Adaptive Neural Network Tracking Control for Double-Pendulum Tower Crane Systems With Nonideal Inputs
针对双摆塔式起重机模型，提出一种自适应神经网络跟踪控制方法，同时处理鲁棒性、跟踪误差限制、双摆效应和输入死区非线性，使吊臂和小车快速跟踪轨迹，消除吊钩和负载摇摆。
A novel adaptive neural network tracking control method is systematically investigated for a unique double-pendulum tower crane system model in this article. Several critical and practical application-oriented control issues, including robustness, tracking error limitation, double-pendulum effects, and input dead zone nonlinearity, are considered simultaneously, which have never been well addressed in the existing literature. Technically, neural networks are employed to approximate the functions with uncertain/unknown dynamics and nonideal inputs. Several barrier Lyapunov functions are proposed that can circumvent the violation of tracking error limitations in the proposed control method. Importantly, based on the designed adaptive neural network tracking control method, the jib and trolley can track their desired trajectories very fast, and the hook and payload sway can be completely eliminated. The Lyapunov stability theory and Babalat’s lemma are utilized to theoretically prove the convergence and stability of the proposed control system. Finally, well-designed simulation studies are carried out to verify the excellent performance and strong robustness of the control method. This article should be the first work considering a double-pendulum tower crane system with guaranteed convergence and performance without any linearization for the original nonlinear dynamic model.