Linear Quadratic Gaussian With Loop Transfer Recovery Research Articles

LQG/LTR based robust youla parameterized adaptive vibration control for the supporting platform of rotating liquid mirror

In mechatronic systems, the vibrations which commonly consist of band-limited random signals mixed with large multiple narrow-band deterministic signals may negatively impact the system performance. For example, due to the incomplete matching of the mechanism and the rotation of the motor, random and deterministic vibration disturbance may occur in the supporting platform of the rotating liquid mirror. In this paper, a robust Youla ( Q) parameterized adaptive regulation approach has been proposed to minimize such kind of vibration signals for the rotating platform system with model uncertainties. Firstly, the inner-loop robust controller with linear quadratic Gaussian with loop transfer recovery ( LQG/LTR) is optimally designed by choosing the suitable weighting functions to achieve the trade-off between robust stability and control performance to deal with random vibration signals. Then the Youla parameter is augmented to construct a set of Q-parameterized stabilizing controllers, and the robust stability of the system is analyzed through dual-Youla parameterization of the uncertain model. The recursive least squares (RLS) adaptive algorithm is developed to tune the Q parameter online to construct a desired adaptive controller for further residual vibration elimination. An experimental evaluation of the controller in reducing the vibration of the supporting platform of a rotating liquid mirror has been carried out, and the results illustrate that the proposed adaptive robust vibration regulation approach can effectively suppress the band-limited random and narrow-band deterministic vibration signals.

LQG/LTR Controller Design for Power Control of Small Pressurized Water Reactors Under Four Feedback-Controlled Strategies

To improve the economy and safety of small pressurized water reactors (SPWRs) with flexible operating characteristics, the reactor power control system should process excellent robustness to provide satisfactory control performances at different operating conditions. This paper proposes four control strategies for reactor power control of SPWRs based on the linear quadratic Gaussian with loop transfer recovery (LQG/LTR) robust control method, including the single-loop reactor power feedback control (RPFC), single-loop average temperature feedback control, dual-loop feedback control, and modified dual-loop feedback control (MDFC) strategies. The corresponding LQG/LTR controllers in the reactor power control system of a SPWR were designed to assess the performance of the four control strategies. The simulation results show that the LQG/LTR controller with the MDFC strategy can provide good control performances for both reactor power and average coolant temperature among the four control strategies while the controller-based single-loop feedback control shows poor control of the reactor power or average coolant temperature. Meanwhile, compared with the existing conventional reactor power control system, the designed robust control system employing the MDFC strategy can provide better control performance for the reactor power and average coolant temperature in full-power operation of 100% to 90% rated power and low-power operation of 25% to 35% rated power with the differential control rod worth taken as 4 pcm/step and 24 pcm/step, indicating its effectiveness and superiority.

LQG/LTR Controller Design Based on Improved SFACC for the PWR Reactor Power Control System

The task of this investigation is to design a controller that has a strong robustness in various operating conditions. A new structure of state feedback assisted classical control (SFACC) that uses a differential lag compensator in the inner classical control loop is proposed to improve the robustness of original SFACC. The linear quadratic Gaussian with loop transfer recovery (LQG/LTR) at the plant output is employed to design the robust controller in the outer control loop. A comparison of the performance and robustness between the gain-insensitive controller and an existing LQG/LTR controller is made by nonlinear simulations. The proposed gain-insensitive LQG/LTR controller can give satisfying performance for both reactor power and coolant temperature over a wide range of reactor operations.

The LQG/LTR control method for turboshaft engine with variable rotor speed based on torsional vibration suppression

In order to realize the rapid response control for turboshaft engine during the process of variable rotor speed, the linear quadratic Gaussian with loop transfer recovery (LQG/LTR) control method for turboshaft engine based on torsional vibration suppression is proposed. Firstly, the two-speed dual clutch transmission model is applied to realize the variable rotor speed of helicopter. Then, based on the state variable model of turboshaft engine, the proper LQG/LTR controller is available. In order to eliminate the limitation of low-order torsional vibration on the bandwidth of LQG/LTR controller, a frequency-domain analysis method for the effect of torsional vibration suppression on LQG/LTR controller performance is developed. Finally, the numerical simulation is conducted to verify the LQG/LTR control for turboshaft engine with variable rotor speed based on torsional vibration suppression. The results show that the bandwidth of the LQG/LTR control loop can increase by 2–3 times under torsional vibration suppression. Meanwhile, when the rotor speed varies continuously by 40%, the overshoot and sag of the power turbine speed can decrease to less than 2% through LQG/LTR controller based on torsional vibration suppression, which achieves the rapid response control of the turboshaft engine.

