Escort Mission Cooperative Control Method and System Based on Obstacle Environment and Bounded Input

Abstract

The invention discloses a collaborative control method and system for escort missions based on obstacle environment and bounded input, including: using the Douller-Lagrangian system to describe the physical model of the escort mission; using an inner and outer loop control structure, and the outer loop adopts The behavior control method based on empty space generates the desired speed and desired motion trajectory required by the physical model of the inner ring; the inner ring is based on the proportional derivative sliding mode control method of the improved adaptive radial basis function neural network, so that in the presence of disturbances and parameters In the case of uncertainty, each physical model is able to track the desired velocity and desired motion trajectory, achieving zero steady-state error and bounded input. The controller that the present invention proposes has simple, model-free and can provide continuous control signal, the powerful learning ability and the robustness that the self-adaptive radial basis function neural network that adopts in the controller design have, very well The disturbance is compensated and finally zero steady-state error is achieved.

Description

technical field [0001] The invention relates to the technical field of collaborative control of escort missions, in particular to a method and system for collaborative control of escort missions based on obstacle environments and bounded inputs. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] The problem of coordinated control of Multi-Euler Lagrangian systems has attracted extensive attention in the past few decades. Mobile robots, underwater vehicles, surface vessels, spacecraft, robotic arms, etc. all belong to Eulerian Lagrangian systems. Due to measurement noise, modeling errors, external disturbances and modeling simplifications, and often unmodeled dynamics in real systems, these can severely degrade system performance and lead to control system instability. Furthermore, neglecting to deal with practical issues such as input satur...

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Problems solved by technology

However, the above method is mainly applied to a single controlled object, such as a robotic arm or a quadrotor aircraft, and the above method has not been applied to multiple controlled objects.

In addition, none of the above methods considers the issue of bounded input, and thinks that the actuator can generate arbitrary torque, which is contradictory to the fact that the torque provided by the actuator is bounded.

Therefore, in practical application, the existing technology either needs to adopt a huge driving mechanism to generate a large torque or cannot be practically applied at all.

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