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1

Sequential target tracking using constrained nonlinear estimators

Stubberud A.R.

Systems Science

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2003

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Vol. 29, nr 2

17-29

EN

An estimator is presented which generates sequential estimates for nonlinear, time-variable discrete-time dynamic systems in which the system state estimates are subject to an instantaneous constraint. That is, at each sample time the state estimate is constrained to lie in a given region of the state space. This nonlinear sequential estimator is an extended version of an optimal sequential estimator for linear, time-variable discrete-time systems with state estimates constrained to a given region of the state space. The linear estimator was developed from a non-probabilistic weighted linear least squares basis with the constraints added through the mechanism of Lagrange multipliers; therefore, the estimator produces "hard" constraints on the state estimate. The solution of the constrained estimation problem, at each instant of time, requires only the unconstrained state estimate at that time instant and the instantaneous constraints which define the constraint region. If the unconstrained sequential estimate satisfies the constraints, then that solution is also the constrained solution. On the other hand, if the unconstrained estimate does not satisfy the constraints, then the constrained solution is generated from the solution of a set of static equations. The constrained estimation problem is thus reduced to a sequence of nonlinear programming problems. The estimator for the state of a nonlinear system was developed by quasi-linearization of the optimal constrained linear estimator. The estimates resulting from this estimator are "optimal in the small" for nonlinear systems and are optimal for linear systems.

2

Self-tuning control under input variance constraint

Królikowski A., Kubiak T., Lemos J.M.

Systems Science

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2003

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Vol. 29, nr 1

135-149

EN

In this paper, a variance-constrained self-tuning control is considered for a plant given by discrete-time ARMAX model. A minimization of a quadratic cost function under constraint is approached by LQG and stochastic approximation (SA) methods, as well as by MUSMAR, a predictive adaptive controller based on multiple identifiers. The optimization algorithms obtained are simulated for unstable plant model and different structures of the controller.

3

MPC: A paradigm shift for automatic control

Maciejowski J. M.

Systems Science

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2000

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Vol. 26, nr 4

49-59

EN

Model Predictive Control (MPC) represents a major paradigm shift in the field of automatic control. This radically affects synthesis techniques (illustrated by control of an unstable system) and underlying concepts (illustrated by control of a multivariable system), as well as lifting the Control engineer's focus from prescriptions to specifications ("what" not "how", illustrated by emulation of a conventional autopilot). Part of the objective of this paper is to emphasise the significance of this paradigm shift. Another part is to consider the fact that this shift was missed for many years by the academic community, and what this tells us about teaching and research in the field.

4

Modeling and motion planning for a class of weight handling equipment

Kiss B., Levine J., Mullhaupt P.

Systems Science

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2000

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Vol. 26, nr 4

79-92

EN

A unified framework for the modeling of a class of weight handling equipment (WHE) is presented. The dynamic equations are obtained using Lagrange multipiers associated to geometric constraints between generalized coordinates. This approach provides a simple way to show differential flatness for all WHEs of the class. The flatness property can then be exploited for motion planning.