Output regulation of Euler-Lagrange systems based on error and velocity feedback

Based on a certainty equivalence property, we propose an adaptive internal model control law that solves global robust output regulation of uncertain Euler-Lagrange (EL) systems based only on error (or relative position) and velocity feedback. The proposed controller does not require apriori knowledge of reference signal and its derivatives, which are commonly assumed in literature. It enables a self-learning mechanism of the closed-loop EL systems where the adaptive internal model-based controller is able to learn the desired trajectories and adapt itself to the uncertain plant parameters. Furthermore, the analysis offers insights to the design of internal model-based output regulation for multivariable nonlinear systems with uniform vector relative degree two

Robust output regulation for voltage control in DC networks with time-varying loads

In this letter, we propose a novel control scheme for regulating the voltage in Direct Current (DC) networks. More precisely, the proposed control scheme is based on the output regulation methodology and, differently from the results in the literature, where the loads are assumed to be constant, we consider time-varying loads whose dynamics are described by a class of nonlinear differential equations. We prove that the proposed control scheme achieves voltage regulation ensuring the stability of the overall network

Issues in the design of switched linear systems : a benchmark study

In this paper we present a tutorial overview of some of the issues that arise in the design of switched linear control systems. Particular emphasis is given to issues relating to stability and control system realisation. A benchmark regulation problem is then presented. This problem is most naturally solved by means of a switched control design. The challenge to the community is to design a control system that meets the required performance specifications and permits the application of rigorous analysis techniques. A simple design solution is presented and the limitations of currently available analysis techniques are illustrated with reference to this example

Robust Nonlinear Estimation and Control Applications using Synthetic Jet Actuators

Limit cycle oscillations (LCO), also known as utter, cause significant challenges in flight control of small unmanned aerial vehicles (SUAVs), and could potentially lead to structural damage and catastrophic failures. LCO can be described as vibrational motions in the rocking, pitching and plunging displacements of an aircraft wing. To address this, the use of synthetic jet actuators (SJAs) in UAV flight control systems is becoming popular as a practical alternative and to mechanical deflection surfaces. Synthetic jet actuators are promising tools for LCO suppression systems in small UAVs due to their small size, ease of operation, and low cost. Uncertainties inherent in the dynamics of the synthetic jet actuators present significant challenges in the synthetic jet actuator-based control design. Specifically, the input-output characteristic (voltage-virtual deflection angle relationship) of the synthetic jet actuators is nonlinear and contains parametric uncertainty. Further control design challenges exist in situations where multiple actuators lose effectiveness. This dissertation focuses on the suppression of limit cycle oscillations on small unmanned air vehicles using synthetic jet actuators. A brief description on how wind gust affects aircraft tracking control is presented. It shows an extension to a paper by adding the wind gust model to the system while also varying the uncertain synthetic jet actuator parameters using a Monte Carlo method. Next, a robust nonlinear control method is presented, which achieves simultaneous aircraft tracking control and limit cycle oscillation suppression using these synthetic jet actuators and a robust controller. Following that, a nonlinear LCO regulation method is presented, which uses a bank of dynamic filters to eliminate the need for pitching and plunging LCO rate measurements. Finally, an alternative method of LCO regulation control is presented, which utilizes a sliding mode observer in lieu of a bank of filters to estimate the pitching and plunging LCO rates

Distributed Control of Multi-agent Systems with Unknown Time-varying Gains: A Novel Indirect Framework for Prescribed Performance

In this paper, a new yet indirect performance guaranteed framework is established to address the distributed tracking control problem for networked uncertain nonlinear strict-feedback systems with unknown time-varying gains under a directed interaction topology. The proposed framework involves two steps: In the first one, a fully distributed robust filter is constructed to estimate the desired trajectory for each agent with guaranteed observation performance that allows the directions among the agents to be non-identical. In the second one, by establishing a novel lemma regarding Nussbaum function, a new adaptive control protocol is developed for each agent based on backstepping technique, which not only steers the output to asymptotically track the corresponding estimated signal with arbitrarily prescribed transient performance, but also largely extends the scope of application since the unknown control gains are allowed to be time-varying and even state-dependent. In such an indirect way, the underlying problem is tackled with the output tracking error converging into an arbitrarily pre-assigned residual set exhibiting an arbitrarily pre-defined convergence rate. Besides, all the internal signals are ensured to be semi-globally ultimately uniformly bounded (SGUUB). Finally, simulation results are provided to illustrate the effectiveness of the co-designed scheme

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

On self-learning mechanism for the output regulation of second-order affine nonlinear systems

This paper studies global robust output regulation of second-order nonlinear systems with input disturbances that encompass the fully-actuated Euler-Lagrange systems. We assume the availability of relative output (w.r.t. a family of reference signals) and output derivative measurements. Based on a specific separation principle and self learning mechanism, we develop an internal model-based controller that does not require apriori knowledge of reference and disturbance signals and it only assumes that the kernels of these signals are a family of exosystems with unknown parameters (e.g., amplitudes, frequencies or time periods). The proposed control framework has a self-learning mechanism that extricates itself from requiring absolute position measurement nor precise knowledge of the feedforward kernel signals. By requiring the high-level task/trajectory planner to use the same class of kernels in constraining the trajectories, the proposed low-level controller is able to learn the desired trajectories, to suppress the disturbance signals, and to adapt itself to the uncertain plant parameters. The framework enables a plug-and-play control mechanism in both levels of control