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.

Modeling and LQG/LTR control for power and axial power difference of load-follow PWR core

The task of this investigation is to design a nonlinear Pressurized Water Reactor (PWR) core load following control system for regulating the core power level and axial power difference. A two-point based nonlinear PWR core model without boron and with the power regulating rod and Axial Offset (AO) rod is built. The linearized single-input single-output (SISO) or multiple-input multiple-output (MIMO) core model under Case 1 or Case 2 classified by two movable regions of power regulating rod is constructed. The linear SISO or MIMO model is further augmented with additional integrators to cater to the control design. The Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) robust optimal control is used to contrive a controller of the linear core model of each case. For Case 1 (Case 2), the nonlinear core model and the LQG/LTR SISO (MIMO) controller construct the nonlinear PWR core load following control system. Two stability theorems are deduced to define that the nonlinear core load following control system of each case is asymptotically stable. Finally, the control system of each case is simulated and the simulation results show that the control system is effective.

Flexibility control and simulation with multi-model and LQG/LTR design for PWR core load following operation

The objective of this investigation is to design a nonlinear Pressurized Water Reactor (PWR) core load following control system. On the basis of modeling a nonlinear PWR core, linearized models of the core at five power levels are chosen as local models of the core to substitute the nonlinear core model in the global range of power level. The Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) robust optimal control is used to contrive a controller with the robustness of a core local model as a local controller of the nonlinear core. Meanwhile, LTR principles are analyzed and proved theoretically by adopting the matrix inversion lemma. Based on the local controllers, the principle of flexibility control is presented to design a flexibility controller of the nonlinear core at a random power level. A nonlinear core model and a flexibility controller at a random power level compose a core load following control subsystem. The combination of core load following control subsystems at all power levels is the core load following control system. Finally, the core load following control system is simulated and the simulation results show that the control system is effective.

Surface-to-air Missile Autopilot Design Using LQG/LTR Gain Scheduling Method

Linear quadratic Gaussian with loop transfer recovery (LQG/LTR) gain scheduling technique is utilized to design gain scheduling autopilot for surface-to-air missile. In order to eliminate the artificial uncertainties that the traditional “trial and error” design process introduces into system, a method to design target loop based on pole assignment is proposed, which provides an explicit algorithm to construct the matrix differential Riccati equation (MDRE) based on the expected poles determined by the performance specifications. Meanwhile, it is proved that by introducing integrators to augment plant dynamics the fast modes of LQG/LTR gain scheduling controller can be restrained effectively, which alleviates an obstacle for the engineering application of LQG/LTR gain scheduling technique. The proposed method is applied in the design of LQG/LTR gain scheduling autopilot for a surface-to-air missile. The design and simulation results indicate that the fast modes of controller are eliminated obviously, and that the dynamic characteristics of autopilot are stable when flight Mach number and altitude vary.

Improved temperature control of a PWR nuclear reactor using an LQG/LTR based controller

State feedback assisted classical (SFAC) control has been developed to improve the temperature response performance of nuclear reactors via modifying the embedded classical controller reference signal. This is done by means of an outermost state feedback controller. A linear quadratic Gaussian with loop transfer recovery (LQG/LTR) at the plant output seems a good candidate for the state feedback loop of SFAC structure, but it ends up in a closed loop system with tightly controlled power. To pay more attention to temperature responses, this paper presents the results of using LQG/LTR at the plant input in SFAC structure. We impose a minor change for considering, to some extent, the variation of system poles (and hence its speed) due to linearization of the nonlinear plant in equilibrium conditions other than the design power. The results are compared to an existing LQG controller with LTR at the plant output. Sensitivity of dominant closed loop poles and nonlinear simulations are used for demonstration and comparison.

전력시스템 동요 억제를 위한 TCSC의 강인한 LQG/LTR 제어기 설계절차에 관한 연구

본 논문에서는 전력동요 억제를 위한 TCSC의 강인한 LQG/LTR (Linear Quadratic Gaussian with Loop Transfer Recovery) 제어기 설계에 관한 연구를 하였다. LQG/LTR 제어기를 설계할 때 성능을 극대화하기 위해서는 여러 단계의 파라미터 조정을 하여야 한다. 이에 본 논문에서는 다 변수 LQG/LTR 안정화 제어기 설계를 체계적으로 할 수 있는 방법을 제안하였다. 설계된 제어기를 실제의 비선형 전력시스템에 적용함으로써 전력동요억제 효과를 검증하였다.

