3results about How to "Guaranteed boundedness" patented technology

The invention discloses a self-adaptive control method of a multivariable nonlinear system. The method comprises the following steps: constructing the multivariable nonlinear system comprising unknown parameters and an unknown high-frequency gain matrix; the model not only describes system behaviors through state, input and output vectors, but also introduces an unknown parameter matrix and a smooth nonlinear function to simulate complexity and nonlinear characteristics of the system. And further constructing an adaptive controller and a switching mechanism, and determining a control direction matrix through the adaptive controller and the switching mechanism, so that the output of the multivariable nonlinear system can accurately track the reference signal. According to the method, by introducing the self-adaptive tracking control law design, the limiting condition of a high-frequency gain matrix is relaxed, it is ensured that the system performance is good when the direction matrix is controlled to be switched, and high-precision tracking of a multivariable nonlinear system reference signal is achieved.

This invention discloses a safety control method and system for unmanned surface vessels (USVs) based on an adaptive neural network observer, relating to the field of USV control technology. The method includes: constructing a second-order dynamic model of the USV using dynamic equations and obtaining its actual position and velocity state; designing an adaptive observer based on a neural network using the approximate nonlinear dynamics of a neural network to estimate the first-order system position state and the second-order system velocity state under DoS attacks and external disturbances; determining the position tracking error between the actual and estimated positions of the USV based on a preset desired trajectory and combining the actual and estimated positions, and constructing a virtual controller; designing a nonlinear filter based on the virtual control signal to obtain the trajectory tracking signal of the second-order velocity system, and then constructing an adaptive controller based on the estimated velocity state; and realizing the desired trajectory tracking of the USV under DoS attacks and disturbances based on the virtual controller and the adaptive controller.

The present invention discloses a motion control method for an underwater robot based on an RBF neural network, comprising the following steps: constructing an earth coordinate system, and constructing a dynamic model of the underwater robot in the coordinate system; designing a theoretical control law of the underwater robot based on the dynamic model; obtaining the actual control law of the underwater robot, and obtaining an inaccurate part model in the dynamic model based on the equivalent relationship between the actual control law and the theoretical control law; adaptively approximating the inaccurate part model based on an RBF neural network; correcting the theoretical control law based on the inaccurate part model after adaptive approximation to obtain a corrected control law; and controlling the motion of the underwater robot based on the corrected control law. The control method of the present invention can effectively improve the accuracy, robustness and adaptability of the control, and realize the posture stability and precise control during the operation of the underwater robot.