (estimate value) X. By using the estimate value X so obtained, the input control quantity U to the control object
1501
can be calculated so that the output Y from the control object
1501
becomes a target value. The neural network (NN) of the NN operation unit
1502
has a three-layer structure which has one intermediate layer in the hierarchical network shown in
FIG. 52
, and inter-layer outputs are obtained by function operation such as a sigmoid function.
As an example of a control system using a neural network, there is an air-to-fuel ratio controller for an internal combustion engine of a motorcar. “Air-to-fuel ratio” is the ratio of air to fuel in the intake gas of the engine. Examples of air-to-fuel ratio controllers are as follows: a motorcar control device disclosed in Japanese Published Patent Application No. Hei. 3-235723, an air-to-fuel ratio controller disclosed in Japanese Published Patent Application No. Hei. 8-74636, and a parameter estimation device disclosed in Japanese Published Patent Application No. Hei. 11-85719 (Application No. Hei. 9-238017)
The advantage of using a neural network in an air-to-fuel controller for an internal combustion engine of a motorcar is as follows.
With respect to NOx, CO, and HC which are noxious gases included in an exhaust gas from a motorcar, regulations in various countries must be cleared. So, there is adopted a method of reducing the noxious gases by using a catalyst. As a typical catalyst, a ternary catalyst is used.
FIG. 54
illustrates the outline of an air-to-fuel ratio controller. An air flowing into an engine according to the opening degree of a throttle (TL) is mixed with a fuel injected from a fuel injection unit (INJ), and the mixture flows through a valve V
1
into a combustion chamber, wherein explosion occurs. Thereby, a downward pressure is applied to a piston (P), and an exhaust gas is discharged through a valve V
2
and an exhaust pipe. At this time, the air-to-fuel ratio is detected by an air-to-fuel ratio sensor AFS, and the exhaust gas is purified by a ternary catalyst (TC). To make the catalyst effectively purify the noxious gases, it is necessary to keep the air-to-fuel ratio constant, i.e., at 14.7, so that the catalyst can work effectively. For this purpose, an air-to-fuel ratio controller which can keep the air-to-fuel ratio constant regardless of the motorcar's operating state is required.
In the air-to-fuel ratio controller constructed as described above, usually, feed-forward control is carried out, that is, increase or decrease in the quantity of fuel to be injected is corrected according to change of the throttle's opening degree or the like and, further, feed-back control is carried out as well. These controls secure successful results in the steady operation state such as idling or constant-speed driving. However, in the transient state such as acceleration or deceleration, it is very difficult to keep the air-to-fuel ratio constant by only the simple feed-forward/feed-back operation because of factors which are difficult to analyze, for example, a delay in response of the air-to-fuel ratio sensor, and successive change in the quantity of fuel actually flowing into the cylinder according to the driving state or external environment.
So, in order to improve the precision of air-to-fuel ratio control, a neural network learns non-linear factors such as the above-described fuel injection, and correction of the fuel injection quantity is controlled by using this neural network to improve the response characte
Ishida Akira
Takigawa Masuo
Matsushita Electric - Industrial Co., Ltd.
Parkhurst & Wendel L.L.P.
Wachsman Hal
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