A method of controlling an electrical actuator of a mechanical system having a plurality of nested zones of contact, the method comprising the steps of: acquiring data about the mechanical system, which system includes a number of nested zones of contact; preparing a model of the system on the basis of said data and of a number of LuGre models put in parallel equal to the number of nested zones of contact, and determining parameters of the model and also a compensation structure for compensating friction in the nested zones of contact; including the compensation structure in a control relationship for the actuator A; and controlling the actuator by means of the control relationship.

Methods for optimizing energy savings and reducing cycle time for mutating an industrial robotic path when a collision is detected. A method includes initializing a plurality of clone paths where a collision was detected, wherein a clone path is a clone of the initial path and the initial path comprises a source location, a plurality of intermediate locations, and a target location; for each clone path, determining a candidate path to store in a population, determining an optimal breed comprising the candidate path with an optimal rating, wherein the optimal rating is determined by the lowest breed rating in the population, and returning the optimal breed.

A method of controlling an electrical actuator of a mechanical system having a plurality of nested zones of contact, the method comprising the steps of: acquiring data about the mechanical system, which system includes a number of nested zones of contact; preparing a model of the system on the basis of said data and of a number of LuGre models put in parallel equal to the number of nested zones of contact, and determining parameters of the model and also a compensation structure for compensating friction in the nested zones of contact; including the compensation structure in a control relationship for the actuator A; and controlling the actuator by means of the control relationship.

The present invention provides a flexible job-shop scheduling method based on a limited stable matching strategy, and belongs to the field of job-shop scheduling. The method adopts the following design solution: a. generating an initial chromosome population through integer coding and initializing relevant parameters; b. conducting crossover and mutation operations on parent chromosomes to obtain progeny chromosomes; c. combining the progeny chromosomes and the parent chromosomes into a set of to-be-selected chromosomes, and selecting a next generation of chromosomes from the set through limited stable matching operation; and d. stopping the algorithm if meeting cut-off conditions; otherwise turning to step b. The present invention introduces a limited stable matching strategy into the selection process of the progeny chromosomes to solve a multi-target flexible job-shop scheduling problem, so as to overcome the defects of insufficient population distribution and insufficient convergence in the existing method for solving the multi-target flexible job-shop scheduling problem when solving such problem, thereby obtaining excellent scheduling solution with good timeliness and high reliability.

The present invention refers to a method for controlling a plant of separation and treatment industrial processes without chemical reaction using artificial intelligence and machine learning, aiming at improving revenues and profits obtained, as well as the performance of the system, and the technique can be applied in steps of conceptual design for a unit in operation, comprising the steps of: defining objectives and gains of the plant; delimiting the plant; evaluation in steady state of the plant; evaluation in dynamic state of the plant; and performing non-linear dynamic simulation of the plant.

The method includes collecting a schedule job list from a database, generating a plurality of schedules for a schedule job to be processed with respect to the schedule job list, calculating an evaluation index for the plurality of generated schedules, determining whether the calculated evaluation index for the plurality of schedules has reached a target evaluation index, selecting a schedule corresponding to two evaluation indices when the calculated evaluation index does not reach the target evaluation index and generating two new schedules using a genetic algorithm, and setting a selection probability so that a schedule having the highest evaluation index is selected and returning the selection probability to a user when the calculated evaluation index reaches the target evaluation index.

Disclosed are a method and apparatus for automatically scheduling jobs in computer numerical control machines using machine learning. The method includes collecting a schedule job list from a database, generating a plurality of schedules for a schedule job to be processed with respect to the schedule job list, calculating an evaluation index for the plurality of generated schedules, determining whether the calculated evaluation index for the plurality of schedules has reached a target evaluation index, selecting a schedule corresponding to two evaluation indices when the calculated evaluation index does not reach the target evaluation index and generating two new schedules using a genetic algorithm, and setting a selection probability so that a schedule having the highest evaluation index is selected and returning the selection probability to a user when the calculated evaluation index reaches the target evaluation index.

A method and system for batch scheduling uniform parallel machines with different capacities based on an improved genetic algorithm are provided. The method is to solve the batch scheduling problem of uniform parallel machines with different capacities. Jobs are distributed to machines by an improved genetic algorithm, and a corresponding batching strategy and a batch scheduling strategy are proposed according to the natural of the problem to obtain a fitness value of a corresponding individual; then, the quality of the solution is improved by a local search strategy; and, a crossover operation is performed on a population based on the fitness of the solution, and the population is continuously updated by repetitive iteration to eventually obtain an optimal solution.

The present disclosure provides techniques for improving aircraft machining process using machine learning-based predictive models and many-objective optimization algorithms. Data from a machining process is collected, where the data comprises a plurality of machining parameters and at least two performance metrics. For each respective performance metric, a respective predictive model is trained using one or more machine learning (ML) techniques, where the plurality of machining parameters are used as inputs, the respective performance metric as target outputs, and the respective predictive model learns to correlate the inputs to the target output. For each respective performance metric, a respective objective function is generated for the respective predictive model. Many-objective optimization is performed on the respective objective functions, and a set of solutions are generated for the machining process based on the many-objective optimization.