System and method for simulation of nonlinear dynamic systems applicable within soft computing

A non-linear and equational technology, applied in the field of stochastic simulation, can solve problems such as increasing simulation time complexity and increasing computing resources

CN1672146AInactive Publication Date: 2005-09-21YAMAHA MOTOR CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2005-09-21
Estimated Expiration
Not applicable · inactive patent

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Abstract

A system and method for efficient stochastic simulation of dynamic systems is described. Since analytic solutions cannot usually be found for stochastic differential equations, complete analysis requires numerical simulations. These simulations are most commonly done with first-order Euler-type algorithm. The efficiency of these algorithms is improved by removing algebraic loops in the simulation. An algebraic loop occurs when an output variable of the system of equations is also in an input variable to one or more of the equations describing the system. In one embodiment, the algebraic loops are removed by formulating a simulation wherein an output variable that gives rise to an algebraic loop is integrated to produce an integrated output. The integrated output is later provided to a differentiator to reconstruct the output variable as needed.
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Description

Technical field

[0001] The present invention generally relates to the stochastic simulation of a nonlinear dynamic system with a variable stochastic structure. Background technique

[0002] The numerical evaluation and simulation of nonlinear dynamic differential equations are usually based on Euler method or Runge-Kutta method. These methods use a local algebraic loop, which actually requires additional integration time. The time complexity of this integration is closely related to the following factors: 1) the number of degrees of freedom of the dynamic system; 2) the non-linear type and non-linear structure displayed by the dynamic system; and 3) the type of random excitation. The accuracy of the calculation result depends on the order of the integration routine and the setting of the integration tolerance.

[0003] The first two factors listed above determine the strategies used for the numerical simulation of actual nonlinear dynamic systems. Standard methods to reduce the o...

Examples

Embodiment Construction

[0030] figure 1 A block diagram of a control system 100 for controlling equipment based on soft computing is shown. In the controller 100, the reference signal y is provided to the first input of the adder 105. The output of the adder 105 is an error signal ε, which is provided to the input of the fuzzy controller (FC) 143 and the input of the proportional-integral-derivative (PID) controller 150. The output of the PID controller 150 is the control signal u * , It is provided to the control input of the device 120 and the first input of the entropy calculation module 132. The interference m(t) 110 is also provided to the input of the device 120. One output of the device 120 is the response x, which is provided to the second input of the entropy calculation module 132 and the second input of the adder 105. The second input of the adder 105 is inverted so that the output of the adder 105 (error signal ε) is the value of the first input minus the value of the second input.

[0031]...