Multi-target task control method

Abstract

The invention relates to a multi-target task control method. Through one or more neural networks, a secondary control target is achieved to the maximum degree based on achievement of a main control target. A sensor detects the external environment to obtain a set of observation values, and a first control quantity is obtained through feeding back the observation values to a main controller or thefirst neural network; the first control quantity is used to reach the main control target; the observation values are fed back to a neural network auxiliary system, the first control quantity is transmitted to the neural network auxiliary system, and a second control quantity for achieving the secondary control target to the maximum degree is calculated by the neural network auxiliary system without affecting achievement of the main control target; output is performed through a second control quantity function system, the observation values obtained by the sensor detecting the external environment are fed back to the main controller, and previous steps are repeated.

Description

technical field [0001] The invention belongs to the technical field of automatic control, and in particular relates to a multi-objective task control method. Background technique [0002] Multi-objective control refers to the simultaneous control of two or more interdependent objects. In a multi-objective control system, there are multiple control quantities and multiple observation quantities due to multiple target quantities, and the target quantities vary nonlinearly with the control quantities. The existing multi-objective control scheme adjusts the control system parameters based on a large number of experiments and the experience of engineers, so it is relatively difficult to adjust the parameters. Moreover, the equipment itself is aging or the working environment of the equipment is inconsistent with the laboratory environment, resulting in the control results not being optimal, so the adaptability is poor. [0003] as attached figure 1 In the traditional multi-obj...

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Problems solved by technology

However, the equivalent method often needs to pay a higher storage cost and is difficult to adjust. It is necessary to update the member functions in the function collection or each tuple in the lookup table one by one.

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