This dissertation is concerned with the position and vibration control of flexible articulated
space robots consisting of a rigid platform, two flexible arms, and a rigid
end-effector carrying a payload, all components being serially connected through
revolute joints. The mission is to carry a payload over a prescribed trajectory in the
inertial space, while suppressing the elastic vibration of the arms and the rigid-body
perturbations. The equations of motion governing the robot dynamics are derived by
means of Lagrangian mechanics and they include actuator dynamics. Based on the
assumption that the elastic deformations and the rigid-body perturbations are small
relative to the nominal trajectory-following rigid-body motions, a perturbation approach
is adopted to separate the equations into nonlinear rigid-body equations and
linear perturbation equations. The nominal trajectory is planned to conserve the
limited actuator resources and keep the platform attitude stationary, by eliminating
the inherent kinematic redundancy of the manipulator. By assuming perfect sensing,
i.e., all the states are completely accessible, two kinds of controls are designed in
discrete-time. First, a feedforward control is designed to minimize the persistent disturbance
resulting from the nominal motions. Next, a feedback control is synthesized
based on the Linear Quadratic Regulator (LQR) theory with a prescribed degree of
stability to make the system stable and further enhance the disturbance-rejection
performances. These controls are subsequently applied to the case in which only the sensor outputs are available, and they are noisy. A finite number of sensors is assumed. A Kalman filter is designed to estimate the state on the assumption of zeromean
Gaussian white plant and measurement noise. In the real situation, controls
are applied to the original plant rather than the linearized model, so that the Linear
Quadratic Gaussian (LQG) control combined with robustness recovery methods is
tested on the plant. Due to difficulties in implementing a Kalman filter, a Maximum
Likelihood Estimator (MLE) is proposed. A numerical example illustrates the approach.
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