Relevant bibliographies by topics / Gaussian linear control systems with feedback

Academic literature on the topic 'Gaussian linear control systems with feedback'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Gaussian linear control systems with feedback"

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Chang, R. J. "Optimal Linear Feedback Control for a Class of Nonlinear Nonquadratic Non-Gaussian Problems." Journal of Dynamic Systems, Measurement, and Control 113, no. 4 (December 1, 1991): 568–74. http://dx.doi.org/10.1115/1.2896459.

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An optimal linear feedback controller designed for a class of nonlinear stochastic systems with nonquadratic performance criteria by a non-Gaussian approach is presented. The non-Gaussian method is developed through expressing the unknown stationary output density function as a weighted sum of the Gaussian densities with undetermined parameters. With the aid of a Gaussian-sum density, the optimal feedback gain for a control system with complete state information is derived. By assuming that the separation principle is valid for the class of stochastic systems, a nonlinear precomputed-gain filter is then implemented. The method is illustrated by a Duffing-type control system and the performance of a linear feedback controller designed through both quadratic and nonquadratic performance indices is compared.
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Cacace, Filippo, Francesco Conte, Massimiliano d’Angelo, and Alfredo Germani. "Feedback polynomial filtering and control of non-Gaussian linear time-varying systems." Systems & Control Letters 123 (January 2019): 108–15. http://dx.doi.org/10.1016/j.sysconle.2018.11.004.

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Liu, Qing-Quan, and Fang Jin. "LQG Control of Networked Control Systems with Limited Information." Mathematical Problems in Engineering 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/206391.

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This paper addresses linear quadratic Gaussian (LQG) control problems for multi-input multioutput (MIMO), linear time-invariant (LTI) systems, where the sensors and controllers are geographically separated and connected via a digital communication channel with limited data rates. An observer-based, quantized state feedback control scheme is employed in order to achieve the minimum data rate for mean square stabilization of the unstable plant. An explicit expression is presented to state the tradeoff between the LQ cost and the data rate. Sufficient conditions on the data rate for mean square stabilization are derived. An illustrative example is given to demonstrate the effectiveness of the proposed scheme.
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Liu, Qing Quan. "Observer-Based Quantized Feedback Control via Noisy Communication Channels." Advanced Materials Research 433-440 (January 2012): 6242–49. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6242.

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This paper investigates the input and output quantized control problem for stochastic linear systems with unbounded and possibly non-Gaussian process disturbance, where sensors, controllers and plants are connected by a noisy digital communication channel. Due to the unbounded process disturbance, a dynamic, logarithmic quantization scheme is proposed. An observer-based control policy is presented to stabilize the unstable plant in the mean square sense. Simulation results show the validity of the proposed quantization and control policy.
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Scruggs, Jeff T., Ian L. Cassidy, and Sam Behrens. "Multi-objective optimal control of vibratory energy harvesting systems." Journal of Intelligent Material Systems and Structures 23, no. 18 (May 6, 2012): 2077–93. http://dx.doi.org/10.1177/1045389x12443015.

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This article examines the use of actively controlled electronics to maximize the energy harvested from a stationary stochastic disturbance. In prior work by the authors, it has been shown that when the harvester dynamics are linear and the transmission losses in the electronics are resistive, the optimal feedback controller is the solution to a nonstandard linear-quadratic-Gaussian optimal control problem. This article augments the theory in the following three distinct ways: (a) It illustrates how to use linear matrix inequalities to balance the objective of energy harvesting against other response control objectives (such as minimum requirements on closed-loop damping and maximum levels of voltage response), in the synthesis of the optimal feedback law; (b) it establishes a more realistic characterization of the transmission losses in the actively controlled power electronics used to regulate the extraction of power; and (c) it illustrates how the optimal control theory for resistive loss models can be extended to accommodate the more realistic loss models. The theory is illustrated in the context of a piezoelectric energy harvesting model, and an example is used to illustrate that the theory can be used to simultaneously optimize the feedback law, together with the switching frequency and storage bus voltage of the power electronics.
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Maity, Dipankar, and John S. Baras. "Minimal Feedback Optimal Control of Linear-Quadratic-Gaussian Systems: No Communication is also a Communication." IFAC-PapersOnLine 53, no. 2 (2020): 2201–7. http://dx.doi.org/10.1016/j.ifacol.2020.12.004.

