Optimally-robust nonlinear control of a class of robotic underwater vehicles
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The subject of this dissertation is the optimally-robust nonlinear control of a class of robotic underwater vehicles (RUVs). The RUV class is characterized by high fineness ratios (length-to-diameter), axial symmetry, and passive roll stability. These vehicles are optimized for robotic applications needing power efficiency for long-range autonomous operations and motion stability for sensor performance improvement. A familiar example is the REMUS vehicle. The particular robot class is further identified by an inconsistent actuator arrangement where the number of inputs is fewer than the number of degrees of freedom, by the loss of controllability at low surge speeds due to the use of fin-based control actuation, and by an inherent heading instability. Therefore, this important type of RUV comprises an interesting and challenging class of systems to study from a control theoretic perspective. The optimally-robust nonlinear control method combines sliding mode control with stochastic state and model uncertainty estimation. First a regular form sliding mode control law is developed for the heading and depth control of the RUV class. The Particle Filter algorithm is then modified and applied to the particular case of estimating not only the RUV state for control feedback but also the functional uncertainty associated with partially modeled shallow water wave disturbances. The functional uncertainty estimate is used to dynamically adjust the sliding mode controller performance term gain according to the estimate of the wave phase and the RUV’s orientation with respect to the predominate wave direction. As a result, the RUV experiences increased performance over constant gain and Kalman Filter methods in terms of heading stability which increases effectiveness and decreased actuator power consumption which increases the RUV mission time. The proposed technique is general enough to be applied to other systems. An experimental RUV was designed and constructed to compare the performance of the regular form sliding mode controller with the conventional PID-type controller. It is demonstrated that the more complicated formulas of the regular form sliding mode controller can still be implemented real-time in an embedded system and that the controller’s performance with regard to modeling uncertainty justifies the added complexity.