A L1-LEMPC hierarchical control structure for economic load-tracking of super-critical power plants.

The super-critical thermal power plants are undertaking more and more responsibilities for the balance of the power grid intermittency of the renewable energies. However, the frequent wide-range load regulation may deteriorate the operational efficiency of the power plant. To this end, a hierarchical control structure with two layers is proposed in this paper. An economic model predictive controller using a locally linearized model of the plant (LEMPC) is employed in the upper layer to realize an optimal load tracking. A L1 adaptive controller in the lower layer forces the plant to track the optimal trajectory by estimating and compensating the lumped uncertainty between the real plant and the linear model. The tracking performance is theoretically proved to reach the desired transient process. The proposed hierarchical control architecture is validated through simulations on a simplified 1000MW super-critical boiler-turbine unit model with comparison to the other two conventional real-time optimization control approaches. The results show that the proposed L1-LEMPC control system produces better load tracking performance than the other two conventional control strategies with higher operational economy efficiency. Moreover, under the environment of severe external disturbance and parameter perturbation, the proposed control system still maintains satisfactory performance.

Advanced Autonomous Underwater Vehicles Attitude Control with L 1 Backstepping Adaptive Control Strategy.

This paper presents a novel attitude control design, which combines L 1 adaptive control and backstepping control together, for Autonomous Underwater Vehicles (AUVs) in a highly dynamic and uncertain environment. The Euler angle representation is adopted in this paper to represent the attitude propagation. Kinematics and dynamics of the attitude are in the strict feedback form, which leads the backstepping control strategy serving as the baseline controller. Moreover, by bringing fast and robust adaptation into the backstepping control architecture, our controller is capable of dealing with time-varying uncertainties from modeling and external disturbances in dynamics. This attitude controller is proposed for coupled pitch-yaw channels. For inevitable roll excursions, a Lyapunov function-based optimum linearization method is presented to analyze the stability of the roll angle in the operation region. Theoretical analysis and simulation results are given to demonstrate the feasibility of the developed control strategy.

A Fiber Optic Ultrasonic Sensing System for High Temperature Monitoring Using Optically Generated Ultrasonic Waves.

This paper presents the design, fabrication, and characterization of a novel fiber optic ultrasonic sensing system based on the photoacoustic (PA) ultrasound generation principle and Fabry-Perot interferometer principle for high temperature monitoring applications. The velocity of a sound wave traveling in a medium is proportional to the medium's temperature. The fiber optic ultrasonic sensing system was applied to measure the change of the velocity of sound. A fiber optic ultrasonic generator and a Fabry-Perot fiber sensor were used as the signal generator and receiver, respectively. A carbon black-polydimethylsiloxane (PDMS) material was utilized as the photoacoustic material for the fiber optic ultrasonic generator. Two tests were performed. The system verification test proves the ultrasound sensing capability. The high temperature test validates the high temperature measurement capability. The sensing system survived 700 °C. It successfully detects the ultrasonic signal and got the temperature measurements. The test results agreed with the reference sensor data. Two potential industry applications of fiber optic ultrasonic sensing system are, it could serve as an acoustic pyrometer for temperature field monitoring in an industrial combustion facility, and it could be used for exhaust gas temperature monitoring for a turbine engine.

Adaptive dynamic inversion via time-scale separation.

This paper presents a full state feedback adaptive dynamic inversion method for uncertain systems that depend nonlinearly upon the control input. Using a specialized set of basis functions that respect the monotonic property of the system nonlinearities with respect to control input, a state predictor is defined for derivation of the adaptive laws. The adaptive dynamic inversion controller is defined as a solution of a fast dynamical equation, which achieves time-scale separation between the state predictor and the controller dynamics. Lyapunov-based adaptive laws ensure that the predictor tracks the state of the nonlinear system with bounded errors. As a result, the system state tracks the desired reference model with bounded errors. Benefits of the proposed design method are demonstrated using Van der Pol dynamics with nonlinear control input.

Novel L1 neural network adaptive control architecture with guaranteed transient performance.

In this paper, we present a novel neural network (NN) adaptive control architecture with guaranteed transient performance. With this new architecture, both input and output signals of an uncertain nonlinear system follow a desired linear system during the transient phase, in addition to stable tracking. This new architecture uses a low-pass filter in the feedback loop, which consequently enables to enforce the desired transient performance by increasing the adaptation gain. For the guaranteed transient performance of both input and output signals of the uncertain nonlinear system, the L1 gain of a cascaded system, comprised of the low-pass filter and the closed-loop desired reference model, is required to be less than the inverse of the Lipschitz constant of the unknown nonlinearities in the system. The tools from this paper can be used to develop a theoretically justified verification and validation framework for NN adaptive controllers. Simulation results illustrate the theoretical findings.