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Most data based state and parameter estimation methods require suitable initial values or guesses to achieve convergence to the desired solution, which typically is a global minimum of some cost function. Unfortunately, however, other stable solutions (e.g., local minima) may exist and provide suboptimal or even wrong estimates. Here we demonstrate for a 9-dimensional Lorenz-96 model how to characterize the basin size of the global minimum when applying some particular optimization based estimation algorithm. We compare three different strategies for generating suitable initial guesses and we investigate the dependence of the solution on the given trajectory segment (underlying the measured time series). To address the question of how many state variables have to be measured for optimal performance, different types of multivariate time series are considered consisting of 1, 2, or 3 variables. Based on these time series the local observability of state variables and parameters of the Lorenz-96 model is investigated and confirmed using delay coordinates. This result is in good agreement with the observation that correct state and parameter estimation results are obtained if the optimization algorithm is initialized with initial guesses close to the true solution. In contrast, initialization with other exact solutions of the model equations (different from the true solution used to generate the time series) typically fails, i.e. the optimization procedure ends up in local minima different from the true solution. Initialization using random values in a box around the attractor exhibits success rates depending on the number of observables and the available time series (trajectory segment)., Comment: 15 pages, 2 figures

Utilizing the information in observations of a complex system to make accurate predictions through a quantitative model when observations are completed at time $T$, requires an accurate estimate of the full state of the model at time $T$. When the number of measurements $L$ at each observation time within the observation window is larger than a sufficient minimum value $L_s$, the impediments in the estimation procedure are removed. As the number of available observations is typically such that $L \ll L_s$, additional information from the observations must be presented to the model. We show how, using the time delays of the measurements at each observation time, one can augment the information transferred from the data to the model, removing the impediments to accurate estimation and permitting dependable prediction. We do this in a core geophysical fluid dynamics model, the shallow water equations, at the heart of numerical weather prediction. The method is quite general, however, and can be utilized in the analysis of a broad spectrum of complex systems where measurements are sparse. When the model of the complex system has errors, the method still enables accurate estimation of the state of the model and thus evaluation of the model errors in a manner separated from uncertainties in the data assimilation procedure.

The Middeck Active Control Experiment (MACE) is a NASA In-Step and Control Structure Interaction (CSI) Office funded Shuttle middeck experiment. The objective is to investigate the extent to which closed-loop behavior of flexible spacecraft in zero-gravity (0-g) can be predicted. This prediction becomes particularly difficult when dynamic behavior during ground testing exhibits extensive suspension and direct gravity coupling. On-orbit system identification and control reconfiguration is investigated to improve performance which would otherwise be limited due to errors in prediction. The program is presently in its preliminary design phase with launch expected in the summer of 1994. The MACE test article consists of three attitude control torque wheels, a two axis gimballing payload, inertial sensors and a flexible support structure. With the acquisition of a second payload, this will represent a multiple payload platform with significant structural flexibility. This paper presents on-going work in the areas of modelling and control of the MACE test article in the zero and one-gravity environments. Finite element models, which include suspension and gravity effects, and measurement models, derived from experimental data, are used as the basis for Linear Quadratic Gaussian controller designs. Finite element based controllers are analytically used to study the differences in closed-loop performance as the test article transitions between the 0-g and 1-g environments. Measurement based controllers are experimentally applied to the MACE test article in the 1-g environment and achieve over an order of magnitude improvement in payload pointing accuracy when disturbed by a broadband torque disturbance. The various aspects of the flight portion of the experiment are also discussed.

The effects of gravity on the actuators, sensors and structural plant of a controlled flexible structure are investigated. These influences include gravity stiffening, gravity induced deformations, gravity influence on inertial sensors and actuators, and the interaction with the suspension system. For each of the four influences, a simplified model is derived, from which the appropriate non-dimensional parameter can be identified, and the magnitude of the influence estimated. The construction of a detailed numerical model, which includes the four gravity influences, is outlined. This modeling procedure is then applied to a simplified model of the Middeck Active Control Eperiment (MACE). The influence on the poles and input-output transfer function is examined for several variations in the plant. The modeling procedure is also applied to the MACE experimental hardware. Experimental and model-based transfer functions are compared to demonstrate the significant improvement by incorporating gravity effects.

Viewgraphs on the Middeck Active Control Experiment (MACE): Gravity and Suspension Effects are presented. Topics covered include: gravity and suspension influences; gravity and suspension effects on structure; gravity effects on sensors and actuators; modeling of gravity and suspension effects on structure; geometric stiffness theory and initial static deformation calculation; modeling gravity's effect on accelerometers and PMA's; application to MACE; MACE EM configuration study; and MACE DM configuration study.

The effects of gravity on the dynamic performance of structural control actuators and sensors are dual forms of an additive perturbation that can attenuate or amplify the device response (input or output). The modal modeling of these perturbations is derived for the general case of arbitrarily oriented devices and arbitrarily oriented planes of deformation. A nondimensional sensitivity analysis to identify the circumstances under which the effects of gravity are important is presented. Results show that gravity effects become important when the product of the ratio of the normalized modal slope and the modal displacement is comparable to the ratio of the gravitational acceleration and the product of the beam length and the squared eigenfrequency for a given mode.