Plant Initial Conditions Research Articles

Initial herbivory and exposure to herbivory-induced volatiles enhance arthropod species richness by diversifying community assemblages

Plant ecological traits affect the species identity of plant-colonizing arthropods, which in turn induces species-specific trait changes in plants, forming feedback between plants and arthropods. Such feedback can amplify initial differences in species composition, leading to large variations (i.e., high β diversity). We hypothesized that the differences in plant initial conditions have sustained effects on arthropod community composition and species richness. To test this hypothesis, we monitored arthropod community assembly on a willow tree species, Salix eriocarpa, which was experimentally manipulated into three initial treatments: undamaged (in chamber 1); damaged by the specialist leaf beetle, Plagiodera versicolora (chamber 2); and “exposed” plants that were undamaged but were exposed to volatiles from damaged plants (in chamber 2). The arrival and population dynamics of the leaf beetle were affected by the plant’s initial condition (chamber 1 vs. 2), which could result from the microscale environmental heterogeneity between chambers (chamber effect) and/or from the herbivory-related impacts (direct herbivory and exposure to induced volatiles in chamber 2). The community composition on damaged and exposed plants became significantly different on day 32. In addition, the divergence in composition between plant individuals was significantly smaller in undamaged plants (chamber 1) than in damaged and exposed plants (chamber 2) on day 60. The compositional variations (β diversity) between chambers, between treatments, and between days, comprised a large proportion (two third) of the total species richness (γ diversity) in the whole community of arthropods. These results suggest that plant initial condition is a key driver of community assembly and the maintenance of species diversity.

Multi-objective optimal control of small-size wastewater treatment plants

In this work, a multi-objective dynamic optimization of the operating strategy of a small-size wastewater treatment plant is carried out. In-situ incineration of the excess sludge produced for electricity production is investigated in order to reduce the operating costs. The trade-offs between the treatment quality and the operating costs are characterized. Compared to the literature, emphasis is put on a more rigorous formulation of the problem and an accurate modeling of the underlying phenomena so as to get physically relevant solutions. Thus, from a mathematical perspective, the problem is formulated so that the solution is less sensitive to the – arbitrarily chosen – plant initial conditions. Modeling of physical phenomena e.g. the detrimental effect of the concentration of suspended solids in the mixed liquor, on oxygen transfer rate, has been included in the model. Several constraints are added to the problem so as to maintain the optimal solutions within the limits of validity of the mathematical model. The results provided a clear picture about the trade-offs between the treatment quality and the exploitation costs. Sludge incineration was shown to be of a high energetic profit, but it does not allow the plant to be electrically autonomous.

Stabilizing a SISO LTI Plant With Gain and Delay Margins as Large as Desired

Handling delays and uncertain parameters in control systems is difficult and of long-standing interest. In this paper, we consider the problem of bounded-input bounded-output stabilizing any single-input single-output linear time-invariant controllable/observable plant with gain and time delay margins as large as desired; to do so, we propose the use of a non-linear time varying (NLTV) controller. Despite being NLTV, the proposed controller is relatively simple and the time variation is periodic; furthermore, the controller is noise tolerant, guarantees the exponential decay of the plant initial conditions, tolerates occasional jumps in the gain and delay and is robust to un-modelled dynamics.

DESIGN OF A FPGA BASED ABWR FEEDWATER CONTROLLER

A feedwater controller targeted for an ABWR has been implemented using a modern field programmable gate array (FPGA), and verified using the full scope simulator at Taipower's Lungmen nuclear power station. The adopted control algorithm is a rule-based fuzzy logic. Point to point validation of the FPGA circuit board has been executed using a digital pattern generator. The simulation model of the simulator was employed for verification and validation of the controller design under various plant initial conditions. The transient response and the steady state tracking ability were evaluated and showed satisfactory results. The present work has demonstrated that the FPGA based approach incorporated with a rule-based fuzzy logic control algorithm is a flexible yet feasible approach for feedwater controller design in nuclear power plant applications.

