Fixed-Point Virtual Sensor Control for Embedded Systems
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Solution Overview
Problem
Conventional virtual sensor techniques require significant run-time computation using floating-point representation, making them impractical for real-time applications, especially on embedded computer platforms without floating-point arithmetic hardware, and are costly and unreliable like physical sensors.
Innovation Solution
A method and system for converting virtual sensor models to fixed-point representation, enabling their operation using fixed-point arithmetic, which reduces computational complexity and improves performance by using a processor to establish and load virtual sensor models indicative of interrelationships between sensing and measured parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If virtual sensor models use floating-point representation for computation, then measurement precision is improved, but device complexity and computational burden increase significantly
Solution Approach 1:
The patent transforms the numerical representation parameter from floating-point to fixed-point format. This parameter change maintains the ability to represent sensor values with sufficient precision while dramatically reducing computational complexity. The fixed-point representation uses integer arithmetic with a predetermined number of bits for integer and fractional parts, eliminating the need for complex floating-point hardware operations.
Solution Approach 2:
The patent substitutes floating-point arithmetic operations with fixed-point arithmetic operations. This substitution replaces complex mechanical/computational mechanisms (floating-point units) with simpler integer-based arithmetic operations that can be executed efficiently on standard microprocessors without dedicated floating-point hardware, thereby reducing device complexity while maintaining adequate measurement precision.
2Device complexity
If virtual sensor models are implemented on embedded platforms without floating-point hardware, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent changes the numerical representation parameter from floating-point to fixed-point format, which is natively supported by embedded platforms without floating-point hardware. This parameter change enables implementation on simple microprocessors while maintaining sufficient precision through careful selection of bit allocation for integer and fractional parts, ensuring adequate accuracy for sensor applications.
Solution Approach 2:
The patent adopts fixed-point arithmetic which can be implemented using simple, inexpensive integer arithmetic units that are already present in standard embedded microprocessors. This approach uses readily available computational resources rather than requiring expensive dedicated floating-point hardware, making the virtual sensor model accessible on cost-effective embedded platforms.
3Measurement precision
If conventional virtual sensor techniques are used, then sensing parameter derivation is achieved, but productivity decreases due to large computation requirements
Solution Approach 1:
The patent transforms the computational parameter from floating-point arithmetic to fixed-point arithmetic, which fundamentally changes the execution speed characteristics. Fixed-point operations can be performed much faster on standard processors since they use simple integer arithmetic instructions rather than complex floating-point operations, thereby dramatically improving real-time processing capability and productivity while maintaining adequate sensing parameter derivation accuracy.
Solution Approach 2:
The patent performs preliminary conversion of the virtual sensor model from floating-point to fixed-point representation during the offline model development phase. This preliminary action ensures that the model is pre-optimized for fast execution on embedded platforms, eliminating the need for runtime floating-point conversions and enabling immediate real-time processing without compromising sensing parameter derivation capability.
Data Source
AI summary
One aspect of the present disclosure includes a method for a control system of a machine. The method may include establishing a virtual sensor model indicative of interrelationships between at least one sensing parameter and a plurality of measured parameters related to the machine. The method may also include obtaining data and function information representing the virtual sensor model and converting the data information into fixed-point representation. Further, the method may include converting the function information into fixed-point representation and loading the converted fixed-point representation of data information and function information in the control system such that the control system uses the virtual sensor model in fixed-point arithmetic operation.