A-controller-enabling-precise-positioning-and-sway-reduction-in-bridge-and-gantry-cranes-起重机设计.docx

ARTICLE IN PRESS
K.L. Sorensen et al. / Control Engineering Practice 15 (2007) 825–837 837
A controller enabling precise positioning and sway reduction in bridge and gantry cranes
Khalid L. Sorensen, William Singhose, Stephen Dickerson
The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 813 Ferst Dr., MARC 257, Atlanta, GA 30332-0405, USA
Received 28 September 2005; accepted 30 March 2006
Available online 5 June 2006
Abstract
Precise manipulation of payloads is difficult with cranes. Oscillation can be induced into the lightly damped system by motion of the bridge or trolley, or from environmental disturbances. To address both sources of oscillation, a combined feedback and input shaping controller is developed. The controller is comprised of three distinct modules. A feedback module detects and compensates for positioning error; a second feedback module detects and rejects disturbances; input shaping is used in a third module to mitigate motioninduced oscillation. An accurate model of vector drive and AC induction motors, typical to large cranes, was used jointly with a deconvolution analysis technique to incorporate the nonlinear dynamics of crane actuators into the control design. The controller is implemented on a 10-ton bridge crane at the Georgia Institute of Technology. The controller achieves good positioning accuracy and significant sway reduction. r 2006 Elsevier Ltd. All rights reserved.
Keywords: Input shaping; Command shaping; Crane control; Oscillation control; Anti-sway; Bridge crane; Gantry crane

1. Introduction
Bridge and gantry cranes occupy a crucial role within industry. They are used throughout the world in thousands of shipping yards, construction sites, steel mills, warehouses, nuclear power and waste storage facilities, and other industrial complexes. The timeliness and effectiveness of this manipulation system are important contributors to industrial productivity. For this reason, improving the operational effectiveness of cranes can be extremely valuable.
These structures, like the one shown in Fig. 1, are highly flexible in nature. External disturbances, such as wind or motion of the overhead support unit (e.g. the bridge or trolley), can cause the payload to oscillate. In many applications these oscillations have adverse consequences. Swinging of the payload or hook makes precision positioning time consuming and inefficient for a human operator; furthermore, when the payload or surrounding

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E-mail address: [e-mail] (W. Singhose).
0967-0661/$-see front matter r 2006 Elsevier Ltd. All rights reserved.
doi:10.1016 /j.conengprac. 2006.03.005
obstacles are of a hazardous or fragile nature, the oscillations may present a safety risk (Khalid, Singhose, Huey, & Lawrence, 2004).
The broad usage of bridge and gantry cranes, coupled with the need to control unwanted oscillations, has motivated a large amount of research pertaining to the control of these structures. Engineers have sought to improve the ease-of-use, increase operational efficiency, and mitigate safety concerns by addressing three primary aspects of crane systems: (1) motion-induced oscillation; (2) disturbance-induced oscillation; and (3) positioning capability.
Singer et al. reduced motion-induced oscillations of a 15-ton bridge crane by using robust input shaping techniques (Singer, Singhose, & Kriikku, 1997). Fang et al. proposed to control final trolley position and motioninduced oscillation through proportional-derivative ( PD ) control in which the coupling between the cable angle and the motion of the trolley was artificially increased (Fang, Dixon, Dawson, & Zergeroglu, 2001). Piazzi proposed a
dynamic-inversion-based control for reducing transient and residual motion-induced oscillation (Piazzi & Visioli,

