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An optimal estimation strategy is developed for post-experiment estimation of discretely measured dynamic systems which accounts for system model errors in a much more rigorous manner than Kalman filter-smoother type methods. The Kalman filter-smoother type methods, which currently dominate post-experiment estimation practice, treat model errors via “process noise", which essentially shifts emphasis away from the model and onto the measurements. The usefulness of this approach is subject to the measurement frequency and accuracy. The current method treats model errors by use of an estimation strategy based on concepts from optimal control theory. Unknown model error terms are explicitly included in the formulation of the problem and estimated as a part of the solution. In this manner, the estimate is improved; the model is improved; and an estimate of the model error is obtained. Implementation of the current method is straightforward, and the resulting state trajectories do not contain jump discontinuities as do the Kalman filter-smoother type estimates. Results from a number of simple examples, plus some examples from spacecraft attitude estimation, are included. The current method is shown to obtain significantly more accurate estimates than the Kalman filter-smoother type methods in many of the examples. The difference in accuracy is accentuated when the assumed model is relatively poor and when the measurements are relatively sparse in time and/or of low accuracy. Even for some well-modeled, densely measured applications, the current method is shown to be competitive with the Kalman filter-smoother type methods. / Ph. D. / incomplete_metadata

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

In this thesis the problem of autonomous landing of an unmanned aerial vehicle named AE-2 is addressed. The guidance and control technique is developed and demonstrated through numerical simulation results. The complete work includes Mathematical modeling, Control design, Guidance and State estimation for AE-2, which is a fixed wing vehicle with 2m wing span and 6kg weight. The aerodynamic data for AE-2 is available from static wind tunnel tests. Functional fit is done on the wind tunnel data with least squares method to find static aerodynamic coefficients. The aerodynamic forces and moment coefficients are highly nonlinear some of them are partitioned in two zones based on the angle of attack. The dynamic derivatives are found with Athena Vortex Lattice software. For the validation of vortex lattice method the static derivatives obtained by the wind tunnel tests and vortex lattice method, are compared before finding dynamic derivatives. The dynamics of the servo actuators for the aerodynamic control surfaces is incorporated in the simulation. The nonlinear dynamic inversion technique has been used for the guidance and control design. The control is structured in two loops, outer and inner loop. The goal of outer loop is to track the guidance commands of altitude, roll angle and yaw angle by converting them into body rate commands through dynamic inversion. The inner loop than tracks these commanded roll rate, pitch rate and yaw rate by finding the required deflection of control surfaces. The forward velocity of the vehicle is controlled by varying the throttle. A controller for actuator is also designed to reduce the lag. The guidance for landing consists of three phases approach, glideslope and flare. During approach the vehicle is aligned with the runway and guided to a specified height from where the glideslope can begin. The glideslope is straight line path specified by a flight path angle which is restricted between 3 to 4 degree. At the end of glideslope which is marked by flare altitude the flare maneuver begins which is an exponential curve. The problem of transition between the glideslope and flare has addressed by ensuring continuity and smoothness at transition. The exponential curve of flare is designed to end below the ground so that it intersects the ground at a prespecified point. The sink rate at touchdown is also controlled along with the location of touchdown point. The state estimation has been done with Extended Kalman Filter in continuous discrete formulation. The external disturbances like wind shear and wind gust are accounted by appending them in state variables. Further the control design with guidance is tested from various initial conditions, in presence of wind disturbances. The designed filter has also been tested for parameter uncertainty.