Showing papers in "Automatica in 1984"

TL;DR: This contribution presents a brief summary of some basic fault detection methods, followed by a description of suitable parameter estimation methods for continuous-time models.

TL;DR: A simple method for estimating the critical gain and the critical frequency is described, which may be used for automatic tuning of simple regulators as well as initialization of more complicated adaptive regulators.

TL;DR: The zeros of the discrete time system obtained when sampling a continuous time system are explored and theorems for the limiting zeros for large and small sampling periods are given.

TL;DR: In this paper, the effects of unmodeled high frequency dynamics and bounded disturbances on stability and performance of adaptive control schemes are analyzed using simple examples, and a procedure is used to construct Lyapunov-like functions for a modified adaptive controller applied to a dominant plant of relative degree one, in the presence of parasitics and disturbances.

TL;DR: The survey is aimed at engineers and applied mathematicians interested in model-order reduction, separation of time scales and allied simplified methods of control system analysis and design.

TL;DR: This paper presents the results of an application of transformations (from nonlinear to linear systems) to the design of a helicopter autopilot.

TL;DR: A new time-domain method of quadratic-optimum control synthesis for systems described by finite-memory output predictors is presented, which leads to algorithms which are numerically robust and therefore suitable for real-time computation using microprocessors with reduced wordlength.

TL;DR: The paper describes the practical features that a self-tuning controller must possess and discusses how plant dead-time and excess continuous-time poles can lead to discrete-time nonminimum-phase zeros.

TL;DR: It is proved that a generalized MRAS control assures the convergence to a suitable reference model for a class of processes: the manipulator is shown to belong to such a class.

TL;DR: A method for automatic tuning of the PID process control parameters, usually called 'auto-tuning', is developed, and is implemented on a digital controller using microprocessors and applied to some real processes, yielding satisfactory results.

TL;DR: It is shown that mean square Stability of the system is a sufficient and almost necessary condition for the existence, uniqueness and stability of the time invariant estimator.

TL;DR: In this paper, the authors discuss the implementation aspects of self-tuning regulators and how to implement them in a real-world environment, including robustness, signal conditioning, parameter tracking, estimator wind-up, reset action and start-up.

TL;DR: A further advantage of the nonlinear approach of this paper is that the identified parameters have a clear physical meaning and can give a useful information on the state of the biomass.

TL;DR: In this paper, the authors describe the application of model reference adaptive control (MRAS) to automatic steering of ships, where the main advantages are the simplified controller adjustment which yields safer operation and the decreased fuel cost.

TL;DR: A novel addition to conventional stabilization techniques which has recently been incorporated in such a system to greatly improve stabilization performance is described, which contains a model in system software which predicts realtime friction torque values.

TL;DR: This paper deals with a control method of a dynamic biped locomotion by utilizing a dynamical system having a stable limit cycle and makes use of the bifurcation of a set of coupled van der Pol's equations.

TL;DR: The use of discontinuous control in adaptive model-following continuous control systems with nonlinear time-varying plant withHyperstability theory is described, and the link among the three approaches is explored by studying the error dynamics.

TL;DR: It is shown in this paper that certain classical identification algorithms can be easily modified in order to ensure the above stabilizability condition, without affecting the other properties.

TL;DR: By increasing the control horizon length, the proposed algorithm, referred to by the acronym MUSMAR, is shown to be a natural generalization of standard self-tuning controllers and closely approximates a steady-state LQG controller inheriting the intrinsic robustness of LQGs design.

TL;DR: The design of optimal controllers for use in self-tuning systems is considered and Linear Quadratic Gaussian controllers offer a guarantee of stability (when the plant parameters are known) which is particularly useful for nonminimum phase systems.

TL;DR: The emphasis is on parameterization issues such as: types of control structures necessary for implementation, required prior information needed for implementation and techniques for reducing the size of the resulting parameter estimation problem.

TL;DR: It is shown here how to define uniquely identifiable overlapping parametrizations for state-space and ARMA models and how they are related to a set of intrinsic invariants, which are obtained from the Markov parameters of the system.

TL;DR: Computer simulation has shown that direct (or implicit) model reference procedures might be successfully applied to the control of blood pressure despite the uncertainty in the delays, time constant and gains.

TL;DR: It is shown that this extension of the well-known minimum variance control strategy to the multi-input-multi-output case is straightforward when the system interactor matrix is diagonal but presents some unexpected difficulties in the general case.

TL;DR: A characterization of structurally fixed modes in linear systems is given in the graph-theoretic framework and results in a computationally efficient test for stabilizability of systems under feedback structure constraints.

TL;DR: Various topics in the resulting extension of the standard LQG design procedure are discussed, for instance optimality conditions, design of optimal low-order controllers and variance-constrained self-tuning control.

TL;DR: The stability and robustness properties of adaptive control systems are examined using input-output stability theory, i.e. passivity and small-gain theory and local results are developed where the magnitudes of the external inputs are restricted.

TL;DR: A new algorithm which is able to successfully control unknown systems with quite rapidly varying parameters is presented and a convergence proof in the linearly time-varying parameter case is given.

TL;DR: A linearization analysis of a nonlinear adaptive controller is used to demonstrate analytically some design guidelines which alleviate some of the problems associated with adaptive control in the presence of unmodeled dynamics.

TL;DR: This paper presents an overview of the current status of convergence theory for adaptive control algorithms and focuses on the conceptual common ground between different approaches.