Fig. 1. Picture of a gantry crane at a shipping dock (courtesy of Favelle Favco Cranes).
2002). Kim implemented a pole-placement strategy on a real container crane to control motion and disturbanceinduced oscillation, as well as final positioning (Kim, Hong, & Sul, 2004). Moustafa developed nonlinear control laws for payload trajectory tracking based on a Lyapunov stability analysis (Moustafa, 2001). O’Connor developed a control strategy based on mechanical wave concepts that involves learning unknown dynamics through an initial trolley motion (O’Connor, 2003). Finally, Fliess used a generalized state variable model suggested in (Fliess, 1989), and then proposed a linearizing feedback control law (Fliess, Levine, & Rouchon, 1991). The position of the trolley and length of the payload cable were the controlled variables, and their respective reference trajectories were limited to a class of fourthorder polynomials to insure minimal payload sway. The control did not attempt to eliminate disturbance-induced oscillations.
The control schemes developed in the literature may be broadly grouped into three categories: time-optimal control, command shaping, and feedback control. The implementation of these various control methods presents several challenges. A common difficulty is the behavior of drives and motors that actuate crane motion. Nonlinear behavior of these elements have been difficult to evaluate with traditional analysis techniques, and are, therefore, often neglected in controller designs.
A drawback related to time-optimal control is its inability to be implemented in real-time, owing to the necessity of precomputation of system trajectories. There is no known implementation of a time-optimal control scheme used with a commercial crane (Gustafsson & Heidenback, 2002).
Command shaping is a reference signal modification technique that is implementable in real time (Singer & Seering, 1990). However, command shaping does not have the closed-loop mechanisms of feedback control, and must, therefore, be used in conjunction with a feedback control if it is to be used for disturbance rejection.
Feedback control is the most common strategy used in research efforts to mitigate positioning and cable sway errors. This type of control is aptly suited for positioning a bridge or trolley. However, when a feedback controller must minimize cable sway, the control task becomes much more problematic. Accurate sensing of the payload must be implemented, which is often costly or difficult. These difficulties are discussed in detail for fully automatic commercial cranes in use at the Pasir Panjang terminal in Singapore (Gustafsson & Heidenback, 2002). Furthermore, feedback control schemes are somewhat slow because they are inherently reactive. For example, when feedback is utilized to control cable sway, cable sway must be present in the system before the control will attempt to eliminate the undesired oscillations.
One of the most useful techniques used for negating a system’s flexible modes is input shaping. Input shaping does not require the feedback mechanisms of closed-loop controllers. Instead, the control reduces oscillations in an anticipatory manner, as opposed to the reactive manner of feedback. Oscillation suppression is accomplished with a reference signal that anticipates the error before it occurs, rather than with a correcting signal that attempts to restore deviations back to a reference signal. In the context of crane control, this means that sensing cable sway is not necessary. As a result, input shaping is easier to implement than feedback schemes. In many instances, input shaping techniques are also amenable to hard nonlinearities present in actuating drives and motors. Input shaping techniques have proven effective at significantly reducing motioninduced cable sway during crane motion (Kenison & Singhose, 1999; Lewis, Parker, Driessen, & Robinett, 1998; Singer et al., 1997; Singhose, Porter, Kenison, & Kriikku, 2000). Cranes utilizing the input shaping control also exhibited a significant improvement in efficiency and safety (Khalid et al., 2004).
The controller developed in this paper has been designed with positioning and oscillation suppression properties by merging feedback control with input shaping. The control is comprised of distinct modules that have been combined into a unified control architecture. Each module is designed to control one aspect of the crane’s performance. A feedback control module is used to position the payload at a desired location, while a different feedback module rejects disturbances. Input shaping is used in a third module to minimize motion-induced oscillation.
This strategy of allocating individual control tasks to individual controllers is similar to the approach used by Sorensen, Singhose, and Dickerson (2005). The strategy helps mitigate design and implementation difficulties discussed previously by utilizing the strengths of the different control modules for tasks they are most suited for, feedback for positioning and disturbance rejection, and input shaping for reducing motion-induced oscillation in the presence of hard nonlinearities. The performance of the control is evaluated experimentally on a 10-ton bridge crane located in the Manufacturing Research Center (MARC) at the Georgia Institute of Technology ( Georgia Tech).
The following section provides a synopsis of crane system dynamics that incorporates the effects of nonlinear drives and motors. Section 3 presents a brief overview of the overall control system followed by a more detailed description of the individual modules comprising the controller. The stability of the two closed-loop modules is also discussed. Section 4 explains how the distinct modules are combined into a single control architecture, and examines the stability of the combined architecture. Section 5 contains concluding remarks. Experimental data are used throughout the paper to demonstrate key aspects of the control system.
2. Crane dynamics
The 10-ton MARC crane used to test the control described here is shown in Fig. 2. Planer motion of this crane can be simply modeled as a pendulum suspended from a moving support unit, as shown in Fig. 3. The mass of the overhead support unit and mass of the payload are labeled as mt and mp, respectively; acceleration due to gravity is represented as g; a viscous damping force, which acts on the payload, can be described by the damping coefficient b; and the length of the suspension cable is labeled as L. The trolley position, x, will be considered as the controlled variable. An obvious benefit to such a choice for the system input is that a model of crane motors and industrial drives is unnecessary at this juncture. Later these systems will be considered when determining the response
y
x
m
t
Datum
m
p
Input, x
m
p
g
θ
b
L
θ

