Neural network-based energy-input shaping combined control for anti-sway and positioning of crane systems under non-zero initial conditions
Portal bridge cranes, as typical underactuated systems, have wide industrial applications. However, in practical operations, the initial swing angle and angular velocity of the crane inevitably exist, which degrades the performance of these methods in real-world applications. This paper proposes a control method based on the combination of energy control, input shaping, and neural network, in order to solve the problem of anti-swing and positioning of portal bridge cranes under non-zero initial conditions. Through the energy control method based on Lyapunov theory, the initial swing angle is quickly suppressed, combined with the optimized four pulse input shaping method to ensure the positioning accuracy of the trolley, and the neural network is used to adaptively adjust the control parameters, which overcomes the problems of sensitivity to initial conditions and insufficient adaptability of the traditional methods. Numerical simulations and physical experiments show the effectiveness of the hybrid method suggested.