Intelligent mechatronic control suspension system based on quantum soft computing
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A control system and suspension system technology, applied in the field of control systems, can solve problems such as poor control quality and difficulty in generating training signal approximations
Inactive Publication Date: 2005-09-21
YAMAHA MOTOR CO LTD
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[0007] When a genetic analyzer is used to generate a training signal in a fuzzy neural network, the training signal typically includes unnecessary random noise, making it difficult to generate an approximation of the training sign
Method used
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example 1
[0165] Example 1: Assume that X(t) is uniformly distributed, ie
[0166] p ( x ) = 1 2 Δ , - Δ ≤ x ≤ Δ - - - ( 2.10 )
[0167] Substitute (2.10) into (2.8)
[0168] D. 2 (x)=α(Δ 2 -x 2 ) (2.11)
[0169] In this case, given the desired Ito equation
[0170] dX = αXdt + α ( Δ 2 - X 2 ) dB ( t ) - - - ( 2.12 )
[0171]Interestingly, ...
example 2
[0174] Example 2: Make X(t) Rayleigh distributed
[0175] p(x)=γ 2 xexp(-γx), γ>0, 0≤x<∞ (2.14)
[0176] Its concentrated form Y(t)=X(t)-2 / γ has a probability density:
[0177] p(y)=γ(γy+2)exp(-γy+2), -2 / γ≤y<∞ (2.15)
[0178] From equation (2.8)
[0179] D 2 ( y ) = 2 α γ ( y + 2 γ ) - - - ( 2.16 )
[0180] The lto equation for Y(t) is
[0181] dY = - αYdt + [ 2 α γ ( Y + 2 γ ...
example 3
[0185] Example 3: Consider a family of probability densities that obey the following equation:
[0186] d dx p ( x ) = J ( x ) p ( x ) - - - ( 2.19 )
[0187] After integrating equation (2.19), we get
[0188] p(x)=C 1 exp(∫J(x)dx) (2.20)
[0189] where C 1 is the standardized constant, in this case
[0190] D. 2 (x)=-2αexp[-J(x)]∫xexp[J(x)]dx (2.21)
[0191] A number of special circumstances should be noted, so that
[0192] J(x)=-γx 2 -δx 4 , -∞
[0193] where γ is arbitrary if δ>0, and substituting equation (2.22) into equation (2.8) gives
[0194] D 2 ( x ) = ...
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Abstract
A control system for optimizing a shock absorber having a non-linear kinetic characteristic is described. The control system uses a fitness (performance) function that is based on the physical laws of minimum entropy and biologically inspired constraints relating to mechanical constraints and/or rider comfort, driveability, etc. In one embodiment, a genetic analyzer is used in an off-line mode to develop a teaching signal. The teaching signal can be approximated online by a fuzzy controller that operates using knowledge from a knowledge base. A learning system is used to create the knowledge base for use by the online fuzzy controller. In one embodiment, the learning system uses a quantum search algorithm to search a number of solution spaces to obtain information for the knowledge base. The online fuzzy controller is used to program a linear controller.
Description
technical field [0001] The present invention relates generally to control systems, and more particularly to electronically controlled suspension systems. Background technique [0002] Feedback control systems are widely used to keep the output value of a dynamic system at a desired value despite external disturbances that make it deviate from the desired value. For example, a domestic space heating fireplace controlled by a thermostat is an example of a feedback control system. The thermostat continuously measures the temperature of the air in the room, and when the temperature drops below the expected minimum temperature, the thermostat turns on the fireplace, and when the room temperature reaches the expected minimum temperature, the thermostat turns off the fireplace. While thermostat-fireplace systems maintain the indoor temperature at a relatively constant value despite external disturbances, such as a drop in outdoor temperature, the same type of feedback control is u...
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