Comparison of Optimal and Intelligent Sway Control for a
Lab-scale Rotary Crane System

M.A. Ahmad, R.E. Samin and M.A. Zawawi
Faculty of Electrical and Electronics Engineering
Universiti Malaysia Pahang,
Lebuhraya Tun Razak, 26300, Kuantan, Pahang, Malaysia


Abstract - This paper presents investigations of sway
feedback control approaches for a rotary crane system with
disturbance effect in the dynamic system. Linear Quadratic
Regulator (LQR) controller and Proportional-Derivative (PD)-
type Fuzzy Logic controller are the techniques used in this
investigation to actively control the sway of rotary crane
system. A lab-scale rotary crane system is considered
and the dynamic model of the system is derived using the
Euler-Lagrange formulation. A complete analysis of
simulation results for each technique is presented in time
domain and frequency domain respectively.
Performances of the controller are examined in terms of
sway suppression and disturbances cancellation. Finally,
a comparative assessment of the impact of each
controller on the system performance is presented and
discussed.

Index Terms – Rotary crane, anti-sway control, LQR controller,
PD-type Fuzzy Logic.

I. INTRODUCTION
The main purpose of controlling a crane is transporting
the load as fast as possible without causing any excessive
swing at the final position. However, most of the common
crane results in a swing motion when payload is suddenly
stopped after a fast motion [1]. The performance of
precision motion depends on damping capacity of the
system. The damping capability of a dynamical system can
be enhanced by passive or active damping methods. In the
passive approach, oscillation damping is increased by
deploying external dampers such as dashpots or viscous
dampers [2]. Feedback control can also be used as an active
approach in a wide band of insensitivity. Another approach
is feed-forward control techniques.

Various attempts in controlling cranes system based on
open loop system were proposed. For example, open loop
time optimal strategies were applied to the crane by many
researchers such as discussed in [3,4]. They came out with
poor results because open loop strategy is sensitive to the
system parameters (e.g. rope length) and could not
compensate for wind disturbances. Another open loop
control strategies is input shaping [5,6,7]. Input shaping is
implemented in real time by convolving the command
signal with an impulse sequence. An IIR filtering technique
related to input shaping has been proposed for controlling
suspended payloads [8]. Input shaping has been shown to be
effective for controlling oscillation of gantry cranes when
the load does not undergo hoisting [9, 10].
Apart from that, neural network has also been applied to
rotary crane system. The method applies three-layered
neural network as a controller (NC) with genetic algorithm
based (GA-based) training in order to control load swing
suppression for the rotary crane system [11]. A nonlinear
controller based on delayed position feedback is being
introduced in [12]. This method suppresses cargo
pendulation on cranes in the presence of noise, initial sway
and wind disturbances. Another stabilization method of
rotary crane via switching control has been proposed by
Kondo [13]. Furthermore, a fuzzy-based intelligent gantry
crane system has been proposed [14]. The proposed fuzzy
logic controllers consist of position as well as anti-sway
controllers. However, most of the feedback control system
proposed needs sensors for measuring the cart position as
well as the load sway angle.
This paper presents investigations of anti-sway angle
control approach in order to eliminate the effect of
disturbances applied to the rotary crane system. A
simulation environment is developed within Simulink and
Matlab for evaluation of the control strategies. In this work,
the dynamic model of the rotary crane system is derived
using the Euler-Lagrange formulation. To demonstrate the
effectiveness of the proposed control strategy, the
disturbances effect is applied at the pendulum of the rotary
crane. This is then extended to develop a feedback control
strategy for sway angle reduction and disturbances rejection.
Two feedback control strategies which are Linear Quadratic
Regulator (LQR) and PD-type fuzzy logic controller are
developed in this simulation work. Performances of each
controller are examined in terms of sway angle suppression
and disturbances rejection. Finally, a comparative
assessment of the impact of each controller on the system
performance is presented and discussed.


II. THE ROTARY CRANE SYSTEM

The 2-DOF rotary crane system with its payload
considered in this work is shown in Figure 1, where θ and
α denote the horizontal angle of the arm and the sway
angle of the pendulum, respectively, r and L is the length of