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Within a recent development of algorithms to establish local structural identifiability, local observability and local strong accessibility of nonlinear systems, it turned out that sensitivities, governed by linear time-varying dynamics, are fundamental. As to local strong accessibility of nonlinear systems, the algorithm essentially checks controllability of linearizations along trajectories of the nonlinear system. In the literature concerning local controllability of nonlinear systems on the other hand, examples are regularly presented illustrating that controllability of linearizations is a stronger property than local strong accessibility. This paper clarifies these apparently contradicting results by using an important theoretical result from the literature and several illustrative examples. These reveal that the Lie algebraic rank condition (LARC), that is currently used to check local strong accessibility, may indeed be replaced by a rank condition based on sensitivities (SERC), that essentially checks controllability of linearizations along trajectories, provided that these trajectories are taken to be non-singular. This replacement is important for two reasons. One is that the computation of LARC requires a finite, but a-priori unknown number of steps, which may be very large. The other is especially important for large-scale nonlinear systems, for which the large number of symbolic differentiations involved in LARC results in excessive computation times or even renders the calculation infeasible. Both phenomena are also illustrated with examples.

The synthesis of finite-horizon digital optimal reduced-order compensators is presented, for asynchronous and aperiodically sampled continuous-time systems. The dimensions of the compensator state are a priori fixed and may be time varying. Asynchronous and aperiodic sampling refers to a deterministic sampling scheme where an arbitrary, but a priori known, number of control variables is updated, and/or an arbitrary, but a priori known, number of outputs is sampled, at arbitrary, but a priori known, time instants. This sampling scheme generalizes most deterministic sampling schemes considered in the control literature. Through the use of an integral criterion the intersample behaviour is explicitly considered in the design. As a result, frequent, synchronous and periodic sampling is no longer necessary, which can be highly relevant in practice. Also the synthesis enables comparison of the optimal performance of reduced-order compensators as a function of their dimensions and the sampling scheme. The synthesis is illustrated with a numerical example

A wide range of various classical and modern control methods is classified from the point of view of required information and put in the context of optimization.

We will argue in this paper that the nature and magnitude of model uncertainty dictate the appropriateness of the control system design methodology. To obtain these arguments, we will pursue optimality of the control system design. Note this is a philosophical paper, in which we present our arguments in qualitative terms. We will identify the circumstances under which several control system design methodologies are appropriate. The design methodologies that will be considered are (1) optimal control and optimal feedback design, (2) model predictive control (receding horizon control), (3) active and passive adaptive control and (4) robust control. Robust control strategies are conservative and limit the possible improvements that can be obtained over say classical PID control. However, it will turn out that for certain types of control problems, improvements in performance are indeed limited. Roughly speaking, the area where we may expect (significant) improvement from advanced (optimal) control system design is very much limited by the uncertainty of the systems model. Miracles are not to be expected from advanced control. What can be expected is reflected in an active adaptive optimal control scheme introduced and discussed in this paper.

A solar greenhouse has been designed that maximizes solar energy use and minimizes fossil energy consumption. It is based on a conventional Venlo greenhouse extended with a heat pump, a heat exchanger, an aquifer and ventilation with heat recovery. The aim is to minimize fossil energy consumption, while maximizing crop dry weight, keeping temperature and humidity within certain limits and satisfying a temperature integral. These requirements are defined in a goal function, which is minimized by optimal control. A greenhouse with crop model is used to simulate the process behaviour to determine closed loop optimal control trajectories. The boiler use is reduced, thereby reducing fossil energy use. Gas use is decreased by 77% compared to a conventional greenhouse with optimal control.

A new algorithm is presented to solve both the finite-horizon time-varying and infinite-horizon time-invariant discrete-time optimal reduced-order LQG problem. In both cases the first order necessary optimality conditions can be represented by two non-linearly coupled discrete-time Lyapunov equations, which run forward and backward in discrete time. The algorithm iterates these two equations forward and backward in discrete time respectively, until they converge. In the finite horizon time varying case the iterations start from boundary conditions and the forward and backward in time recursions are repeated until they converge. The discrete-time recursions are suitable for UDU factorisation. It is shown how UDU factorisation increases both the numerical efficiency and accuracy of the recursions. By means of a numerical example, two computer experiments and the benchmark problem proposed by the European Journal of Control, the results obtained with the new algorithm are compared to results obtained with algorithms that iterate the strengthened discrete-time optimal projection equations forward and backward in time. The convergence of both these algorithms is highly improved by an automatic procedure, introduced in this paper, to adjust the numerical damping, present in each algorithm. Furthermore the convergence properties are shown to be comparable. Especially when the reduced compensator dimensions are significantly smaller than those of the controlled system, the algorithm presented in this paper is more efficient.

So far, researchers have developed simplified models to control the so called Acurex field, which is a solar power plant situated in Tabernas in Spain, used mainly for research purposes. Most of these simplified models for control have been used in simulation. Some have also been used in control experiments. Usually an advanced model of the plant was used to identify and verify the simplified model. This advanced model is a finite dimensional approximation of the physical description of the plant by two partial differential equations. In this paper we use the advanced model itself to control the plant. Despite the high dimension of the model our digital optimal reduced-order controller design procedure enables us to synthesize a digital optimal reduced-order controller with just one state variable. This controller is shown to work properly in simulation and also in a control experiment

The mass flow of fertilizer spreaders must be calibrated for the different types of fertilizers used. To obtain accurate fertilizer application manual calibration of actual mass flow must be repeated frequently. Automatic calibration is possible by measurement of the actual mass flow, based on dynamic weighing of the spreader. This paper describes the design and test of a dynamic weighing system. Under field conditions, this system was able to provide force due to spreader weight readings that have a standard deviation of 20 N, over a measurement range of 6-20 kN, at a maximum time delay of 1 s. Time-stamped data logging of the spreader weight and the theoretical application rate combined with position information allows determination of the realized spatial application of fertilizer. A comparison can be made between realized and prescribed application rates. This information is necessary for evaluation of fertilizer management in a precision agriculture farming system and to accurately target fertilizer application to crop and soil status. (C) 2001 Silsoe Research Institute.