TL;DR: It HAS been interesting to observe the impact of microprocessor technology in the field of automatic control.

TL;DR: It is shown that adaptive control laws can also be obtained from stochastic control theory, and different approaches are discussed with particular emphasis on model reference adaptive systems and self-tuning regulators.

TL;DR: An approach for studying stability of adaptively controlled systems subject to bounded disturbances with unknown statistics is given and uniform boundedness of all signals is proven under a condition that is discussed.

TL;DR: The role of the human operator as a problem solver in man-machine systems such as vehicles, process plants, transportation networks, etc. is considered and specifications for an overall model outlined.

TL;DR: A known stabilizability criterion and a simple stability theorem for the open-loop system are obtained as special cases of the main result.

TL;DR: A new procedure of selecting weighting matrices in linear quadratic optimal control problems (LQ-problems) is proposed, which has merits of an LQ-problem as well as a pole-assignment problem and will be useful for designing a linear feedback system.

TL;DR: It is shown that a class of ultimate boundedness problems which have been solved to date via nonlinear control, can in point of fact be solved via linear control and some of the so-called matching assumptions can be weakened somewhat.

TL;DR: A hierarchically intelligent control procedure to resolve certain aspects of the urban traffic management problem by optimizing an assigned subgoal and incorporating a learning algorithm at the lowest level.

TL;DR: The approach permits a simple characterization of multivariable root loci under a high-gain feedback and asymptotic behavior of optimal closed loop poles, state and control trajectories, performance index and optimal transfer function as the control cost coefficient in the performance index goes to zero.

TL;DR: The regulator problem with robustness is solved for systems modelled by rational transfer matrices and a topology for possibly unstable plants is presented.

TL;DR: The class of decentrally stabilizable large-scale systems presented in this paper is the largest such class hitherto described by the structure of interconnections.

TL;DR: Two adaptive control techniques are evaluated by application to a realistic mathematical model of a suspension polyvinyl chloride (PVC) reactor and the adaptive controllers are shown to always outperform the PID controller.

TL;DR: The control engineer, who is responsible for enlarging the scale of automation, should also play a role in adapting it to people, and technology should be individually designed to each culture.

TL;DR: A joint multitime scale-multiparameter singular perturbation is formulated and resolved in the context of linear time-varying systems and provides a suitable framework for establishing qualitative properties of multitime Scale systems.

TL;DR: It is shown, as compared with other non-Gaussian filters, the MIPA Kalman filter is computationally feasible, unbiased, more efficient and robust.

TL;DR: This approach imbeds a decentralized estimation problem into an equivalent scattering problem, and makes use of the super-position principle to relate local and centralized estimates.

TL;DR: A fast recursive algorithm is shown to construct stable approximants of linear systems from the finite data of the impulse response and autocorrelation sequences in multi-input-multi-output, discrete-time linear systems.

TL;DR: The entire system is described as a Markov process and different learning schemes are shown to lead to different flow patterns in the steady state.

TL;DR: Analytical design aids which take into account the stability and sensitivity properties of MIMO systems with multiple dead times are presented and it is shown that the type of dead time compensator treated here cannot be applied to unstable processes.

TL;DR: An overview of, and perspective on, human performance modelling, and a recently developed model that illustrates how features of the two approaches may be synthesized to analyze a wider range of supervisory control problems is described and discussed.

TL;DR: It is formally demonstrated that a 'forced' singular perturbation model results in the same zero-order composite feedback control solution as a classical singularly perturbed model.

TL;DR: A self-tuning regulator for a variance constrained optimal control problem is given that applies the Robbins-Monro scheme to adjust the Lagrange multiplier of the variance constrained control problem.

TL;DR: A convergence analysis of a modified version of the least-squares recursive identification algorithm with forgetting factor is given and it is shown that the parametric distance converges to a zero mean random variable.

TL;DR: A highly accurate and widely applicable tracking control system which guides a welding torch along a joint line is described, employed in the welding apparatus for penstocks with good practical results.

TL;DR: The Rauch-Tung-Streibel smoother recursion is used to derive a new smoother algorithm based upon a decomposition of the linear model dynamical equation and maximizing use of rank-1 matrix modification that parallels Bierman's forward recursive square-root information filter/ backward recursive U-D factorized covariance algorithm.

TL;DR: A computational algorithm for the identification of biases in discrete-time, nonlinear, stochastic systems is derived by extending the separate bias estimation results for linear systems to the extended Kalman filter formulation.

TL;DR: Although the paper emphasizes power generation and energy recovery area, the research on total mill energy management is reviewed and the main results are briefly presented.

TL;DR: Significant theoretical developments in discrete-time control over the past 10-15 years are reviewed in this paper, including optimal control, Riccati equations, controllability, stability, robustness, deadbeat control and minimum-time systems.