1,599 research outputs found
Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems
Many modern nonlinear control methods aim to endow systems with guaranteed
properties, such as stability or safety, and have been successfully applied to
the domain of robotics. However, model uncertainty remains a persistent
challenge, weakening theoretical guarantees and causing implementation failures
on physical systems. This paper develops a machine learning framework centered
around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and
unmodeled dynamics in general robotic systems. Our proposed method proceeds by
iteratively updating estimates of Lyapunov function derivatives and improving
controllers, ultimately yielding a stabilizing quadratic program model-based
controller. We validate our approach on a planar Segway simulation,
demonstrating substantial performance improvements by iteratively refining on a
base model-free controller
Finite-Time Adaptive Fuzzy Tracking Control for Nonlinear State Constrained Pure-Feedback Systems
This paper investigates the finite-time adaptive fuzzy tracking control
problem for a class of pure-feedback system with full-state constraints. With
the help of Mean-Value Theorem, the pure-feedback nonlinear system is
transformed into strict-feedback case. By employing finite-time-stable like
function and state transformation for output tracking error, the output
tracking error converges to a predefined set in a fixed finite interval. To
tackle the problem of state constraints, integral Barrier Lyapunov functions
are utilized to guarantee that the state variables remain within the prescribed
constraints with feasibility check. Fuzzy logic systems are utilized to
approximate the unknown nonlinear functions. In addition, all the signals in
the closed-loop system are guaranteed to be semi-global ultimately uniformly
bounded. Finally, two simulation examples are given to show the effectiveness
of the proposed control strategy
Adaptive Fuzzy Tracking Control for Nonlinear State Constrained Pure-Feedback Systems With Input Delay via Dynamic Surface Technique
This brief constructs the adaptive backstepping control scheme for a class of
pure-feedback systems with input delay and full state constraints. With the
help of Mean Value Theorem, the pure-feedback system is transformed into
strict-feedback one. Barrier Lyapunov functions are employed to guarantee all
of the states remain constrained within predefined sets. By introducing the
Pade approximation method and corresponding intermediate, the impact generated
by input delay on the output tracking performance of the system can be
eliminated. Furthermore, a low-pass filter driven by a newly-defined control
input, is employed to generate the actual control input, which facilitates the
design of backstepping control. To approximate the unknown functions with a
desired level of accuracy, the fuzzy logic systems (FLSs) are utilized by
choosing appropriate fuzzy rules, logics and so on. The minimal learning
parameter (MLP) technique is employed to decrease the number of nodes and
parameters in FLSs, and dynamic surface control (DSC) technique is leveraged to
avoid so-called "explosion of complexity". Moreover, smooth robust compensators
are introduced to circumvent the influences of external disturbance and
approximation errors. By stability analysis, it is proved that all of signals
in the closed-loop system are semi-globally ultimately uniform bounded, and the
tracking error can be within a arbitrary small neighbor of origin via selecting
appropriate parameters of controllers. Finally, the results of numerical
illustration are provided to demonstrate the effectiveness of the designed
method.Comment: arXiv admin note: text overlap with arXiv:2310.1540
Safe Learning of Quadrotor Dynamics Using Barrier Certificates
To effectively control complex dynamical systems, accurate nonlinear models
are typically needed. However, these models are not always known. In this
paper, we present a data-driven approach based on Gaussian processes that
learns models of quadrotors operating in partially unknown environments. What
makes this challenging is that if the learning process is not carefully
controlled, the system will go unstable, i.e., the quadcopter will crash. To
this end, barrier certificates are employed for safe learning. The barrier
certificates establish a non-conservative forward invariant safe region, in
which high probability safety guarantees are provided based on the statistics
of the Gaussian Process. A learning controller is designed to efficiently
explore those uncertain states and expand the barrier certified safe region
based on an adaptive sampling scheme. In addition, a recursive Gaussian Process
prediction method is developed to learn the complex quadrotor dynamics in
real-time. Simulation results are provided to demonstrate the effectiveness of
the proposed approach.Comment: Submitted to ICRA 2018, 8 page
Adaptive Control of Unknown Pure Feedback Systems with Pure State Constraints
This paper deals with the tracking control problem for a class of unknown
pure feedback system with pure state constraints on the state variables and
unknown time-varying bounded disturbances. An adaptive controller is presented
for such systems for the very first time. The controller is designed using the
backstepping method. While designing it, Barrier Lyapunov Functions is used so
that the state variables do not contravene its constraints. In order to cope
with the unknown dynamics of the system, an online approximator is designed
using a neural network with a novel adaptive law for its weight update. In the
stability analysis of the system, the time derivative of Lyapunov function
involves known virtual control coefficient with unknown direction and to deal
with such problem Nussbaum gain is used to design the control law. Furthermore,
to make the controller robust and computationally inexpensive, a novel
disturbance observer is designed to estimate the disturbance along with neural
network approximation error and the time derivative of virtual control input.
The effectiveness of the proposed approach is demonstrated through a simulation
study on the third-order nonlinear system
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