36,880 research outputs found
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
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
Nonlinear control and its application to active tilting-pad bearings
The drawbacks of active magnetic bearings are arousing interest in the adaptation of mechanical bearings for active use. A promising mechanical bearing candidate for active operation is the tilting-pad bearing. In this research, we introduce an active tilting-pad bearing with linear actuators that translate each pad. The use of feedback in determining the actuator forces allows for the automatic, continuous adjustment of the pad position during the machine operation. In this work, we develop the dynamic model of the active bearing system such that the actuator forces are the control inputs. The hydrodynamic force is modeled as a spring/damper-like force with unknown damping and stiffness coefficients. Whereas in the literature, the damping and stiffness effects are normally considered linear, here, motivated by a numerical study based on the Reynolds equation, we use a nonlinear model for the stiffness force. An adaptive controller is designed to asymptotically regulate the rotor to the bearing center. The proposed control design is applicable to both the linear and nonlinear stiffness models. Simulations and experiments show that the active strategy improves the bearing performance in comparison to its traditional passive operation. Further, the experiments indicate the nonlinear stiffness-based controller slightly improves the active bearing regulation performance relative to the linear-based one. To the best of our knowledge, this dissertation is the first to report the experimental demonstration of an active tilting-pad bearing using feedback control. Since the model of the active tilting-pad bearing has a parametric strict-feedback-like form, the second part of this dissertation is dedicated to constructing new nonlinear control tools for this class of systems. Specifically, we consider the regulation and tracking control problems for multi-input/multi-output parametric strict-feedback systems in the presence of additive, exogenous disturbances and parametric uncertainties. For such systems, robust adaptive controllers usually cannot ensure asymptotic tracking or even regulation. In this work, under the assumption the disturbances are C2 with bounded time derivatives; we present a new C0 robust adaptive control construction that guarantees the output/tracking error is asymptotically driven to zero. Numerical examples illustrate the main results, including cases where the disturbances do not satisfy the aforementioned assumptions
Robust Asymptotic Stabilization of Nonlinear Systems with Non-Hyperbolic Zero Dynamics
In this paper we present a general tool to handle the presence of zero
dynamics which are asymptotically but not locally exponentially stable in
problems of robust nonlinear stabilization by output feedback. We show how it
is possible to design locally Lipschitz stabilizers under conditions which only
rely upon a partial detectability assumption on the controlled plant, by
obtaining a robust stabilizing paradigm which is not based on design of
observers and separation principles. The main design idea comes from recent
achievements in the field of output regulation and specifically in the design
of nonlinear internal models.Comment: 30 pages. Preliminary versions accepted at the 47th IEEE Conference
on Decision and Control, 200
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