Dynamics and Embedded Internet of Things Input Shaping Control for Overhead Cranes Transporting Multibody Payloads

Abstract

Input shaping is an Optimal Control feedforward strategy whose ability to define how and when a flexible dynamical system defined by Ordinary Differential Equations (ODEs) and computer controlled would move into its operative space, without command induced unwanted dynamics, has been exhaustively demonstrated. This work examines the issue of Embedded Internet of Things (IoT) Input Shaping with regard to real time control of multibody oscillatory systems whose dynamics are better described by differential algebraic equations (DAEs). An overhead crane hanging a double link multibody payload has been appointed as a benchmark case; it is a multibody, multimode system. This might be worst scenario to implement Input Shaping. The reasons can be found in the wide array of constraints that arise. Firstly, the reliability of the multibody model was tested on a Functional Mock-Up Interface (FMI) with the two link payload suspended from the trolley by comparing the experimental video tapping signals in time domain faced with the signals extracted from the multibody model. The FFTs of the simulated and the experimental signal contain the same frequency harmonics only with somewhat different power due to the real world light damping in the joints. The application of this approach may be extended to other cases i.e., the usefulness of mobile hydraulic cranes is limited because the payload is supported by an overhead cable under tension that allows oscillation to occur during crane motion. If the payload size is not negligible small when compared with the cable length may introduce an additional oscillatory mode that creates a multibody double pendulum. To give the insight into the double pendulum dynamics by Lagrangian methods two slender rods as payloads are analyzed dealing with the overhead crane and a composite revolute-revolute joint is proposed to model the cable of the hydraulic crane, both assumptions facilitates an affordable analysis. This allows developing a general study of this type of multibody payloads dynamics including its normal modes, modes ratios plus ranges of frequencies expected. Input Shapers were calculated for those multimodes of vibration by convolving Specified Insensitivity (SI) shapers for each mode plus a novel Direct SI-SI shaper well suited to reduce the computational requirements, i.e., the number of the shaper taps, to carry out the convolution sum in real time by the IoT device based on a single microcontroller working as the command generator. Several comparisons are presented for the shaped and unshaped responses using both the multibody model, the experimental FMI set-up and finally a real world hydraulic crane under slewing motion commanded by an analog Joystick connected by two RF modules 802.15.4 to the IoT device that carry out the convolution sum in real time. Input Shaping improves the performances for all the cases.

Multi-Gene Genetic Programming-Based Identification of a Dynamic Prediction Model of an Overhead Traveling Crane

“…Since transient oscillations can be dangerous to both the surroundings and the payload itself, an effective dampening of oscillations has become a focus of research. Effective positioning, as well as residual oscillations, decrease the time spent on loading and unloading [ 1 ]. As an underactuated mechanical system, the sway cannot be arbitrarily controlled, requiring a need for the development of effective control strategies.…”

“…A thorough review of various methods for crane dynamic modeling and control reported in the literature up to 2001 is presented in [ 2 ], while more current, state-of-the-art methods (up to 2016) are discussed in [ 3 ]; however, most of the presented approaches are derived from the Euler–Lagrange equation. Other methods that have been applied to modeling material handling systems include Takagi–Sugeno fuzzy models [ 4 , 5 ], bond graph methods [ 6 ], multi-body dynamics [ 1 , 7 ] and neural networks [ 8 ]. Evolutionary algorithms have been implemented in a variety of crane applications including anti-sway crane control [ 9 ], scheduling [ 10 , 11 ] and proactive maintenance [ 12 ].…”

Global-Equivalent Sliding Mode Control Method for Bridge Crane

“…Representative methods of open-loop control include optimal control [1], [2], trajectory planning [3], [4] and input shaping control [5], [6], yet whose strong dependence and low robustness hinder their application in real-world practices. For this reason, a series of closed-loop control methods have been suggested by numerous scholars, including dynamic PID control [7], fuzzy PID control [8], adaptive neural network control [9], etc.…”

An energy-time optimal autonomous motion control framework for overhead cranes in the presence of obstacles

“…Various control techniques for overhead cranes are proposed, and they are categorized into open-loop control and closed-loop control according to whether feedbacks are integrated. Open-loop control techniques for overhead crane includes path planning, 8–10 input shaping, 11,12 filters, 13 etc. The main drawback of these open-loop techniques is that they cannot cope with various external disturbances that exist in the actual operational environment.…”