D.J. Mook

Automatic Carrier Landing System Utilizing Aircraft Sensors

A new closed-loop control system is developed and evaluated for use in automatic carrier landings. The system is based on a new tracking filter that uses angle of attack and airspeed measurements from the airplane, in addition to the usual ship-based radar measurements. The filter dynamic model is based on actual flight dynamics, using the additional measurements, and thus achieves significant radar noise sensitivity reduction by eliminating the existing dependence on numerical differentiation of the radar output. An additional feedback loop that blends aircraft model estimates with radar measurements is also added to the system. Nonlinear optimization techniques are used to determine a set of optimal filter and control gains for the entire closed-loop system. A detailed digital computer simulation, verified with available flight data, indicates that the use of the flight-dynamics-based tracking filter and the addition of the feedback loop dramatically improves the noise rejection sensitivity in the automatic carrier landing system.

Improved noise rejection in automatic carrier landing systems

A technique for reducing the effect of sensor noise in a closed-loop control system is developed. The effect of radar noise on an automatic carrier landing system is studied using digital computer simulation techniques. A noise rejection filter, which blends model estimates of the plane’s vertical velocity and acceleration with altitude information obtained by radar, is added to the system to decrease the sensitivity to noise. However, this results in an increased turbulence response. A control/filter variable optimization is then performed to prevent degradation of the system’s response to turbulence while simultaneously achieving high noise rejection and adequate transient response.

Robust modal identification/estimation of the Mini-Mast testbed

The Mini-Mast is a 20 meter long three-dimensional, deployable/retractable truss structure designed to imitate future trusses in space. Presented here are results from a robust (with respect to measurement noise sensitivity), time domain, modal identification technique for identifying the modal properties of the Mini-Mast structure even in the face of noisy environments. Three testing/analysis procedures are considered: sinusoidal excitation near resonant frequencies of the Mini-Mast, frequency response function averaging of several modal tests, and random input excitation with a free response period.

A novel approach to model determination using the minimum model error estimation

The purpose of this paper is to present an algorithm for the combination of a proven nonlinear system identification technique, the minimum model error estimation algorithm (MME) with an analysis of variance (ANOVA) correlation routine where a forward stepwise procedure is implemented. The analysis of variance approach to model identification is well documented primarily in social science literature but has been sparsely written about for engineering applications. This paper shows a significant improvement in nonlinear model identification when used in conjunction with the MME algorithm.

Integrated Estimation/Identification Using Second-Order Dynamic Models

An algorithm is presented which accurately identifies multi-input-multi-output systems characterized by vibrating structures. More specifically, an identification technique is integrated with an optimal estimator in order to develop an algorithm which is robust with respect to measurement and process noise. The unique functional form of the integrated approach utilizes systems described by second-order models. Therefore, theoretical mass, damping, and stiffness matrices, associated with lumped parameter models, are tailored with experimental time-domain data for system estimation and identification. This leads to an algorithm that is computationally efficient, producing realizations of complex multiple degree-of-freedom systems. The combined estimation/identification algorithm is used to identify the properties of an actual flexible truss from experimental data. Comparison of experimental frequency-domain data to the predicted model characteristics indicates that the integrated algorithm produces near-minimal realizations coupled with accurate modal properties.

Correlation techniques in robust nonlinear system realization/identification

The fundamental challenge in identification of nonlinear dynamic systems is determining the appropriate form of the model. A robust technique is presented in this paper which essentially eliminates this problem for many applications. The technique is based on the Minimum Model Error (MME), optimal estimation approach. A detailed literature review is included in which fundamental differences between the current approach and previous work is described. The most significant feature of the current work is the ability to identify nonlinear dynamic systems without prior assumptions regarding the form of the nonlinearities, in contrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. Model form is determined via statistical correlation of the MME optimal state estimates with the MME optimal model error estimates. The example illustrations indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.

Damage Assessment through Nonlinear Structural Identification

Motivated by the problem of damage assessment of existing structures, a procedure is developed in this paper for the nonlinear systems identification problem which is based on the minimum model error approach combined with correlation tests and least squares. This procedure enables the form of the damage model to be accurately determined, thus eliminating the requirement that the form be assumed a priori. Once the form is determined, the parameters involved in the nonlinear term can be estimated using least squares or other parameter estimation procedures.

A robust transcutaneous electro-muscle stimulator (RTES): a multi-modality tool

This paper introduces a transcutaneous electro-muscle stimulator design that has a wide range of output capabilities. Because of this, the unit is referred to as a robust transcutaneous electro-muscle stimulator (RTES). The RTES is a constant current stimulator that is designed to be capable of generating significant tetanic contractions from large muscle groups, such as the quadriceps. It is capable of generating complex current pulse profiles and has been tested at pulse frequencies greater than 7500 Hz. It is routinely used to generate rectangular, bi-phasic pulses in muscle-modelling studies in ranges of widths from 3 to 1000 microseconds, amplitudes from -50 to +50 mA and frequencies from 10 to 60 Hz. The design extrema on pulse width and amplitude, are 1000 microseconds and +/- 100 mA respectively. Because of the stimulators robust output characteristics, it is suitable for many types of electro-stimulation studies including pain management, edema reduction and more.

Realization of closed-loop specific optimal control

Designing nonlinear control systems warrants considerable effort under most circumstances. This fact is accentuated when one considers the optimal control problem. In this paper we address synthesis of specific optimal control for general nonlinear, fixed structure systems. By considering the stability relative to optimal open-loop solutions, we arrive at conditions sufficient for a given policy to produce optimal or suboptimal performance in a closed-loop configuration. The approach is applicable to any system and performance index whose open-loop solution may be obtained. A bound for the departure of the propagated closed-loop state from optimum is given as a function of time, control policy, and initial condition. An example problem is also given.

A real-time model error filter and state estimator

In this paper, a new real-time filter and state estimator is developed which provides a method of determining optimal state estimates in the presence of significant error in the assumed (nominal) model. Also, the new algorithm is able to determine actual model-error time histories using sequential measurements. The real-time filteristate estimator is derived for continuous systems. The functional form for this case involves the determination of an algebraic Riccati equation, a Lyapunov equation, and a linear equation to determine the gain matrix used in the filter design. Three examples are shown which demonstrate the usefulness of this new algorithm. The lirst example involves the estimation of a single state using no assumed model. The second example involves the estimation of the nonlinear trajectory of Van der Pols equation using a linear state model matrix. The third example involves the estimation of the orientation of a highly maneuverable fighter aircraft using an inaccurate system model. Results indicate that this new algorithm is able to determine accurate state estimates in the presence of significant errors in the assumed model.