17,562 research outputs found
Adaptive output feedback stabilization for nonlinear systems with unknown polynomial-of-output growth rate and sensor uncertainty
summary:In this paper, the problem of adaptive output feedback stabilization is investigated for a class of nonlinear systems with sensor uncertainty in measured output and a growth rate of polynomial-of-output multiplying an unknown constant in the nonlinear terms. By developing a dual-domination approach, an adaptive observer and an output feedback controller are designed to stabilize the nonlinear system by directly utilizing the measured output with uncertainty. Besides, two types of extension are made such that the proposed methods of adaptive output feedback stabilization can be applied for nonlinear systems with a large range of sensor uncertainty. Finally, numerical simulations are provided to illustrate the correctness of the theoretical results
Robust adaptive sampled-data control of a class of systems under structured nonlinear perturbations
Cataloged from PDF version of article.A robust adaptive sampled-data feedback stabilization
scheme is presented for a class of systems with nonlinear additive
perturbations. The proposed controller generates a control input by
using high-gain static or dynamic feedback from nonuniform sampled
values of the output. A simple adaptation rule adjusts the gain and the
sampling period of the controller
Adaptive output regulation for a class of nonlinear systems with guaranteed transient performance
This paper is dedicated to adaptive output regulation for a class of nonlinear systems with asymptotic output tracking and guarantee of prescribed transient performance. With the employment of internal model principle, we first transform this problem into a specific adaptive stabilization problem with output constraints. Then, by integrating the time-varying Barrier Lyapunov Function (BLF) technique together with the high gain feedback method, we develop an output-based control law to solve the constrained stabilization problem and consequently confine the output tracking error to a predefined arbitrary region. The output-based control law enables adaptive output regulation in the sense that, under unknown exosystem dynamics, all the closed-loop system signals are bounded whilst the controlled output constraints are not violated. Finally, efficacy of the proposed design is illustrated through a simulation example
Robust adaptive sampled-data control of a class of systems under structured nonlinear perturbations
A robust adaptive sampled-data feedback stabilization scheme is presented for a class of systems with nonlinear additive perturbations. The proposed controller generates a control input by using high-gain static or dynamic feedback from nonuniform sampled values of the output. A simple adaptation rule adjusts the gain and the sampling period of the controller
A nonparametric learning framework for nonlinear robust output regulation
This paper proposes a nonparametric learning solution framework for a generic
internal model design of nonlinear robust output regulation. The global robust
output regulation problem for a class of nonlinear systems with output feedback
subject to a nonlinear exosystem can be tackled by constructing a linear
generic internal model, provided that a continuous nonlinear mapping exists. An
explicit continuous nonlinear mapping was constructed recently in [1] under the
assumption that the steady-state generator is linear in the exogenous signal.
We further relax such an assumption to a relaxed assumption that the
steady-state generator is polynomial in the exogenous signal. A nonparametric
learning framework is proposed to solve a linear time-varying equation to make
the nonlinear continuous mapping always exist. With the help of the proposed
framework, the nonlinear robust output regulation problem can be converted into
a robust non-adaptive stabilization problem for the augmented system with
integral Input-to-State Stable (iISS) inverse dynamics. Moreover, a dynamic
gain approach can adaptively raise the gain to a sufficiently large constant to
achieve stabilization without requiring any a priori knowledge of the
uncertainties appearing in the dynamics of the exosystem and the system. We
further apply the nonparametric learning framework to globally reconstruct and
estimate multiple sinusoidal signals with unknown frequencies without using
adaptive techniques. An explicit nonlinear mapping can directly provide the
estimated parameters, which will exponentially converge to the unknown
frequencies. As a result, a feedforward control design is proposed to solve the
output regulation using our nonparametric learning framework.Comment: 15 pages; Nonlinear control; iISS stability; output regulation;
parameter estimation; Non-adaptive contro
Robust Output Regulation for Autonomous Robots:self-learning mechanisms, task-space control and multi-agent systems
This thesis focuses on robust output regulation for autonomous robots. The control objective of output regulation is to design a feedback controller to achieve asymptotic tracking and/or disturbance rejection for a class of exogenous reference and/or disturbance while maintaining closed-loop stability. We investigate three research problems that pertain to the constructive design of robust output regulation for fully actuated Euler-Lagrange systems from centralized to distributed fashions. The first one is the global robust output regulation of second-order affine nonlinear systems with input disturbances that encompass the fully-actuated Euler-Lagrange systems. Based on a certainty equivalence principle method, we proposed a novel class of nonlinear internal models taking a cascade interconnection structure with strictly relaxed conditions than before. The second one is the output regulation for robot manipulators working in task-space. An internal model-based adaptive controller is designed to cope with uncertain manipulator kinematic and dynamic parameters, as well as unknown periodic reference trajectories generated by harmonic oscillators. The last one is the formation control of manipulators’ end-effector subject to external disturbances or parameter uncertainties. We present and analyze gradient descent-based distributed formation controllers for end-effectors. Internal models are used to reject external disturbances. Moreover, by introducing an extra integrator and an adaptive estimator for gravitational compensation and stabilization, respectively, we extend the proposed gradient-based design to the case where the plant parameters are not exactly known
Additive-Decomposition-Based Output Feedback Tracking Control for Systems with Measurable Nonlinearities and Unknown Disturbances
In this paper, a new control scheme, called as additive-decomposition-based
tracking control, is proposed to solve the output feedback tracking problem for
a class of systems with measurable nonlinearities and unknown disturbances. By
the additive decomposition, the output feedback tracking task for the
considered nonlinear system is decomposed into three independent subtasks: a
pure tracking subtask for a linear time invariant (LTI) system, a pure
rejection subtask for another LTI system and a stabilization subtask for a
nonlinear system. By benefiting from the decomposition, the proposed
additive-decomposition-based tracking control scheme i) can give a potential
way to avoid conflict among tracking performance, rejection performance and
robustness, and ii) can mix both design in time domain and frequency domain for
one controller design. To demonstrate the effectiveness, the output feedback
tracking problem for a single-link robot arm subject to a sinusoidal or a
general disturbance is solved respectively, where the transfer function method
for tracking and rejection and backstepping method for stabilization are
applied together to the design.Comment: 23 pages, 6 figure
Global Stabilization of Triangular Systems with Time-Delayed Dynamic Input Perturbations
A control design approach is developed for a general class of uncertain
strict-feedback-like nonlinear systems with dynamic uncertain input
nonlinearities with time delays. The system structure considered in this paper
includes a nominal uncertain strict-feedback-like subsystem, the input signal
to which is generated by an uncertain nonlinear input unmodeled dynamics that
is driven by the entire system state (including unmeasured state variables) and
is also allowed to depend on time delayed versions of the system state variable
and control input signals. The system also includes additive uncertain
nonlinear functions, coupled nonlinear appended dynamics, and uncertain dynamic
input nonlinearities with time-varying uncertain time delays. The proposed
control design approach provides a globally stabilizing delay-independent
robust adaptive output-feedback dynamic controller based on a dual dynamic
high-gain scaling based structure.Comment: 2017 IEEE International Carpathian Control Conference (ICCC
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