Predictive Iterative Learning Control using Laguerre Functions

AbstractThis paper develops a predictive iterative learning control algorithm starting from some recent results in the area of predictive repetitive control. Although similar in spirit to the original norm-optimal iterative learning control, the algorithm developed employs receding horizon control and Laguerre functions to parameterize the future control trajectory. As a result, difficulties encountered by the norm-optimal iterative control algorithm are overcome. The stability of the predictive iterative learning system is analyzed and conditions on error convergence are established. In addition, a strategy for learning the unknown plant initial conditions and the reduction of the initial plant errors is developed. A simulation example to illustrate the algorithm is also given.

Linear-quadratic and γ-optimal output control laws

The problem of linear-quadratic control in the class of linear dynamic output controllers was solved, and the necessary and sufficient conditions for existence of the optimal controllers were formulated in terms of the linear matrix inequalities. The parameters of such controllers were proved to depend on the plant initial conditions, and the optimal controllers were shown to be unrealizable under certain initial conditions. The results of numerical comparison of the linear-quadratic and γ-optimal controllers were presented.

Uncertainty analysis of LBLOCA for Advanced Heavy Water Reactor

The main objective of safety analysis is to demonstrate in a robust way that all safety requirements are met, i.e. sufficient margins exist between real values of important parameters and their threshold values at which damage of the barriers against release of radioactivity would occur. As stated in the IAEA Safety Requirements for Design of NPPs “a safety analysis of the plant design shall be conducted in which methods of both deterministic and probabilistic analysis shall be applied”. It is required that “the computer programs, analytical methods and plant models used in the safety analysis shall be verified and validated, and adequate consideration shall be given to uncertainties”. Uncertainties are present in calculations due to the computer codes, initial and boundary conditions, plant state, fuel parameters, scaling and numerical solution algorithm. All conservative approaches, still widely used, were introduced to cover uncertainties due to limited capability for modelling and understanding of physical phenomena at the early stages of safety analysis. The results obtained by this approach are quite unrealistic and the level of conservatism is not fully known. Another approach is the use of Best Estimate (BE) codes with realistic initial and boundary conditions. If this approach is selected, it should be based on statistically combined uncertainties for plant initial and boundary conditions, assumptions and code models. The current trends are going into direction of the best estimate code with some conservative assumptions of the system with realistic input data with uncertainty analysis. The BE analysis with evaluation of uncertainties offers, in addition, a way to quantify the existing plant safety margins. Its broader use in the future is therefore envisaged, even though it is not always feasible because of the difficulty of quantifying code uncertainties with sufficiently narrow range for every phenomenon and for each accident sequence. In this paper, uncertainty analysis for the Large Break LOCA (200% Inlet Header Break) of Advanced Heavy Water Reactor (AHWR) has been carried out. The uncertainty analysis was carried out for the peak cladding temperature (PCT), based on the two different methods i.e., Wilk’s method and the response surface technique. Their findings have also been compared.

Relatively optimal control and its linear implementation

Motivated by the fact that determining a feedback solution for the optimal control problem under constraints is a hard task we introduce the concept of relative optimality, roughly optimality for a specific (nominal) plant initial condition. We consider a generic discrete-time finite-horizon constrained optimal control problem for linear systems, and we seek for a state feedback (possibly dynamic) controller. As a fundamental requirement, we do not admit preactions or controller-state initialization based on the plant initial state and we assume our controller to be time-invariant. In particular, we do not consider controllers simply achieved by the feedforward and tracking of the optimal trajectory. A relatively optimal control is a stabilizing controller such that, if initialized at its zero state, produces the optimal (constrained) trajectory for the nominal initial condition of the plant. We show that one of such controllers is linear, dead-beat, and its order is equal to the length of the horizon minus the plant order, thus, of complexity which is known a priori. Some additional features such as the assignment of the compensator poles to achieve strong stabilization are proposed. We show that, by means of the proposed approach, we can face several problems such as optimal point-to-point operations, optimal impulse response and optimal tracking.

