44 results about "Uncertain systems" patented technology

A learning computer system may include a data processing system and a hardware processor and may estimate parameters and states of a stochastic or uncertain system. The system may receive data from a user or other source. Parameters and states of the stochastic or uncertain system are estimated using the received data, numerical perturbations, and previous parameters and states of the stochastic or uncertain system. It is determined whether the generated numerical perturbations satisfy a condition. If the numerical perturbations satisfy the condition, the numerical perturbations are injected into the estimated parameters or states, the received data, the processed data, the masked or filtered data, or the processing units.

A vehicle stability control method for a traveling system with uncertainties which Contains the following steps: Collecting vehicle driving state information, according to the two-degree-of-freedom model, expressing The uncertainties of the analytical system of the vehicle model as linear uncertain systems, Taking the deviation between the actual value and the expected value of the state quantityas the input of the robust optimization vehicle stability controller, obtaining the expected value of the control quantity required for vehicle stability by using the suboptimal control method, and distributing the tire force by the pseudo-inverse distribution method, and finally executing the tire force distribution result by the actuator.

The invention discloses a method for setting parameters for predictive control of uncertain system model based on machine learning, comprising the following steps: 1) obtaining an m-dimensional output weight matrix Q and an n-dimensional input weight matrix R in the cost function of the system model predictive control; 2) Obtain the output sequence and mn×L 3 Group performance indicators, in which each group of performance indicators is a set of column vectors constructed from the output overshoot and adjustment time. For each group Q and R, the worst overshoot and worst adjustment time are obtained respectively, and then the The worst overshoot and worst adjustment time taken are stored in the matrix F; 3) Build an RBF neural network, and then use the established RBF neural network to calculate the optimal performance index; 4) Build a BP neural network, and then use the BP neural network to find Take the performance label; 5) Using the performance label as the basis for optimization, the PSO optimization algorithm is used to adjust the model predictive control parameters of the uncertain system. This method can more accurately realize the setting of the model predictive control parameters of the uncertain system.

Aiming at the control problems of non-affine nonlinear uncertain systems, a Two-Speed ​​Adaptive Proportional-Derivative (2SAPD) control method that does not depend on the plant model is invented. The control method of the invention defines the system dynamics and internal and external uncertainties as the sum disturbance, thereby transforming the non-affine nonlinear uncertain system into a linear uncertain system, and then constructs the error dynamic system under the excitation of the sum disturbance, according to which A two-speed adaptive proportional-derivative controller model with center speed factor as the core is designed. Theoretical analysis shows that the closed-loop control system composed of 2SAPD controller not only has global asymptotically stable robustness, but also has good anti-disturbance robustness. The invention has wide application value in the fields of electric power, machinery, chemical industry, transportation, aviation, aerospace and the like.