IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 23, NO. 1, JANUARY 2015 231
Guidance of Air Vehicles: A Sliding
Mode Approach
Muhammad Zamurad Shah, Student Member, IEEE, Raza Samar, Member, IEEE,
and Aamer Iqbal Bhatti, Senior Member, IEEE
Abstract—This paper presents a novel nonlinear guidance
scheme for ground track control of aerial vehicles. The pro-
posed guidance logic is derived using the sliding mode con-
trol technique, and is particularly suited for unmanned aerial
vehicle (UAV) applications. The main objective of the guidance
algorithm is to control the lateral track error of the vehicle
during flight, and to keep it as small as possible. This is achieved
by banking the vehicle, that is, by executing roll maneuvers.
The guidance scheme must perform well both for small and
large lateral track errors, without saturating the roll angle of
the vehicle, which serves as the control input for the guidance
algorithm. The limitations of a linear sliding surface for lateral
guidance are indicated; a nonlinear sliding surface is thereafter
proposed which overcomes these limitations, and also meets the
criterion of a good helmsman. Stability of the nonlinear surface
is proved using Lyapunov theory; control boundedness is also
proved to ensure that the controls are not saturated even for
large track errors. The proposed guidance law is implemented
on the flight control computer of a scaled YAK-54 UAV and
flight results for different scenarios (consisting of both small and
large errors) are presented and discussed. The flight test results
confirm the effectiveness and robustness of the proposed guidance
scheme.
Index Terms—Control boundedness, guidance and control of
unmanned aerial vehicles (UAVs), lateral guidance, sliding mode
control (SMC), track control, UAVs.
I. I NTRODUCTION
U
NMANNED AERIAL VEHICLES (UAVs) are of great
interest for intelligence, surveillance and reconnaissance
applications, and also for rescue operations. They reduce risk
to life, and have a relatively low operational cost. In recent
years, the work on UAV autonomy has added a new dimension
to the utility of these vehicles. Autonomy is the ability to
perform a task (mission) without being directly or remotely
controlled by a human operator [1]. Autonomous vehicles
must have sufficiently advanced path planning algorithms,
combined with effective and robust guidance and automatic
control systems. Successful control system design for high-
performance UAVs requires efficient and effective techniques
for the design of guidance and control algorithms that ensure
satisfactory operation in the face of system uncertainties and
Manuscript received April 16, 2013; revised November 26, 2013; accepted
May 1, 2014. Date of publication June 3, 2014; date of current version
December 15, 2014. Manuscript received in final form May 4, 2014. Rec-
ommended by Associate Editor A. Behal.
The authors are with the Department of Electronics Engineering, Moham-
mad Ali Jinnah University, Islamabad 44000, Pakistan (e-mail: zamurad@
gmail.com; raza.samar@gmail.com; aamer987@gmail.com).
Color versions of one or more of the figures in this paper are available
online at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TCST.2014.2322773
environmental disturbances. A key performance criterion for
the guidance and control system is to have the ability of precise
ground track control in the presence of disturbing forces.
A criterion for assessing the lateral performance and handling
qualities for such vehicles is presented in [2].
Two approaches are usually employed for ground track con-
trol of UAVs. In the first approach, the guidance and control
design problems are separated into an outer-loop guidance and
an inner-loop control design problem [3]–[7]. In the second
approach, the guidance and control problems are addressed
together in an integrated and unified framework [7]–[11].
A good background to the integrated approach and associated
challenges is presented in [12] and [13]. This approach is
more complicated due to the coupling of the different guidance
and control variables, and has not been as popular among
the practising aerospace community. In most applications, the
first approach is employed owing to its simplicity and intu-
itive appeal. Various well-established techniques exist for the
(inner loop) control design problem, such as linear, nonlinear,
robust, and intelligent techniques [14]. Work has also been
done on the design of the guidance loop; methods such as
proportional navigation, vector field methods, vision-based
methods and neural networks have been used, see for example
[9], [15]–[17].
A lateral track control law for small UAVs has been dis-
cussed in [6]; this is based on a pure geometrical concept. The
idea is to make the ratio of lateral deviation to lateral velocity
equal to the ratio of longitudinal distance to longitudinal veloc-
ity. Simulation results indicate that the yaw-rate command
generated by the guidance law exhibits oscillations in the vehi-
cle roll channel, this could be a problem for implementation
on a real vehicle. Mixed integer linear programming-based
guidance for UAVs has also been considered [18], [19]; how-
ever, here the optimization program generates a sequence of
waypoints (positions) and velocities for the vehicle to follow.
In other words, a mission plan is generated and deviations
from this plan need to be corrected through a lower level
guidance algorithm. A conceptually different guidance scheme
employing vector fields for curved path following has also
been pursued, see for example [20], [21]. Here a vector field of
course commands is generated, which is a function of vehicle
position relative to the desired track. The difference between
the actual and commanded course angles forms the error which
is driven to zero by using an appropriate control algorithm. The
vector field can, in some cases, give rise to large and sudden
course commands, which can tax the capability of the control
system.
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