Wave-Based Control of a Crane System With Complex Loads

2017 ◽  
Vol 139 (8) ◽  
Author(s):  
Jiao Zhou ◽  
Kai Zhang ◽  
Gengkai Hu
Keyword(s):  
Wave Scattering ◽  
Control Method ◽  
Model Simulation ◽  
Double Pendulum ◽  
Time Varying ◽  
Pendulum Model ◽  
Simulation And Experiment ◽  
Special Case ◽  
Transmission And Reflection ◽  
Load Mass

In the framework of wave-based method, we have examined swing motion control for double-pendulum and load-hoist models. Emphases are placed on wave scattering by the middle load mass in the double-pendulum model and on time-varying configuration in the load-hoist model. By analyzing wave transmission and reflection, trolley's motion to alleviate swing is designed by absorbing reflected wave through adjusting the velocity of trolley. Simulation and experiment are also conducted to validate the proposed control method. The results show that with the designed trolley's motion swings of load can be significantly reduced for both double-pendulum model, suspended rod model which is demonstrated a special case of double-pendulum model, and load-hoist model. Simulation results agree well with the experimental measurement. Launch velocity profiles may have important impact on motion design, especially on force necessary to displace trolley. Finally, a wave-based feedback control is also discussed to demonstrate the flexibility of method.

A single and double closed-loop compound anti-sway control method for double-pendulum bridge cranes locating at random position

2021 ◽  
pp. 014233122110464
Author(s):  
Minghui Xia ◽  
Xiaokai Wang ◽  
Qingxiang Wu ◽  
Lin Hua
Keyword(s):  
Closed Loop ◽  
Control Method ◽  
Smooth Transition ◽  
Double Pendulum ◽  
Bridge Crane ◽  
Special Equipment ◽  
Pendulum Model ◽  
Bumpless Transfer ◽  
Random Position ◽  
Pendulum Dynamics

In the assembly workshops of some heavy special equipment, the bridge cranes for payload lifting often needs to be located frequently. However, the locating position is often determined by the operator, which is random and results in significant payload oscillation and difficulties in trolley positioning. Furthermore, in practice, the bridge crane always exhibits more complicated double-pendulum dynamics compared with single-pendulum crane. To solve these problems, this paper establishes the double-pendulum model of bridge crane. Derived from the proportional-derivative (PD) control, the single closed-loop is designed based on the hook oscillation during acceleration and transporting; when locating, the double closed-loop is presented by utilizing the position and the hook oscillation. Combining the two control methods, a single and double closed-loop compound anti-sway control (SDCAC) method for the bridge crane is proposed. On this basis, to improve the performance of the SDCAC system, the sequential quadratic optimization (SQP) method is adopted to optimize PD parameters. Besides, a novel bumpless transfer control method is proposed to realize the smooth transition between the two control modes. Finally, the simulations and experiments are conducted. The results demonstrate the effectiveness of the proposed method.

scholarly journals Adaptive Neural Networks Control Using Barrier Lyapunov Functions for DC Motor System with Time-Varying State Constraints

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Ma ◽  
Dapeng Li
Keyword(s):  
State Constraints ◽  
Control Method ◽  
Time Varying ◽  
Control Approach ◽  
Dc System ◽  
Full State ◽  
Constraint Problem ◽  
Barrier Lyapunov Functions ◽  
Special Case ◽  
Full State Constraints

This paper proposes an adaptive neural network (NN) control approach for a direct-current (DC) system with full state constraints. To guarantee that state constraints always remain in the asymmetric time-varying constraint regions, the asymmetric time-varying Barrier Lyapunov Function (BLF) is employed to structure an adaptive NN controller. As we all know that the constant constraint is only a special case of the time-varying constraint, hence, the proposed control method is more general for dealing with constraint problem as compared with the existing works on DC systems. As far as we know, this system is the first studied situations with time-varying constraints. Using Lyapunov analysis, all signals in the closed-loop system are proved to be bounded and the constraints are not violated. In this paper, the effectiveness of the control method is demonstrated by simulation results.

Predictive Model for BOF Steelmaking Using RBF Neural Network

2011 ◽  
Vol 58-60 ◽  
pp. 1214-1218
Author(s):  
Ya Ping Zhu ◽  
Xiao Dong Zhao ◽  
Sheng Lin Xu
Keyword(s):  
Neural Network ◽  
Predictive Model ◽  
Input Data ◽  
Control Method ◽  
Model Simulation ◽  
Rbf Neural Network ◽  
Receding Horizon Control ◽  
Effective Control ◽  
Steel Temperature ◽  
Simulation And Experiment

Effective control of the endpoint steel temperature and contents of carbon, sulphur, etc. is one of the main tasks of BOF steelmaking process. This paper established a multivariable predictive model for BOF steelmaking using RBF neural network. The input data is pretreated and standardized. Receding horizon control method is used to increase the accuracy of the model. Simulation and experiment comparisons show that the model is validated and has high hit rate.

A novel robust finite-time tracking control of uncertain robotic manipulators with disturbances

2020 ◽  
pp. 107754632098244
Author(s):  
Hamid Razmjooei ◽  
Mohammad Hossein Shafiei ◽  
Elahe Abdi ◽  
Chenguang Yang
Keyword(s):  
Finite Time ◽  
Control Method ◽  
Sliding Mode ◽  
Robotic Manipulators ◽  
Sliding Mode Controller ◽  
Time Varying ◽  
State Variables ◽  
Terminal Sliding Mode ◽  
Control Laws ◽  
Finite Time Boundedness

In this article, an innovative technique to design a robust finite-time state feedback controller for a class of uncertain robotic manipulators is proposed. This controller aims to converge the state variables of the system to a small bound around the origin in a finite time. The main innovation of this article is transforming the model of an uncertain robotic manipulator into a new time-varying form to achieve the finite-time boundedness criteria using asymptotic stability methods. First, based on prior knowledge about the upper bound of uncertainties and disturbances, an innovative finite-time sliding mode controller is designed. Then, the innovative finite-time sliding mode controller is developed for finite-time tracking of time-varying reference signals by the outputs of the system. Finally, the efficiency of the proposed control laws is illustrated for serial robotic manipulators with any number of links through numerical simulations, and it is compared with the nonsingular terminal sliding mode control method as one of the most powerful finite-time techniques.

Decoupling Control for Bearingless Synchronous Reluctance Motor Based on Neural Networks Inverse

2012 ◽  
Vol 150 ◽  
pp. 30-35
Author(s):  
Ze Bin Yang ◽  
Huang Qiu Zhu ◽  
Xiao Dong Sun ◽  
Tao Zhang
Keyword(s):  
Neural Networks ◽  
Control Method ◽  
Decoupling Control ◽  
Performance Simulation ◽  
Inverse Control ◽  
Linear Control Theory ◽  
Synchronous Reluctance Motor ◽  
Reluctance Motor ◽  
Static Performance ◽  
Simulation And Experiment

A novel decoupling control method based on neural networks inverse system is presented in this paper for a bearingless synchronous reluctance motor (BSRM) possessing the characteristics of multi-input-multi-output, nonlinearity, and strong coupling. The dynamic mathematical models are built, which are verified to be invertible. A controller based on neural network inverse is designed, which decouples the original nonlinear system to two linear position subsystems and an angular velocity subsystem. Furthermore, the linear control theory is applied to closed-loop synthesis to meet the desired performance. Simulation and experiment results show that the presented neural networks inverse control strategy can realize the dynamic decoupling of BSRM, and that the control system has fine dynamic and static performance.

Sign in / Sign up

Export Citation Format

Share Document