5 results about "Fractional order calculus" patented technology

The invention discloses a PMSM (permanent magnet synchronous motor) control method for a mining electric locomotive. The method comprises a cooperative control framework of a fractional order sliding mode controller and a nonlinear extended state observer. Wherein the fractional order sliding mode controller constructs a novel sliding mode surface by introducing a terminal factor and a fractional order calculus theory, and introduces a state variable to design an adaptive fractional order reaching law, so that buffeting is effectively inhibited and controlled while system error convergence is accelerated; and the nonlinear extended state observer is used for estimating the uncertainty of a system model and the disturbance of an external load in real time, and introducing the uncertainty and the disturbance as feedforward compensation quantity into a control loop, so that the anti-interference capability of the system is remarkably enhanced. Through deep fusion of the fractional calculus theory, the sliding mode control and the nonlinear extended state observer, the problems of large buffeting, slow response, poor anti-interference capability and the like of a traditional control method of the mining electric locomotive under complex working conditions are solved, and the dynamic and steady state performance and the control precision of the system are remarkably improved.

This invention relates to the field of voice coil motor servo control technology, specifically to a fractional-order high-order sliding mode control method for voice coil motors based on a third-order superspiral sliding mode observer (STSMO) and an adaptive reaching law. The method includes the following steps: S1, establishing a mathematical model of the voice coil motor, deriving the dynamic equations of the voice coil motor including parameter uncertainties and external disturbances, defining the position tracking error, and deriving the error dynamic equation; S2, designing a fractional-order high-order integral sliding surface, combining it with an improved Oustaloup filter to achieve rational function approximation of the fractional-order calculus operator, and constructing the first-order derivative equation of the sliding surface; S3, designing an adaptive hyperbolic tangent reaching law that integrates dynamic feedback of the error amplitude, and deriving the initial expression of the sliding mode control law based on the first-order derivative equation of the sliding surface and the adaptive hyperbolic tangent reaching law. This invention achieves real-time accurate observation and feedforward compensation of lumped disturbances through a third-order superspiral sliding mode observer (STSMO).

The present application relates to the technical field of unmanned aerial vehicle flight control, and particularly relates to a three-dimensional terminal game control method for unmanned aerial vehicle based on deep reinforcement learning, comprising: initializing a model and a maneuvering constraint boundary, and collecting a local and enemy state vector; performing fractional order calculus on the three-dimensional position of the enemy to extract a fractional order derivative vector, and splicing the fractional order derivative vector with the state vectors of both sides to generate a non-Markov state vector; inputting the non-Markov state vector into a strategy network to solve a three-dimensional action vector; performing physical mapping according to the maneuvering constraint boundary to generate a aerodynamic instruction to drive state transition, and obtaining a next time state vector; combining the foregoing vectors to construct a tracking error and a Lyapunov function, calculating a total derivative and performing divergence punishment to generate a Lyapunov reward value; storing a transition tuple into a replay pool to update model parameters in a closed loop, and outputting an optimal strategy. The present application solves the problems of prediction lag and easy divergence off-target when chasing a high dynamic target.

This invention provides a battery thermal management control method based on a fractional-order thermal model and a rolling time-domain chimpanzee algorithm. It introduces fractional-order calculus theory to reconstruct the battery's thermal dynamic equations, capturing the memory characteristics of the thermal system with high fidelity; constructs a rolling time-domain control architecture to achieve proactive prediction and intervention of future thermal states; and replaces the traditional iterative algorithm with an improved chimpanzee optimization algorithm that integrates a lens imaging back-learning mechanism. This invention aims to systematically innovate from three dimensions: mechanism modeling, control architecture, and optimization algorithm, thereby achieving multi-objective synergistic optimization of system energy efficiency, temperature control accuracy, and temperature uniformity while ensuring the safe operation boundary of the battery.

The application provides a fractional calculus energy reduction guiding method for enhancing deep Transformer-Attention integrated prediction, which is composed of a Transformer-Attention network, an efficient time series prediction network combined with a time series attention unit and a fractional order random dynamic calculus controller. The method considers the energy consumption of a comprehensive energy system, takes energy consumption influencing factors and energy production as inputs, and outputs an optimal energy reduction guiding signal. The Transformer-Attention network and the efficient time series prediction network combined with the time series attention unit in the method can solve the prediction problem of benchmark energy consumption, and are used for outputting an optimal prediction result; the fractional order random dynamic calculus controller in the method can obtain an optimal energy reduction guiding signal through the predicted energy consumption and energy production. The method can reduce the overall energy consumption of the comprehensive energy system and improve the stability of the comprehensive energy system.