Data processing: artificial intelligence – Fuzzy logic hardware – Fuzzy neural network
Reexamination Certificate
2002-07-02
2004-04-13
Davis, George B. (Department: 2121)
Data processing: artificial intelligence
Fuzzy logic hardware
Fuzzy neural network
Reexamination Certificate
active
06721718
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The disclosed invention relates generally to control systems, and more particularly to control systems using artificial intelligence to control a nonlinear plant.
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 disturbance forces that would move the output away from the desired value. For example, a household furnace controlled by a thermostat is an example of a feedback control system. The thermostat continuously measures the air temperature of the house, and when the temperature falls below a desired minimum temperature, the thermostat turns the furnace on. When the furnace has warmed the air above the desired minimum temperature, then the thermostat turns the furnace off. The thermostat-furnace system maintains the household temperature at a constant value in spite of external disturbances such as a drop in the outside air temperature. Similar types of feedback control are used in many applications.
A central component in a feedback control system is a controlled object, otherwise known as a process “plant,” whose output variable is to be controlled. In the above example, the plant is the house, the output variable is the air temperature of the house, and the disturbance is the flow of heat 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 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. Feedback that is the sum of proportional plus integral plus derivative feedback is often referred to as PID control.
The 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 nonlinear, and unstable. For example, the dynamic model may contain parameters (e.g., masses, inductances, aerodynamic coefficients, etc.) which are either poorly 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 sufficient. However, if the parameter variation is large, or if the dynamic model is unstable, then it is common to add adaptation or intelligent (AI) control to the PID control system.
AI control systems use an optimizer, typically a nonlinear optimizer, to program the operation of the PID controller and thereby improve the overall operation of the control system. The optimizers used in many AI control systems rely on a genetic algorithm. Using a set of inputs, and a fitness function, the genetic algorithm works in a manner similar to process of evolution to arrive at a solution which is, hopefully, optimal. The genetic algorithm generates sets of chromosomes (corresponding to possible solutions) and then sorts the chromosomes by evaluating each solution using the fitness function. The fitness function determines where each solution ranks on a fitness scale. Chromosomes which are more fit, are those chromosomes which correspond to solutions that rate high on the fitness scale. Chromosomes which are less fit, are those chromosomes which correspond to solutions that rate low on the fitness scale. Chromosomes that are more fit are kept (survive) and chromosomes that are less fit are discarded (die). New chromosomes are created to replace the discarded chromosomes. The new chromosomes are created by crossing pieces of existing chromosomes and by introducing mutations.
The PID controller has a linear transfer function and thus is based upon a linearized equation of motion for the plant. Prior art genetic algorithms used to program PID controllers typically use simple fitness functions and thus do not solve the problem of poor controllability typically seen in linearization models. As is the case with most optimizers, the success or failure of the optimization often ultimately depends on the selection of the performance (fitness) function.
Evaluating the motion characteristics of a nonlinear plant is often difficult, in part due to the lack of a general analysis method. Conventionally, when controlling a plant with nonlinear motion characteristics, it is common to find certain equilibrium points of the plant and the motion characteristics of the plant are linearized in a vicinity near an equilibrium point. Control is then based on evaluating the pseudo (linearized) motion characteristics near the equilibrium point. This technique works poorly, if at all, for plants described by models that are unstable or dissipative.
SUMMARY OF THE INVENTION
The present invention solves these and other problems by providing a new AI control system that allows a reduced number of sensors to be used without a significant loss in control accuracy. The new AI control system is self-organizing and uses a fitness (performance) function that are based on the physical laws of minimum entropy and maximum sensor information. The self-organizing control system may be used to control complex plants described by nonlinear, unstable, dissipative models. The reduced control system is configured to use smart simulation techniques for controlling the plant despite the reduction in the number of sensor number without significant loss of control quality (accuracy) as compared to an optimal control system. In one embodiment, the reduced control system comprises a neural network that is trained by a genetic analyzer. The genetic analyzer uses a fitness function that maximizes information while minimizing entropy production.
In one embodiment, the reduced control system is applied to an internal combustion engine to provide control without the use of extra sensors, such as, for example, an oxygen sensor. The reduced control system develops a reduced control signal from a reduced sensor set. The reduced control system is trained by a genetic analyzer that uses a control signal developed by an optimized control system. The optimized control system provides an optimum control signal based on data obtained from temperature sensors, air-flow sensors, and an oxygen sensor. In an off-line learning mode, the optimum control signal is subtracted from a reduced control signal (developed by the reduced control system) and provided to an information calculator. The information calculator provides an information criteria to the genetic analyzer. Data from the reduced sensor set is also provided to an entropy model, which calculates a physical criteria based on entropy. The physical criteria is also provided to the genetic analyzer. The genetic analyzer uses both the information criteria and the physical criteria to develop a training signal for the reduced control system.
In one embodiment, a reduced control system is applied to a vehicle suspension to provide control of the suspension system using data from a reduced number of sensors. The reduced control system develops a reduced control signal from a reduced sensor set. The reduced control system is trained by a genetic analyzer that uses a control signal developed by an optimized control system. The optimized control system provides an optimum control signal based on data obtained from a plurality of angle and position sensors. In an off-line learning mode, the optimum control signal is subtracted from a reduced control signal (developed by the reduced control system) and provided to an information calculator. In one embodiment, the reduced control system uses a vertical accelerometer mounted near the
Davis George B.
Knobbe Martens Olson & Bear LLP
Yamaha Hatsudoki Kabushiki Kaisha
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