Theoretical modeling and vibration control for pre-twisted composite blade based on LLI controller

Theoretical modeling and vibration control for divergent motion of thin-walled pre-twisted wind turbine blade have been investigated based on “linear quadratic Gaussian (LQG) controller using loop transfer recovery (LTR) at plant input” (LLI). The blade section is a single-celled composite structure with symmetric layup configuration of circumferentially uniform stiffness (CUS), exhibiting displacements of vertical/lateral bending coupling. Flutter suppression for divergent instability is investigated, with blade driven by nonlinear aerodynamic forces. Theoretical modeling of CUS-based structure is implemented based on Hamilton variational principle of elasticity theory. The discretization of aeroelastic equations is solved by Galerkin method, with blade tip responses demonstrated. The LLI controller is characterized by LTR at the plant input. The effects of LLI controller are achieved and illustrated by displacement responses, controller responses and frequency spectrum analysis, respectively.

Design of a Robust Controller by LQG/LTR Formalism for Francis hydro Turbine Driving a Synchronous Generator

This paper presents the design and application of a robust controller by Linear-Quadratic-Gaussian method with Loop-Transfer-Recovery (LQG \LTR) at the same time to carefully attain performance and robustness objectives. To improve Stability, the robust controller has been shown to provide good performance i n normal operations conditions. Objectives cannot be suitable unless the controller can perpetuate such quality in the presence of plant uncertainties or any working conditions in the hydroelectric power plants. The approach is based to synthesizing a robust controller minimizing a quadratic criterion (controller LQG) while using the Loop Transfer Recovery (LTR), to restore robustness properties of the Estimator. In this study, we applied this robust control law on the model of a Francis hydro turbine. Computer simulations are carried out to establish a n d compare the performance and robustness of using the Infinite horizon control ( H ), internal model control (IMC), Proportional Integral Derived (PID) and LQG/LTR controllers.

Feedforward Feedback Linearization Linear Quadratic Gaussian With Loop Transfer Recovery Control of Piezoelectric Actuator in Active Vibration Isolation System

Hysteresis exists widely in intelligent materials, such as piezoelectric and giant magnetostrictive ones, and it significantly affects the precision of vibration control when a controlled object moves at a range of micrometers or even smaller. Many measures must be implemented to eliminate the influence of hysteresis. In this work, the hysteresis characteristic of a proposed piezoelectric actuator (PEA) is tested and modeled based on the adaptive neuro fuzzy inference system (ANFIS). A linearization control method with feedforward hysteresis compensation and proportional–integral–derivative (PID) feedback is established and simulated. A linear quadratic Gaussian with loop transfer recovery (LQG/LTR) regulator is then designed as a vibration controller. Verification experiments are conducted to evaluate the effectiveness of the control method in vibration isolation. Experiment results demonstrate that the proposed vibration control system with a feedforward feedback linearization controller and an LQG/LTR regulator can significantly improve the performance of a vibration isolation system in the frequency range of 5–200 Hz with low energy consumption.

Artificial bee colony optimised controller for small-scale unmanned helicopter

ABSTRACTIn this paper, an efficient approach to design and optimize a flight controller of a small-scale unmanned helicopter is proposed. Given the identified helicopter model, the Linear Quadratic Gaussian/Loop Transfer Recovery (LQG/LTR) robust control method is applied for trajectory tracking and attitude control of the helicopter with a two-loop hierarchical control architecture. Since the performance of the controller extremely depends on its weighting matrices, the Artificial Bee Colony (ABC) algorithm is introduced to automatically select the parameters of the matrices. Comparative studies between optimal algorithms are also carried out. A series of flight experiments and simulations are conducted to investigate the effectiveness and robustness of the proposed optimised controller.