Non-fragile optimal control method for Lipschitz nonlinear system

Abstract

The invention discloses a non-fragile optimal control method for a Lipschitz nonlinear system. The method comprises the steps of 1) enabling a sensor to sample the state of the Lipschitz nonlinear system with model uncertainty, employing a logarithm quantizer to convert the state into a digital quantity, and transmitting the digital quantity to a far-end controller; 2) describing the data transmission process by adopting a Markov chain, and adopting a corresponding control strategy by the controller according to the data receiving condition; and 3) describing the closed-loop system as a Markovjump system, and designing a non-fragile infinite time optimal controller by using a linear matrix inequality based on the system model and performance indexes of the system. Aiming at the design problem of the non-fragile infinite time optimal controller of the Lipschitz nonlinear system with quantitative and incomplete measurement data, the invention provides the design method of the optimal controller, so that the system is stable, and the performance index functional takes the minimum value.

Description

technical field [0001] The invention relates to the field of optimal control, in particular to a non-fragile optimal control method for Lipschitz nonlinear systems. Background technique [0002] Lipschitz system is a kind of common nonlinear system, which can describe many physical processes. For example, robotic systems with sinusoidal terms, nonlinear systems satisfying Lipschitz conditions in a given interval, etc. When the sensor, controller, and actuator components of the system are distributed in a distant physical space, information transmission can be carried out through the network. The network-based control system has the advantages of low cost, good scalability, and convenient installation and maintenance, and has become the development trend of the control system. When the sensor measures and samples the state of the controlled system and converts it into a digital signal, it needs to be quantified. During the process of transmitting quantitative data to the c...

AI Technical Summary

Problems solved by technology

Due to the influence of factors such as analog-to-digital conversion accuracy and word length, there are uncertainties when the controller is implemented by digital devices

However, the existing optimal control of Lipschitz nonlinear systems has the following problems: 1. The uncertainty of the system model is not considered, and the control performance of the actual system will be affected

2. Factors such as conversion accuracy and word length are not considered when the controller is implemented, and the non-fragility is poor

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