Abstract: The present invention discloses an iterative learning control (ILC) method for a multi-particle vehicle platoon driving system, and relates to the field of ILC. The method includes: firstly, discretizing a multi-particle train dynamic equation using a finite difference method to obtain a partial recurrence equation, and then transforming the partial recurrence equation into a spatially interconnected system model; secondly, transforming the spatially interconnected system model into an equivalent one-dimensional dynamic model using a lifting technology, and in order to compensate input delay, designing an ILC law based on a state observer; and thirdly, transforming a controlled object into an equivalent discrete repetitive process according to the ILC law, and converting a controller combination problem into a linear matrix inequality based on stability analysis of the repetitive process.

Abstract: In implementations of the present disclosure, there is provided a method for access point (AP) validation. The method comprises detecting a trigger event for validating a network including a plurality of APs, and selecting a target AP from the plurality of APs based on a neighbor table of an AP associated with the detected trigger event. The method further comprises determining a validation case corresponding to the detected trigger event from a knowledge base. The method further comprises sending the validation case to the target AP, and, receiving a result of performance of the validation case from the target AP in response to the validation case being performed. Implementations of the present disclosure can validate whether the network change takes effect in the network automatically, and can improve the validation efficiency of the network changes.

Abstract: The present invention discloses an iterative learning control (ILC) method for a multi-particle vehicle platoon driving system, and relates to the field of ILC. The method includes: firstly, discretizing a multi-particle train dynamic equation using a finite difference method to obtain a partial recurrence equation, and then transforming the partial recurrence equation into a spatially interconnected system model; secondly, transforming the spatially interconnected system model into an equivalent one-dimensional dynamic model using a lifting technology, and in order to compensate input delay, designing an ILC law based on a state observer, and thirdly, transforming a controlled object into an equivalent discrete repetitive process according to the ILC law, and converting a controller combination problem into a linear matrix inequality based on stability analysis of the repetitive process.