Retrospective Cost Adaptive Control of Generic Transport Model Under Uncertainty and Failure

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143070/1/1.I010454.pd

Input and State Estimation for Discrete-Time Linear Systems with Application to Target Tracking and Fault Detection

This dissertation first presents a deterministic treatment of discrete-time input reconstruction and state estimation without assuming the existence of a full-rank Markov parameter. Algorithms based on the generalized inverse of a block-Toeplitz matrix are given for 1) input reconstruction in the case where the initial state is known; 2) state estimation in the case where the initial state is unknown, the system has no invariant zeros, and the input is unknown; and 3) input reconstruction and state estimation in the case where the initial state is unknown and the system has no invariant zeros. In all cases, the unknown input is an arbitrary deterministic or stochastic signal. In addition, the reconstruction/estimation algorithm is deadbeat, which means that, in the absence of sensor noise, exact input reconstruction and state estimation are achieved in a finite number of steps. Next, asymptotic input and state estimation for systems with invariant zeros is considered. Although this problem has been widely studied, existing techniques are confined to the case where the system is minimum phase. This dissertation presents retrospective cost input estimation (RCIE), which is based on retrospective cost optimization. It is shown that RCIE automatically develops an internal model of the unknown input. This internal model provides an asymptotic estimate of the unknown input regardless of the location of the zeros of the plant, including the case of nonminimum-phase dynamics. The input and state estimation method developed in this dissertation provides a novel approach to a longstanding problem in target tracking, namely, estimation of the inertial acceleration of a body using only position measurements. It turns out that, for this problem, the discretized kinematics have invariant zeros on the unit circle, and thus the dynamics is nonminimum-phase. Using optical position data for a UAV, RCIE estimates the inertial acceleration, which is modeled as an unknown input. The acceleration estimates are compared to IMU data from onboard sensors. Finally, based on exact kinematic models for input and state estimation, this dissertation presents a method for detecting sensor faults. A numerical investigation using the NASA Generic Transport Model shows that the method can detect stuck, bias, drift, and deadzone sensor faults. Furthermore, a laboratory experiment shows that RCIE can estimate the inertial acceleration (3-axis accelerometer measurements) and angular velocity (3-axis rate-gyro measurements) of a quadrotor using vision data; comparing these estimates to the actual accelerometer and rate-gyro measurements provide the means for assessing the health of the accelerometer and rate gyro.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145813/1/ansahmad_1.pd

Data-Driven Retrospective Cost Adaptive Control

This dissertation develops data-driven retrospective cost adaptive control (DDRCAC) and applies it to flight control. DDRCAC combines retrospective cost adaptive control (RCAC), a direct adaptive control technique for sampled-data systems, with online system identification based on recursive least squares (RLS) with variable-rate forgetting (VRF). DDRCAC uses elements of the identified model to construct the target model, which defines the retrospective performance variable. Using RLS-VRF, optimization of the retrospective performance variable updates the controller coefficients. This dissertation investigates the ability of RLS-VRF to provide the modeling information needed to construct the target model, especially nonminimum-phase (NMP) zeros, which are needed to prevent NMP-zero cancellation. A decomposition of the retrospective performance variable is derived and used to assess target-model matching and closed-loop performance. These results are illustrated by single-input, single-output (SISO) and multiple-input, multiple-output (MIMO) examples with a priori unknown dynamics. Finally, DDRCAC is applied to several simulated flight control problems, including an aircraft that transitions from minimum-phase to NMP lateral dynamics, an aircraft with flexible modes, aeroelastic wing flutter, and a nonlinear planar missile.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169972/1/aseemisl_1.pd

Adaptive Control of an Aircraft with Uncertain Nonminimum-Phase Dynamics

Abstract-This paper investigates four control architectures that use adaptive control to follow step, ramp, and harmonic roll-angle commands for a linearized aircraft model with an unknown transition from minimum-phase to nonminimumphase (NMP) dynamics. In particular, we consider retrospective cost adaptive control (RCAC) with 1) a command-feedforward control architecture; 2) an output-feedback control architecture; 3) a centralized control architecture that uses both command feedforward and output feedback; and 4) a decentralized control architecture that uses both command feedforward and output feedback. For baseline tests, we assume that the location of the NMP zero is known. The goal of this work is to improve the transient response and rate of convergence. Numerical testing shows that RCAC with the decentralized control architecture using command feedforward and output feedback gives the fastest convergence. Furthermore, resetting controller coefficients at the start of the transition improves the transient response