Per Olof Gutman

Controlling mechanical systems with backlash-a survey

Backlash is one of the most important non-linearities that limit the performance of speed and position control in industrial, robotics, automotive, automation and other applications. The control of systems with backlash has been the subject of study since the 1940s. This survey reveals that surprisingly few control innovations have been presented since the early path breaking papers that introduced the describing function analysis of systems with backlash. Promising developments are however taking place using adaptive and non-linear control strategies.

Decentralized output-feedback MRAC of linear state delay systems

In this note, we develop coordinated decentralized output-feedback adaptive controllers for a class of large-scale systems with state time delays in the subsystems and in the interconnections. We present a decentralized model reference adaptive control scheme which requires an exchange of signals between the different reference models, but does not involve the exchange of output signals between the different subsystems. It can not only guarantee closed-loop stability but also asymptotic zero tracking errors when uncertainties and delays are present in the subsystems and interconnections. Closed-loop signal boundedness and asymptotic output-feedback tracking are proven analytically and verified by simulation.

Optimal CO2 control in a greenhouse modeled with neural networks

Abstract CO2 enrichment in warm climates requires a delicate balance between the need to ventilate and the desire to enrich. Model-based optimization can achieve this balance, but requires reliable models of the greenhouse environment and of the crop response. This study assumes that the crop response is known, and focuses on the greenhouse model. Neural network greenhouse models were trained using data collected over two summer months in a small greenhouse. The models were reduced to minimum size, by predicting separately the temperature and CO2 concentration, and by eliminating any unessential input. The resulting models not only fit the data well, they also seem qualitatively correct, and produce reasonable optimization results. Using these models, the effect of evaporative cooling on extending the enrichment duration is demonstrated.

Robust Adaptive Output-Feedback Tracking for a Class of Nonlinear Time-Delayed Plants

Within the model reference adaptive control (MRAC) framework, a continuous adaptive output-feedback control scheme is developed for a class of nonlinear SISO dynamic systems with time delays which is robust with respect to unknown time-varying plant delays and to an external disturbance with unknown bounds. A special form of the Lyapunov-Krasovskii functional with a “virtual” adaptation gain vector is introduced to prove stability.

Implicit improved vertex control for uncertain, time-varying linear discrete-time systems with state and control constraints

The problem of regulating an uncertain and/or time-varying linear discrete-time system with state and control constraints to the origin is addressed. It is shown that feasibility and a robustly asymptotically stable closed loop can be achieved using an interpolation technique. The design method can be seen as an alternative to optimization-based control schemes such as Robust Model Predictive Control. Especially for problems requiring complex calculations to find the optimal solution, the present method can provide a straightforward suboptimal solution. A simulation demonstrates the performance of this class of constrained controllers.

Output feedback model reference adaptive control for multi-input–multi-output plants with state delay

Two new output feedback adaptive control schemes based on Model Reference Adaptive Control (MRAC) and adaptive laws for updating the controller parameters are developed for a class of linear multi-input–multi-output (MIMO) systems with state delay. An effective controller structure established on a new error equation parametrization is proposed to achieve tracking with the error tending to zero asymptotically. To achieve exact asymptotical tracking, we introduce, in the standard MRAC structure for plants without delay, a new additional adaptive feedforward control component as an output of a dynamical system driven by the reference signal. Adaptive laws are developed using the SPR-Lyapunov design approach and two assumptions regarding the prior knowledge of the high-frequency matrix Kp. This work is the first asymptotic exact zero tracking results for this class of systems in the framework of the certainty equivalence approach.

A linear programming regulator applied to hydroelectric reservoir level control

Abstract For linear dynamical systems with linear state and control constraints the regulator problem can be formulated as a linear programming problem. A regulator, built around a standard LP program and operating in the Open Loop Optimal Feedback (OLOF) fashion, is presented. The LP-OLOF regulator was implemented on a VAX 11/780 computer to control, in real time, a double water tank laboratory process, and the water level of a hydroelectric power station reservoir. The reservoir control experiment showed that even with an assumed simple process model, satisfactory performance was achieved. In particular it was beneficial that the LP-OLOF regulator allows dynamic changes of the water flow bounds corresponding to the number of generators available at different operating conditions, and that the regulator can predict the water level.

Optimal Steady-State Control for Isolated Traffic Intersections

A simplified isolated controlled vehicular traffic intersection with two movements is considered. A discrete-event max-plus model is proposed to formulate an optimization problem for the green-red switching sequence. In the case when the criterion is a strictly increasing, linear function of the queue lengths, the problem becomes a linear programming problem. Also, in this case, the steady-state control problem can be solved analytically. A sufficient and necessary condition for steady-state control is derived, and the structure of optimal steady-state traffic control is revealed. Our condition is the same as the necessary condition in for both queue lengths to be non-increasing at an isolated intersection.

Robust controllers for simultaneous control of temperature and CO2 concentration in greenhouses

Abstract The present work focuses on the control of greenhouse air temperature and CO 2 concentration by means of simultaneous ventilation and enrichment. Such an operation, which may a priori seem contradictory, was shown by several authors to be required to maintain optimal temperature and CO 2 setpoints. The control process is divided into two distinct control loops, the first maintaining the temperature by adjusting the ventilation, and the second maintaining the CO 2 concentration by adjusting the enrichment. The CO 2 concentration controller assumes the ventilation rate to be constant and approximately known over 2-min intervals. Implementation in an experimental greenhouse shows the ability of the controllers to meet the requirements.

Dominant parameter selection in the marginally identifiable case

Often a rather limited set of experimental data is available for the identification of a dynamic model, which contains many parameters. This is, e.g. the usual case for crop growth models. In this situation, only some parameter values can be estimated. Based on an analysis of the Fisher information matrix, a method for a reasonable selection of parameters is suggested here. The method chooses the most sensitive parameters, i.e. those to which the model under the considered experimental conditions is most sensitive, and excludes both coupled parameters and those that exhibit multiplecorrelation. A comparison with different ridge regression methods is made. The methodology is illustrated with a simple lettuce growth model.