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188 Autonomous Mobile Robots

and a switching strategy to guarantee robust stability of the closed loop system
in the presence of disturbances and measurement noise.
The second class of results on the control of nonholonomic systems is
dynamic control, where the torque and force are taken as the control inputs. Both
trajectory tracking and force control are manageable for a constrained robot if
the exact robot dynamic model is available for controller design. In real applica-
tions, however, perfect cancellation of the robot dynamics is almost impossible.
As such, adaptive control was proposed to deal with parameter uncertain-
ties. Approximator-based adaptive control approaches have been extensively
studied in the past decade using Lyapunov analysis for general nonlinear sys-
tems. Motivated by previous works on the control of nonholonomic constrained
mechanical systems and the approximation-based adaptive control of nonlinear
systems, the adaptive neuro-fuzzy (NF) control is developed in Chapter 6 for
the control of nonholonomic constrained systems using the Lyapunov stability
analysis in a unified procedure.
In addition, we should note that actuator dynamics constitute an import-
ant component of the complete robotic dynamics, especially in the case of
high-velocity movement and highly varying loads. Many control methods have
therefore been developed to take into account the effects of actuator dynamics.
However, very few works in literature have considered the control of nonholo-
nomic systems with actuator dynamics. To address this, Chapter 7 considers the
stabilization problem for general nonholonomic mechanical systems at the actu-
ator level, taking into account the uncertainties in dynamics and the actuators.
The controller design consists of two stages. In the first stage, to facilitate con-
trol system design, the nonholonomic kinematic subsystem is transformed into
a skew-symmetric form and the properties of the overall systems are discussed.
Then, a virtual adaptive controller is presented to compensate for the parametric
uncertainties of the kinematic and dynamic subsystems. In the second stage, an
adaptive controller is designed at the actuator level and the controller guarantees
that the configuration state of the system converges to the origin.
The last chapter of this part of the book considers the control of nonholo-
nomic (specifically, car-like) robots for vehicle following. This is an important
aspect of advanced autonomous mobile robot systems in which robots may
very likely outnumber human operators. The nonholonomic nature of car-
like mobile robot motion imposes intrinsic difficulties in control design. This
chapter, hence, presents a unified control design for tracking maneuvers of two
car-like mobile robots. The vehicle tracking maneuvers are formulated into an
integrated framework, with forward tracking, backward tracking, driving, and
steering, at the kinematics and dynamics levels. A nonlinear controller with a
few design parameters is designed for maneuvers with simultaneous driving and
steering for vehicle tracking — in both forward tracking and backward track-
ing maneuvers. Tracking stability is ensured by the proper design of a stable

© 2006 by Taylor & Francis Group, LLC

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