AE–Automation and Engineering Technologies: Optimal Header Height Control System for Combine Harvesters

Automatic control of header height has been employed in combine harvesters as a means to reduce harvest losses, operator fatigue and the risks of equipment damage. State-of-the-art combine harvesters usually incorporate on–off controllers to keep the header at the desired height. The fixed control signals and the relatively broad dead-bands required for stabilization of this type of controller impose serious limitations on the performance of such systems. This paper describes an alternative control system aiming to improve the performance of combine harvesters in following the soil profile. The linear quadratic Gaussian with loop transfer recovery (LQG/LTR) method has been used to design a robust and optimal header height controller. The results obtained indicate that the use of the LQG/LTR controller can significantly improve the disturbance rejection capacity of the system, while keeping its energy expenditure levels unchanged with respect to the conventional on–off system.

Countermeasures for self-excited torsional oscillations using reduced order robust control approach

Torsional oscillations can be destructive to power systems. Understanding the factors leading to these oscillations and finding appropriate countermeasures has been a major concern. In this paper, a robust controller based on the linear quadratic Gaussian with loop transfer recovery (LQG/LTR) approach is designed to damp these undesired oscillations. The controller uses only one measurable feedback signal (generator speed deviation). A reduced-order version of this controller is also obtained. The robust control results are compared to the "idealistic" full state optimal control. Simulation results revealed that the technique damps all torsional oscillatory modes in a very short time, yet maintains reasonable control actions.

LQG/LTR-Based Robust Control of Composite Beams with Piezoelectric Devices

The performance of an LQG/LTR-based multi-input multi-output robust vibration control system for a laminated composite beam is investigated. A finite element model based on a higher order shear defor mation theory and accounting for piezoelectric effects is developed. The model is thus applicable to both thick and thin laminated composite beams. The lateral strain is also incorporated in the model by a systematic re duction of two-dimensional plate constitutive equations. The mode superposition method is used to transform the coupled finite element equations of motion in the physical coordinates into a set of reduced uncoupled equations in the modal coordinates. The state space model of the system is obtained in the reduced-order modal coordinates. An LQG/LTR-based robust controller is designed using the reduced-order state space model of the structure. The performance of the controller is verified for various arbitrary initial conditions of the beam. The effect of structural parameter variation on the closed-loop system performance is also investi gated. The performance robustness of the linear quadratic Gaussian with loop transfer recovery (LQG/LTR) controller is compared with that of the proportional feedback controller.-

An intelligent supervisory system for drum type boilers during severe disturbances

Severe disturbances cause a number of problems in the operation of power plants. The plant subsystem that often suffers the most is the boiler. Alleviating the severity of the problem requires improving the boiler's capability in dealing with disturbances. One of the more severe upsets is partial or full load rejections. Withstanding load rejections depends almost exclusively on the boiler control. This paper presents a fuzzy supervisory scheme to improve the performance of the boiler response during such disturbances. This scheme consists of a robust local controller designed using the Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR). The set-points to the controller are modified whenever necessary by a supervisor partially based on fuzzy logic.

Dynamic Modeling and Neural Control of Composite Shells Using Piezoelectric Devices

A modal dynamic model was developed for the active vibration control of laminated doubly curved shells with piezoelectric sensors and actuators. The dynamic effects of the mass and stiffness of the piezoelectric patches were considered in the model. Finite element equations of motion were developed based on shear deformation theory and implemented for an isoparametric shell element. The mode superposition method was used to transform the coupled finite element equations into a set of uncoupled equations in the modal coordinates. A robust controller was developed using Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) design methodology to calculate the gain and actuator voltage requirements. A Neural Network controller was then designed and trained offline to emulate the performance of the LQG/LTR controller. Numerical results have been presented for a flat plate and a spherical shell showing the variation in initial conditions and structural parameters. The neural network controller was shown to effectively emulate the LQG/LTR controller.

