Showing papers in "Automatica in 1982"

TL;DR: The important new feature of the proposed algorithms is their ability to ignore redundant data and the efficient data extraction property of the new algorithms is achieved with small computational effort and with improved performance when compared to the least square algorithm.

TL;DR: It is shown that systems composed of cascade, feedforward, feedback and multiplicative connections of linear dynamic and zero memory nonlinear elements can be identified in terms of the individual component subsystems from measurements of the system input and output only.

TL;DR: A dynamical model of container cranes is derived by using Lagrange's equation and a new algorithm which is employed for computing the optimal control is explained in detail.

TL;DR: The problem of state estimation and system structure detection for discrete stochastic dynamical systems with parameters which may switch among a finite set of values is considered and a unified treatment of the existing suboptimal algorithms is provided.

TL;DR: Output and equation error adaptive identification algorithms and an adaptive control algorithm are shown to be exponentially convergent under a deterministic or stochastic persistently exciting condition on the reference trajectory together with some standard conditions.

TL;DR: A close link is established between the stability and closed loop properties of optimizing type and feedback type of control laws and the performance 'measures' of robustness of such systems are proposed.

TL;DR: An introduction to adaptive (self-tuning) control algorithms with recursive parameter estimation, which have obtained increasing attention in recent years, and the introduction of a third feedback level for coordination and supervision is considered.

TL;DR: This work first investigates the deterministic version of the incentive problem, which reveals the basic simple idea behind the problem and illustrates the different possibilities introduced by the presence of dynamics and multi-follower nature of the problem.

TL;DR: It was shown that the Michaelis-Menten model is not practically identifiable and a recursive state-estimation algorithm for control and supervision purposes was developed and its application in combination with both the aggregated and the age distribution model was demonstrated.

TL;DR: A performance index is developed which is minimized by the RHFC (inverse optimal control problem), and similar controllers for which the horizon distance is an easily computed explicit function of the state are developed.

TL;DR: The infinite horizon optimal control problem is considered in the general case of linear discrete time systems and quadratic criteria, both with stochastic parameters which are independent with respect to time.

TL;DR: This paper presents an optimization algorithm which permits all the constraints of control system design requirements to be considered, involving the singular values of a system or return difference transfer function matrix.

TL;DR: It is proved that a prediction error method will give better accuracy and that an instrumental variable technique may give better or worse accuracy depending on the actual noise correlation.

TL;DR: It is shown that, with probability one, the algorithm will ensure that the system inputs and outputs are sample mean square bounded and the mean square output tracking error achieves its global minimum possible value for linear feedback control.

TL;DR: This study leads to an understanding of the generic difficulty associated with the application of overspeed protection control to large coal fired power plants and provides a feedback control structure with considerable promise for such applications.

TL;DR: An algorithm is described for the selection of model structure for identifying state-space models of 'black box' character and it is discussed how the model structure selection algorithm can be interfaced with an off-line identification procedure.

TL;DR: The identifiability of multiple input-multiple output stochastic systems operating in closed loop is considered for the case where the plant and the regulator are both linear and time-invariant.

TL;DR: Coherency with respect to a set of slowest modes and an earlier developed grouping algorithm are used to identify weakly coupled areas, as demonstrated on a 48-machine model of the United States North-East power system.

TL;DR: A new method for estimating the parameters of an ARMA process is presented, which consists of three linear least-squares estimations, and it is shown that the resultant estimator is 'p-consistent' and ' p-efficient' (the asymptotic efficiency approaches the theoretical maximum as p tends to infinity).

TL;DR: This paper suggests how nonlinear adaptive control might lead to improved control of the dissolved oxygen (DO) concentration in the aerator of a wastewater treatment plant.

TL;DR: The definition presented combines dynamic programming, the theory of bargaining and the notion of enforceable agreements to produce a class of cooperative solutions defined in the form of memory Nash equilibria satisfying the principle of optimality along the equilibrium trajectory.

TL;DR: Recent algorithms for solving design problems having infinite dimensional constraints of the form @f(z, @a) @?

TL;DR: It is proved that if the maximum eigenvalue of the inverse of the gain matrix F"k has an upper bound and a non-zero lower bound then the global convergence of the control algorithm is insured.

TL;DR: Design of observers and observer-based controllers for linear, discrete control systems with fast and slow modes are considered and it is shown that a low-order observer- based controller can be designed with independent gain matrices to stabilize the original discrete system.

TL;DR: Some asymptotically ideal feedback properties are demonstrated, such as the complete desensitization, the complete servo performance with decoupling and the complete disturbance rejection, which formulate the loop-tightness for multivariable systems.

TL;DR: The similarities between model reference adaptive controllers (MRAC) and S-STURE for minimum phase plants where the control objectives are specified by reference models and stochastic self-tuning regulators (S-STURES) permit the extension of the duality existing in the linear case with known parameters between modal control and minimum variance control.

TL;DR: A time-optimal control algorithm for digital computer control allowing bounds on control variables and state variables is presented and an application of the algorithm to a sixth-order multivariable system is given.

TL;DR: Active and passive vehicle suspensions viewed as control actuators are discussed using the notion of passivity developed in network theory to show that for the given quadratic performance index, only active optimal suspension realizations are possible.

TL;DR: It is shown that the proposed scheme is in fact the asymptotic limit of the exact solution as the small parameters go to zero.