Research of amphibious unmanned vehicle control system based on neural network algorithm
In response to high-risk environments such as amphibious warfare in coastal areas that are not suitable for soldiers to charge forward,a control system design for amphibious unmanned boats based on neural network algorithms is proposed,and servo motion control is one of the key cores.Considering the current shortcomings of traditional motion control systems such as low control accuracy,large errors,and the need for manual parameter adjustment,the BP-PID neural network algorithm is proposed,which integrates the GWO algorithm and utilizes its search ability to optimize network weights and thresholds,accelerate network convergence,and improve control accuracy.Firstly,requirement analysis is conducted on the control system.Subsequently,the mathematical and control model design,neural network algorithm architecture,and other designs for the servo operation control system are completed.The designed algorithm is introduced into the motion control system of amphibious unmanned vehicles,and experimental verification is conducted to obtain the driving curve.The research has important practical significance and engineering value for achieving the upgrade of combat power to protect the safety of soldiers in high-risk unknown environments that are not suitable for soldiers to directly land near the sea.It provides reference for the intelligent research and future weapons and equipment.
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