Internal behaviour provided by nonlinear l1-optimal controllers

An l 1-optimal linear time-invariant (LTI) compensator may have an order significantly higher than that of the plant, even when the state is measurable. Recently there has been work exploring the use of nonlinear static feedback controllers which provide near optimal performance. Here we consider a class of nonlinear state feedback controllers and derive superposition-like bounds on both the plant state and the controlled output in the event that the plant initial condition, the disturbance, and the noise are all non-zero.

Calculus of variations derivation of the minimax linear-quadratic (H-infinity) controller

The linear-quadratic best controller for the worst bounded disturbances (LQW controller), or so-called Hoo controller, is derived as a differential game between the controller and disturber using the calculus of variations. This derivation explicitly shows that for the full-order LQW controller the worst measurement disturbance is zero (!) and the controller initial conditions must be set equal to the plant initial conditions for a well-posed differential game. As in previous derivations, the worst process disturbance is shown to be a feedback on the plant states. The derivation yields necessary conditions for reduced-order and higher order LQW controllers, useful in multiple-plan t optimization for robustness. A helicopter near hover is used to illustrate differences between LQW and linear-quadratic-Gaussian (LQG) control. This comparison suggests the relative merits of LQG and LQW control design and shows that a special case of LQW control called infinite-disturbance LQW (optimal H«>} control is not practical.

A solution to the decentralized adaptive stabilization problem

In this paper we present for the first time a solution to the problem of designing a globally convergent truly decentralized adaptive controller for systems of arbitrary relative degree without any matching assumptions. Specifically, we prove that using Morse's new dynamic certainty equivalent adaptive controller we can determine a class of unmodelled interconnections in the face of which regulation of the plant utput to zero with internal stability is still possible. Furthermore, we prove that the output regulation happens with some guaranteed transient performance bounds. Namely, we show that the L 2 norm of the output is uniformly bounded by the initial parameter estimation error and that, if the latter and the plant initial conditions are sufficiently small, then we can ensure a bound for the L ∞ norm of the output.

Design of a PI-type state feedback optimal servo system

A method of designing a type-one optimal servo system is described. The design uses an exact model matching method with pole-zero cancellations. The initial values of the integrators are set according to the plant initial conditions at the beginning of control. This servo system is optimal in the sense that the performance index, comprising the quadratic forms of both the error vector and the deviation control vector from its steady-state, is minimized. The sensitivity to plant parameter variations can be reduced without loss of optimality for the nominal plant. Moreover, this servo system has the same robustness characteristic as typical optimal servo systems designed using different performance indices. Two numerical examples illustrate the efficiency of this servo system.

A general theory for compensating constant linear systems†

A general theory is presented for the compensation of completely controllable, completely observable linear constant systems as in eqns (1) and (2) of this paper. A Kalman observer is used to generate the state variables from the inputs and outputs of the system. State variable feedback is used as the design approach, but the Kalman observer states are used instead of the system states, which are assumed to be internal to the system, and not available for feedback to the system inputs. The system outputs are found to depend on three different transfer functions; the poles of each may be shifted at will by the designer, while the zeros may be invariant. The steady-state input-output transfer function for the closed-loop compensated system has n poles which are chosen by the designer to be anywhere in the complex plane in complex-conjugate pairs or real locations, whilst the single input-output system zeros are invariant. There are two transient responses, one due to the plant initial conditions, and the ot...

Estimation of pulse transfer function parameters by quasilinearization

An iterative algorithm for the identification of single-input single-output linear stationary discrete systems is developed using the method of quasilinearization. The resulting procedure is similar to mode 1 of the method of Steiglitz and McBride but has the advantage of the quadratic convergence property of quasilineariza-tion. It is shown that this algorithm becomes mode 1 if the measured plant output is used in the calculations in place of the model output. Consequently, the two methods are extremely compatible and it is a simple matter to combine them in a single program, which generates its own initial estimates has the wide range of convergence of mode 1, and possesses the quadratic convergence property of quasilineariza-tion for final convergence to a solution. The method also permits the estimation of plant initial conditions in those cases where they must be considered. Results of a few numerical applications are discussed.