A Specified Decay Rate Nonfragile Optimal Control Method for Lipschitz Nonlinear Systems

Abstract

The invention discloses a non-fragile optimal control method for a specified decay rate of a Lipschitz nonlinear system, including: 1) a sensor samples the state of a Lipschitz nonlinear system with model uncertainty, quantifies it and transmits it to a remote control 2) Using the Markov chain to describe the data transmission process, the controller adopts a corresponding control strategy according to the received data; 3) Describe the closed-loop system as a Markov jump system, based on the system model and system performance indicators , using linear matrix inequalities to design a nonfragile infinite-time optimal controller with a given decay rate. The present invention aims at the design problem of the non-fragile infinite time optimal controller with specified decay rate of the Lipschitz nonlinear system with quantized and incomplete measurement data, and provides the design method of the optimal controller, so that the system is stable according to the specified decay rate, At the same time, 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 a specified decay rate of a Lipschitz nonlinear system. Background technique [0002] Lipschitz systems are a common class of nonlinear systems that can describe many physical processes. For example, there are robotic systems with sinusoidal terms, nonlinear systems that satisfy Lipschitz conditions in a given interval, etc. When the sensor, controller, and actuator components of the system are distributed in a remote physical space, information can be transmitted over the network. The network-based control system has the advantages of low cost, good scalability and convenient installation and maintenance, and has become the trend of control system development. When the sensor measures and samples the state of the controlled system and converts it into a digital signal, it needs to be quantified. When the quantitative data is transmitted ...

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

3. There is no guarantee that the system state will decay at the desired rate

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