Identification and Optimal Linear Tracking Control of ODU Autonomous Surface Vehicle

Autonomous surface vehicles (ASVs) are being used for diverse applications of civilian and military importance such as: military reconnaissance, sea patrol, bathymetry, environmental monitoring, and oceanographic research. Currently, these unmanned tasks can accurately be accomplished by ASVs due to recent advancements in computing, sensing, and actuating systems. For this reason, researchers around the world have been taking interest in ASVs for the last decade. Due to the ever-changing surface of water and stochastic disturbances such as wind and tidal currents that greatly affect the path-following ability of ASVs, identification of an accurate model of inherently nonlinear and stochastic ASV system and then designing a viable control using that model for its planar motion is a challenging task. For planar motion control of ASV, the work done by researchers is mainly based on the theoretical modeling in which the nonlinear hydrodynamic terms are determined, while some work suggested the nonlinear control techniques and adhered to simulation results. Also, the majority of work is related to the mono- or twin-hull ASVs with a single rudder. The ODU-ASV used in present research is a twin-hull design having two DC trolling motors for path-following motion. A novel approach of time-domain open-loop observer Kalman filter identifications (OKID) and state-feedback optimal linear tracking control of ODU-ASV is presented, in which a linear state-space model of ODU-ASV is obtained from the measured input and output data. The accuracy of the identified model for ODU-ASV is confirmed by validation results of model output data reconstruction and benchmark residual analysis. Then, the OKID-identified model of the ODU-ASV is utilized to design the proposed controller for its planar motion such that a predefined cost function is minimized using state and control weighting matrices, which are determined by a multi-objective optimization genetic algorithm technique. The validation results of proposed controller using step inputs as well as sinusoidal and arc-like trajectories are presented to confirm the controller performance. Moreover, real-time water-trials were performed and their results confirm the validity of proposed controller in path-following motion of ODU-ASV

Gas turbine transient performance simulation, control and optimisation

A gas turbine engine is a complex and non-linear system. Its dynamic response changes at different operating points. The exogenous inputs: atmospheric conditions and Mach number, also add disturbances and uncertainty to the dynamic. To satisfy the transient time response as well as safety requirements for its entire operating range is a challenge for control system design in the gas turbine industry. Although the recent design of engine control units includes some advanced control techniques to increase its control robustness and adaptability to the changing environment, the classic scheduling technique still plays the decisive role in determining the control values due to its better reliability under normal circumstances. Producing the schedules requires iterative experiments or simulations in all possible circumstances for obtaining the optimal engine performance. The techniques, such as scheduling method or linear control methods, are still lack of development for control of transient performance on most commercial simulation tools. Repetitive simulations are required to adjust the control values in order to obtain the optimal transient performance. In this project, a generalised model predictive controller was developed to achieve an online transient performance optimisation for the entire operating range. The optimal transient performance is produced by the controller according to the predictions of engine dynamics with consideration of constraints. The validation was conducted by the application of the control system on the simulated engines. The engines are modelled to component-level by the inter-component volume method. The results show that the model predictive controller introduced in this project is capable of providing the optimal transient time response as well as operating the engine within the safety margins under constant or varying environmental conditions. In addition, the dynamic performance can be improved by introducing additional constraints to engine parameters for the specification of smooth power transition as well as fuel economy

Proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress

Published proceedings of the 2018 Canadian Society for Mechanical Engineering (CSME) International Congress, hosted by York University, 27-30 May 2018