Showing papers in "Automatica in 1997"

TL;DR: Although many approaches and techniques exist to approach different versions of the static output feedback problem in the control of linear, time-invariant systems, no efficient algorithmic solutions are available.

TL;DR: A tracking control methodology via time-varying state feedback based on the backstepping technique is proposed for both a kinematic and simplified dynamic model of a two-degrees-of-freedom mobile robot.

TL;DR: While the main emphasis is on Linear-Quadratic optimal control and active suspensions, the paper also addresses a number of related subjects including semi-active suspensions; robust, adaptive and nonlinear control aspects and some of the important practical considerations.

TL;DR: This paper considers the adaptive robust control of a class SISO nonlinear systems in a semi-strict feedback form and develops a systematic way to combine the backstepping adaptive control with deterministic robust control.

TL;DR: It is shown that the solvability of a set of matrix inequalities is necessary and sufficient to the existence of a proper controller that satisfies a prescribed H ∞ norm condition as well as stabilizing the closed-loop system and eliminating all impulsive modes.

TL;DR: This paper states sufficient conditions that guarantee that the Galerkin approximation converges to the solution of the GHJB equation and that the resulting approximate control is stabilizing on the same region as the initial control.

TL;DR: An approach to estimate the tire-road friction using only the wheel slip, that is, the relative difference in wheel velocities, is presented and an adaptive estimator is presented for a model linear in parameters, designed to work for periods of poor excitation, errors in variables, simultaneous slow and fast parameter drifts and abrupt changes.

TL;DR: Two algorithms for the rejection of sinusoidal disturbances with unknown frequency are presented and the indirect algorithm is found to have a larger capture region for the parameter estimates, whereas the direct algorithm has superior convergence properties locally about the optimum parameter estimates.

TL;DR: In this paper, the authors deal with the problem of robust stability analysis and robust stabilization for a class of uncertain linear systems with a time-varying state delay, where the uncertainty is assumed to be norm-bounded and appears in all the matrices of the state space model.

TL;DR: Extended Kalman-Bucy filtering and Bayesian hypothesis selection are applied to estimate motion, tire forces, and road coefficient of friction of vehicles on asphalt surfaces to select the most likely μ from a set of hypothesized values.

TL;DR: This work focuses on a moving average model with an integrator, and derives computationally simpler suboptimal algorithms based on the assumptions made about the future inputs in optimizing the current input.

TL;DR: The main contribution of this paper is the development of a decomposition principle that is, the design of a fuzzy discrete-time control system can be decomposed into a set of discrete- time subsystems.

TL;DR: The regulating feedback law, derived on the basis of a ‘boost’ model composed of ideal switches and ideal circuit components, is assessed, via computer simulations, on a realistic stochastically perturbed switched converter model, including parasitic resistances and parasitic voltage sources.

TL;DR: This paper is the first of two dealing with the analysis and design of a class of complex control systems using a combination of fuzzy logic and modern control theory, which can be represented by a fuzzy aggregation of a set of local linear models.

TL;DR: An approach to structurally stable regulation that unifies and extends a number of existing results is described, and the issue of robust regulation is addressed, i.e. theissue of achieving regulation in the presence of parameter uncertainties ranging within a prescribed set.

TL;DR: This note presents a form of SPSA that requires only one function measurement (for any dimension), and theory is presented that identifies the class of problems for which this one-measurement form will be asymptotically superior to the standard two-measuresment form.

TL;DR: A new method is developed for the state estimation of linear discrete-time stochastic systems in the presence of an unknown disturbance that is optimal in the unbiased minimum variance sense.

TL;DR: A novel approach is presented for the fault detection and diagnosis of faults in actuators and sensors via the use of adaptive updating rules, where a fixed observer is used to detect the fault whilst an adaptive diagnositic observer is constructed to diagnose the fault.

TL;DR: A method for auto-tuning fully cross-coupled multivariable PID controllers from decentralized relay feedback is proposed forMultivariable processes with significant interactions, where significant performance improvement over the existing tuning methods is demonstrated.

TL;DR: This paper is concerned with the estimation of a suitable explicit expression for the feedback controller-invariant term of the closed-loop MIMO process from routine operating data based on filtering and correlation (FCOR) analysis of the process output and filtered data.

TL;DR: An adaptive observer for a class of single-input single-output (SISO) nonlinear systems is proposed using a generalized dynamic recurrent neural network (DRNN), with tuned on-line, with no off-line learning required.

TL;DR: The Lie-Poisson structure is derived for the underwater vehicle dynamics with noncoincident centers of gravity and buoyancy and provides a setting for exploring the stabilizing and destabilizing effects of dissipation and externally applied control forces and torques.

TL;DR: A solution to the problem of identifying multivariable finite dimensional linear time-invariant systems from noisy input/output measurements is developed in the framework of subspace identification and it is shown that the proposed algorithms give consistent estimates when the system is operating in open- or closed-loop.

TL;DR: The solution of a tracking problem for a secondorder nonlinear system with uncertain dynamics and incomplete state measurement is obtained by means of a procedure directly inspired by the solution of the classical minimum-time optimal control problem.

TL;DR: A critical overview of the existing techniques, focusing on regularization theory and Bayesian estimation, is provided and a fast SVD-based numerical algorithm is developed that includes the optimization of the regularization parameter, and the computation of confidence intervals.

TL;DR: Time-invariant discontinuous feedback laws are constructed to asymptotically stabilize the system to the desired configuration with exponential convergence rates.

TL;DR: Exponential stability and asymptotic perturbed stability results are derived for nonlinear discrete-time systems to determine conditions under which discrete- time nonlinear model predictive control is stable in the face of perturbations.

TL;DR: Under appropriate assumptions, it is shown that the bounded solution to the partial differential equation of Isidori and Byrnes for each trajectory of an exosystem must be given by an integral representation formula of Devasia, Chen and Paden.

TL;DR: Simulation results show that the proposed robust nonlinear decentralized controller can greatly enhance the transient stability of the system regardless of the network parameters, operating points and fault locations.