Yamaha Hatsudoki Kabushiki Kaisha

Yamaha Motor Company Ltd

Sergei V Ulyanov

Takahide Hagiwara

Masashi Yamaguchi

Shigeki Hashimoto

Sergei Panfilov

Ichiro Kurawaki

Kazuki Takahashi

Takahide Hagiwira

A control system using a genetic analyzer based on discrete constraints is described. In one embodiment, a genetic algorithm with step-coded chromosomes is used to develop a teaching signal that provides good control qualities for a controller with discrete constraints, such as, for example, a step-constrained controller. In one embodiment, the control system uses a fitness (performance) function that is based on the physical laws of minimum entropy. In one embodiment, the genetic analyzer is used in an off-line mode to develop a teaching signal for a fuzzy logic classifier system that develops a knowledge base. The teaching signal can be approximated online by a fuzzy controller that operates using knowledge from the knowledge base. The control system can be used to control complex plants described by nonlinear, unstable, dissipative models. In one embodiment, the step-constrained control system is configured to control stepping motors.

System and method for nonlinear dynamic control based on soft computing with discrete constraints

A reduced control system suitable for control of a nonlinear or unstable plant is described. The reduced control system is configured to use a reduced sensor set for controlling the plant without significant loss of control quality (accuracy) as compared to an optimal control system with an optimum sensor set. The control system calculates the information content provided by the reduced sensor set as compared to the information content provided by the optimum set. The control system also calculates the difference between the entropy production rate of the plant and the entropy production rate of the controller. A genetic optimizer is used to tune a fuzzy neural network in the reduced controller. A fitness function for the genetic optimizer provides optimum control accuracy in the reduced control system by minimizing the difference in entropy production while maximizing the sensor information content.

System for intelligent control based on soft computing

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. An information filter is used to filter the teaching signal to produce a compressed teaching signal. The compressed teaching signal can be approximated online by a fuzzy controller that operates using knowledge from a knowledge base. In one embodiment, the control system includes a learning system, such as a neural network that is trained by the compressed training signal. The learning system is used to create a knowledge base for use by an online fuzzy controller. The online fuzzy controller is used to program a linear controller.

Intelligent mechatronic control suspension system based on soft computing

A reduced control system suitable for control of an engine as a nonlinear plant is described. The reduced control system is configured to use a reduced sensor set for controlling the plant without significant loss of control quality (accuracy) as compared to an optimal control system with an optimum sensor set. The control system calculates the information content provided by the reduced sensor set as compared to the information content provided by the optimum set. The control system also calculates the difference between the entropy production rate of the plant and the entropy production rate of the controller. A genetic optimizer is used to tune a fuzzy neural network in the reduced controller. A fitness function for the genetic optimizer provides optimum control accuracy in the reduced control system by minimizing the difference in entropy production while maximizing the sensor information content.

System for intelligent control of an engine based on soft computing

A methodology and an algorithm for programming a quantum logic algorithm is described. In one embodiment, an algorithm for generating a quantum gate is described. The quantum gate describes the evolution of the quantum computing algorithm and is used to implement a desired quantum algorithm. In one embodiment, the quantum gate is used in a quantum search algorithm to search a number of local solution spaces to find a global solution to be used in a control system to control a plant. In one embodiment, the quantum search algorithm is an iterative algorithm and an entropy-based basis for stopping the iterations is described. In one embodiment, the quantum search algorithm is used to improve a genetic optimizer in the control system.

System and method for control using quantum soft computing

A control system for optimizing the performance of a vehicle suspension system by controlling the damping factor of one or more shock absorbers is described. The control system uses a fitness (performance) function that is based on the physical laws of minimum entropy. The control system uses a fuzzy neural network that is trained by a genetic analyzer. The genetic analyzer uses a fitness function that maximizes information while minimizing entropy production. The fitness function uses a difference between the time differential of entropy from a control signal produced in a learning control module and the time differential of the entropy calculated by a model of the suspension system that uses the control signal as an input The entropy calculation is based on a dynamic model of an equation of motion for the suspension system such that the suspension system is treated as an open dynamic system.

Optimization control method for shock absorber

A reduced control system suitable for control of an active suspension system as a controlled plant is described. The reduced control system is configured to use a reduced sensor set for controlling the suspension without significant loss of control quality (accuracy) as compared to an optimal control system with an optimum sensor set. The control system calculates the information content provided by the reduced sensor set as compared to the information content provided by the optimum set. The control system also calculates the difference between the entropy production rate of the plant and the entropy production rate of the controller. A genetic optimizer is used to tune a fuzzy neural network in the reduced controller. A fitness function for the genetic optimizer provides optimum control accuracy in the reduced control system by minimizing the difference in entropy production while maximizing the sensor information content.

System for intelligent control of a vehicle suspension based on soft computing

A self-organizing control system suitable for nonlinear control of a physical object is described. The control system calculates the entropy production difference between a time differentiation (dS /dt) of the entropy inside the controlled object and a time differentiation (dS /dt) of the entropy given to the controlled object from a PID controller that controls the object. The entropy production difference is used to generate an evolving control rule by using the entropy production difference as a performance function for a genetic optimizer.

Self-organizing control system

A control system for optimizing the performance of a vehicle suspension system by controlling the damping factor of one or more shock absorbers is described. The control system uses a fitness (performance) function that is based on the physical laws of minimum entropy. The control system uses a fuzzy neural network that is trained by a genetic analyzer. The genetic analyzer uses a fitness function that maximizes information while minimizing entropy production. The fitness function uses a difference between the time differential of entropy from a control signal produced in a learning control module and the time differential of the entropy calculated by a model of the suspension system that uses the control signal as an input. The entropy calculation is based on a dynamic model of an equation of motion for the suspension system such that the suspension system is treated as an open dynamic system.

Growing community of inventors

Crema, Italy

Sergei V Ulyanov