Robust control of smart structures using neural network hardware

In this paper, the use of Intel's Electronically Trainable Analog Neural Network (ETANN) chipi80170NX for implementing single-chip robust controllers for smart structures is successfully demonstrated. Robust controllers like the linear quadratic regulator (LQR) and linear quadratic Gaussian with loop transfer recovery (LQG/LTR) are implemented in various configurations using the ETANN chip on the smart structure test article. The test article is a cantilevered plate with PZTs as actuators and shaded PVDF film as sensors. The spatially distributed sensors allow direct measurement of the states of the system enabling the implementation of the LQR controller. A two step connectionist approach is used to design and implement the neural network based controllers. First a robust controller is designed using conventional design techniques to meet the required closed loop performance. The controller dynamics are copied into the ETANN chip and the trained chip is used to control the test structure. A custom interface board and external signal conditioning circuits are developed to interface the neural network chip with the sensors and actuators on the smart structure test article. The steps involved in training and implementing the robust controllers on a smart structure are detailed. Some of the practical considerations of implementing the controller using the ETANN chip are pointed out and some suggestions made to deal with the limitations. Simulation and experimental results of the closed loop system with all the controller implementation models are presented.

Robust vibration control of composite beams using piezoelectric devices and neural networks

Robust vibration control of smart composite beams using neural networks was studied. Linear quadratic Gaussian with loop transfer recovery (LQG/LTR) methodology was used to design a robust controller on the basis of the state space model of the system. The state space model of the system was obtained using the finite-element method and mode superposition. The finite-element model was based on a higher-order shear deformation theory which included the lateral strains. The mode superposition method was used to transform the coupled finite-element equations of motion in the physical coordinates into a set of reduced uncoupled equations in the modal coordinates. The performance of the LQG/LTR controller was verified for various arbitrary initial conditions. A system of neural networks was then trained to emulate the robust controller. The neural network system was trained using the backpropagation algorithm. After suitable training, the NN (neural network) controller was shown to effectively control the vibrations of the composite beam. A robustness study including the effects of tip mass, structural parameter variation, and loss of a sensor input was performed. The NN controller is shown to provide robustness and control capabilities equivalent to that of the LQG/LTR controller.

Multivariable Neural Network Based Controllers for Smart Structures

This paper details identification and robust control of smart structures using artificial neural networks. To demonstrate the use of artificial neural networks in the control of smart structural systems, two smart structure test articles were fabricated. Active materials like piezoelectric (PZT), polyvinylidene (PVDF) and shape memory alloys (SMA) were used as actuators and sensors. The Eigensystem Realization Algorithm (ERA), a structural identification method has been utilized to determine a minimal order discrete time state space model of the test articles. The ERA requires the Markov parameters of the physical system. A neural network based method has been developed to estimate the Markov parameters of a multi input multi output system from experimental test data. The accelerated adaptive learning rate algorithm and the adaptive activation function were utilized to improve the learning characteristics of the network and reduce the learning time. The identified models were used to design a robust controllers for vibration suppression of smart structures using a modified Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) method. This control design methodology has better loop transfer recovery properties while accommodating the limited control force available from the SMA and the PZT actuators. This controller was copied into a feedforward neural network using the connectionist approach. This neural network controller was implemented using a PC based data acquisition system. The closed loop performance and robustness properties of the conventional and the neural network based controller are compared experimentally.

LQG/LTR control of an AC induction servo drive

A new design method based on the linear-quadratic-Gaussian with loop-transfer-recovery (LQG/LTR) theory has been developed for the design of high performance AC induction servomotor drives using microcomputer-based digital control. The principle of field orientation is employed to achieve the current decoupling control of an induction motor. An equivalent model representing the dynamics of the decoupled induction motor has been developed. Based on the developed model with specified parameter uncertainties and given performance specifications, a frequency domain loop-gain-shaping method based on the LQG/LTR theory is proposed for the design of the servo loop controller. A microcomputer-based induction servomotor drive has been constructed to verify the proposed control scheme. Simulation and experimental results are given to illustrate the effectiveness of the proposed design method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Identification and robust control of smart structures using artificial neural networks

This paper describes an integrated approach to design and implement robust controllers for smart structures. To demonstrate this procedure, we have designed and fabricated a structural test article incorporating shape memory alloy (SMA) actuators, strain gauge sensors, signal-processing circuits and digital controllers with flexible structures. A neural-network-based structural identification method to determine a state space model of the system from its experimental input/output data is presented. To reduce the learning time required to train a neural network significantly, we have developed an accelerated adaptive learning-rate algorithm. The mathematical model derived using neural networks is compared with models obtained by more conventional and well known methods. Using this model, a modified linear quadratic Gaussian with loop transfer recovery (LQG/LTR) controller is designed for vibration suppression purposes. This robust controller accommodates the limited control effort produced by SMA actuators. A multilayered feedforward neural network is then trained to mimic this controller. These designs are all then realized as digital controllers and their closed-loop performances have been compared. In particular, the robustness properties of the controller have been verified for variations in the mass of the test article and the sampling time of the controller.