Fig. 3. Multi-body model of the crane along one axis.
of the system to reference signals. A similar modeling formulation was used in Fliess et al. (1991).
With the crane modeled in this manner, the system is reduced to one degree of freedom with the cable angle, y, used as the independent coordinate.
The differential equation of motion is

y € þ b y_ þ gsin y ¼ cos y x€. (1)
Lmp L L
The limited cable sway of crane systems allows one to assume a small angle approximation, reducing (1) to:

y€ þ b y_ þ gy ¼ 1 x€. (2)
Lmp L L
Recognizing that (2) represents a second-order damped oscillatory system, one may write
b
g
L

¼

o
2
n

(4)
,
and

1
L

¼

o
2
n
g

.
(5)

the

a
system
obtain

representation

relating
To
velocity

of

may
the

overhead

support

unit

to
the

angle

of

the

cable

one
of

the

support

position,
unit

the

derivative
time
substitute

_

v
t
,

for

€
x
.

Assuming

zero

initial

conditions,

and

using

the
relations

in

(3)

through

(5)
,

one

obtains

the

following

transfer

function

relating

the

cable

angle

to

the
velocity

of
support
unit:
the
Y
ð
s
Þ
V
t
ð
s
Þ
¼

ð
o
2
n
=
g
Þ
s
s
2
þ
2
zo
n
s
þ
o
2
n
.

(6)
This

relationship

is

represented

as

a

block

diagram

in

Fig. 2. Bridge crane in the Manufacturing Research Center. Fig. 4, where the block labeled as ‘‘Payload’’ represents the
Lmp ¼ 2zo n, (3)
Vr Drive &
Motors
V
t
Payload
p
Fig. 4. Input velocity–output angle relationship for a crane.
V
t
V
r
Y
NP
Rate Limiter
Switch
V
r
0
H

Fig. 5. Model of AC induction motors and vector drives.
transfer function expressed in (6). The velocity of the overhead support unit, Vt, causes the payload plant to respond with cable angle yp. Also illustrated in Fig. 4 is the relationship between the support unit velocity, Vt, and the desired reference velocity, Vr. The actual velocity of the support unit is the response of the plant labeled ‘‘Drive & Motors’’ to the reference velocity. This plant is a composite representation of the actuation elements and crane mass. These elements are comprised of the bridge and trolley masses, motors, and drives.
An accurate model of industrial AC induction motors, vector drives, and support unit mass, typical for large cranes, was derived in Sorensen (2005). This model is represented in the block diagram of Fig. 5.
The model relates the velocity response of drives and motors to a desired reference velocity. Three elements comprise the model: a switch, a rate limiter, and a linear, second-order, heavily damped plant, H. The switching element ordinarily passes the original reference signal, Vr, to the rate-limiting block. However, when transitional velocity commands are issued to the crane, the switch temporarily sends a signal of zero. Transitional velocity commands are those commands that change the direction of travel of the crane (forward to reverse or vise versa). This type of behavior depends on Vr and Vt, and can be described with the following switching rules:
8 >< Vr; SignðVrÞ ¼ SignðVtÞ;
Switch output ¼ Vr; jVtjpX%; (7)
> : 0; otherwise:
This model may be used to represent the response of many industrial vector drive and AC induction motor combinations by proper selection of four parameters associated with the model: the slew rate parameter of the rate limiter, S; the switch percent of the switch element, X%; the natural frequency of H; and the damping ratio of H. For the MARC crane, these parameters were estimated to be 160%/s, 0.9%, 6.98rad/s, and 0.86, respectively.
0
1
2
3
-100
-50
0
50
100
Velocity (% maximum)
Actual Response
Simulated Response
Response 1
Response 1
Response 1

Time (s)
Fig. 6. Comparison of actual and simulated responses to several velocity commands.
The response of the model closely follows the actual response of the MARC crane. This can be seen in Fig. 6, where the