Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Vehicle subsystem or accessory control
Reexamination Certificate
2001-10-19
2004-03-02
Cuchlinski, Jr., William A. (Department: 3661)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Vehicle subsystem or accessory control
C280S005504, C703S001000, C703S002000, C703S006000
Reexamination Certificate
active
06701236
ABSTRACT:
BACKGROUND
1. Field of the Invention
The disclosed invention is relates generally to control systems, and more particularly to electronically controlled suspension systems.
2. Description of the Related Art
Feedback control systems are widely used to maintain the output of a dynamic system at a desired value in spite of external disturbances that would displace it from the desired value. For example, a household space-heating furnace, controlled by a thermostat, is an example of a feedback control system. The thermostat continuously measures the air temperature inside the house, and when the temperature falls below a desired minimum temperature the thermostat turns the furnace on. When the interior temperature reaches the desired minimum temperature, the thermostat turns the furnace off. The thermostat-furnace system maintains the household temperature at a substantially constant value in spite of external disturbances such as a drop in the outside temperature. Similar types of feedback controls are used in many applications.
A central component in a feedback control system is a controlled object, a machine, or a process that can be defined as a “plant”, having an output variable or performance characteristic to be controlled. In the above example, the “plant” is the house, the output variable is the interior air temperature in the house and the disturbance is the flow of heat (dispersion) through the walls of the house. The plant is controlled by a control system. In the above example, the control system is the thermostat in combination with the furnace. The thermostat-furnace system uses simple on-off feedback control system to maintain the temperature of the house. In many control environments, such as motor shaft position or motor speed control systems, simple on-off feedback control is insufficient. More advanced control systems rely on combinations of proportional feedback control, integral feedback control, and derivative feedback control. A feedback control based on a sum of proportional feedback, plus integral feedback, plus derivative feedback, is often referred as PID control.
A PID control system is a linear control system that is based on a dynamic model of the plant. In classical control systems, a linear dynamic model is obtained in the form of dynamic equations, usually ordinary differential equations. The plant is assumed to be relatively linear, time invariant, and stable. However, many real-world plants are time-varying, highly non-linear, and unstable. For example, the dynamic model may contain parameters (e.g., masses, inductance, aerodynamics coefficients, etc.), which are either only approximately known or depend on a changing environment. If the parameter variation is small and the dynamic model is stable, then the PID controller may be satisfactory. However, if the parameter variation is large or if the dynamic model is unstable, then it is common to add adaptive or intelligent (AI) control functions to the PID control system.
AI control systems use an optimizer, typically a non-linear optimizer, to program the operation of the PID controller and thereby improve the overall operation of the control system.
Classical advanced control theory is based on the assumption that near of equilibrium points all controlled “plants” can be approximated as linear systems. Unfortunately, this assumption is rarely true in the real world. Most plants are highly nonlinear, and often do not have simple control algorithms. In order to meet these needs for a nonlinear control, systems have been developed that use soft computing concepts such as genetic algorithms, fuzzy neural networks, fuzzy controllers and the like. By these techniques, the control system evolves (changes) over time to adapt itself to changes that may occur in the controlled “plant” and/or in the operating environment.
Currently, self-organizing control systems based on fuzzy controllers suffer from two drawbacks. First, when a genetic analyzer is used to develop a teaching signal for a fuzzy neural network, the teaching signal typically contains unnecessary stochastic noise, making it difficult to later develop an approximation to the teaching signal. Second, the fitness functions used for the genetic analyzers in self-organizing suspension control systems typically optimize the control system according to some desired control paradigm without reference to human factors such as rider comfort.
SUMMARY
The present invention solves these and other problems by providing a control system for optimizing a shock absorber system having a non-linear kinetic characteristic. The control system uses a fitness (performance) function that is based on the physical laws of minimum entropy and biologically inspired constraints relating to 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. The control system can be used to control complex plants described by nonlinear, unstable, dissipative models. The control system is configured to use smart simulation techniques for controlling the shock absorber (plant).
In one embodiment, the control system comprises a learning system, such as a neural network that is trained by a genetic analyzer. The genetic analyzer uses a fitness function that maximizes sensor information while minimizing entropy production based on biologically-inspired constraints.
In one embodiment, a suspension control system uses a difference between the time differential (derivative) of entropy (called the production entropy rate) from the learning control unit and the time differential of the entropy inside the controlled process (or a model of the controlled process) as a measure of control performance. In one embodiment, the entropy calculation is based on a thermodynamic model of an equation of motion for a controlled process plant that is treated as an open dynamic system.
The control system is trained by a genetic analyzer that generates a teaching signal. The optimized control system provides an optimum control signal based on data obtained from one or more sensors. For example, in a suspension system, a plurality of angle and position sensors can be used. In an off-line learning mode (e.g., in the laboratory, factory, service center, etc.), fuzzy rules are evolved using a kinetic model (or simulation) of the vehicle and its suspension system. Data from the kinetic model is provided to an entropy calculator that calculates input and output entropy production of the model. The input and output entropy productions are provided to a fitness function calculator that calculates a fitness function as a difference in entropy production rates for the genetic analyzer constrained by one or more constraints obtained from rider preferences. The genetic analyzer uses the fitness function to develop a training signal for the off-line control system. The training signal is filtered to produce a compressed training signal. Control parameters from the off-line control system are then provided to an online control system in the vehicle that, using information from a knowledge base, develops an approximation to the compressed training signal.
In one embodiment, the invention includes a method for controlling a nonlinear object (a plant) by obtaining an entropy production difference between a time differentiation (dS
u
/dt) of the entropy of the plant and a time differentiation (dS
c
/dt) of the entropy provided to the plant from a controller. A genetic algorithm that uses the entropy production difference as a fitness (performance) function evolves a control rule in an off-line controller. The nonlinear stability characteristics of the plant are evaluated using a Lyapunov function. The genetic analyzer minimizes entropy and maximizes sensor information content. Filtered control rules from the off-line contro
Hagiwira Takahide
Kurawaki Ichiro
Panfilov Sergei
Ulyanov Sergei V.
Cuchlinski Jr. William A.
Hernandez Olga
Knobbe Martens Olson & Bear LLP
Yamaha Hatsudoki Kabushiki Kaisha
LandOfFree
Intelligent mechatronic control suspension system based on... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Intelligent mechatronic control suspension system based on..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Intelligent mechatronic control suspension system based on... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3194412