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Ahmad, S. M., A. J. Chipperfield, and M. O. Tokhi. "Dynamic modelling and linear quadratic Gaussian control of a twin-rotor multi-input multi-output system." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 217, no. 3 (May 1, 2003): 203–27. http://dx.doi.org/10.1177/095965180321700304.

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This paper presents an investigation into the modelling and control of a one-degree-of-freedom (1 DOF) twin-rotor multi-input multi-output (MIMO) system (TRMS). The behaviour of the TRMS in certain aspects resembles that of a helicopter. Hence, it is an interesting identification and control problem. A dynamic model characterizing the TRMS in hover is extracted using a black-box system identification technique. The extracted model is employed in the design of a feedback linear quadratic Gaussian compensator, namely the stability augmentation system (SAS). This has a good tracking capability but requires high control effort and has inadequate authority over residual vibration of the system. These problems are resolved by further augmenting the system with a command path prefilter, resulting in the command and stability augmentation system (CSAS). The combined feedforward and feedback compensator satisfies the performance objectives and obeys the actuator constraint. The control law is implemented in realtime on the TRMS platform.
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Bai, Mingsian R., and Weibin Luo. "DSP Implementation of an Active Bearing Mount for Rotors Using Hybrid Control." Journal of Vibration and Acoustics 122, no. 4 (April 1, 2000): 420–28. http://dx.doi.org/10.1115/1.1287788.

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An on-line active technique for suppressing rotor vibration is proposed. Electromagnetic actuators are mounted on the housing of a ball bearing for generating counter forces to cancel the transverse vibrations due to imbalance, misalignment, and so forth. Controllers based on feedback structure, feedforward structure and hybrid structure are investigated. The multiple channel active control systems are implemented on the platform of a digital signal processor. Numerical simulation and experimental investigations indicate that the proposed methods are effective in suppressing the periodic disturbances. In particular, the hybrid control by using feedback linear quadratic gaussian control and feedforward least mean square algorithm with synthetic reference achieves the best performance in terms of vibration attenuation and convergence speed. [S0739-3717(00)00904-1]
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Derpich, Milan S., Matias Müller, and Jan Østergaard. "The Entropy Gain of Linear Systems and Some of Its Implications." Entropy 23, no. 8 (July 24, 2021): 947. http://dx.doi.org/10.3390/e23080947.

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We study the increase in per-sample differential entropy rate of random sequences and processes after being passed through a non minimum-phase (NMP) discrete-time, linear time-invariant (LTI) filter G. For LTI discrete-time filters and random processes, it has long been established by Theorem 14 in Shannon’s seminal paper that this entropy gain, (G), equals the integral of log|G|. In this note, we first show that Shannon’s Theorem 14 does not hold in general. Then, we prove that, when comparing the input differential entropy to that of the entire (longer) output of G, the entropy gain equals (G). We show that the entropy gain between equal-length input and output sequences is upper bounded by (G) and arises if and only if there exists an output additive disturbance with finite differential entropy (no matter how small) or a random initial state. Unlike what happens with linear maps, the entropy gain in this case depends on the distribution of all the signals involved. We illustrate some of the consequences of these results by presenting their implications in three different problems. Specifically: conditions for equality in an information inequality of importance in networked control problems; extending to a much broader class of sources the existing results on the rate-distortion function for non-stationary Gaussian sources, and an observation on the capacity of auto-regressive Gaussian channels with feedback.
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Nelson, R., B. Protas, and T. Sakajo. "Linear feedback stabilization of point-vortex equilibria near a Kasper wing." Journal of Fluid Mechanics 827 (August 18, 2017): 121–54. http://dx.doi.org/10.1017/jfm.2017.484.

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This paper concerns feedback stabilization of point-vortex equilibria above an inclined thin plate and a three-plate configuration known as the Kasper wing in the presence of an oncoming uniform flow. The flow is assumed to be potential and is modelled by the two-dimensional incompressible Euler equations. Actuation has the form of blowing and suction localized on the main plate and is represented in terms of a sink–source singularity, whereas measurement of pressure across the plate serves as system output. We focus on point-vortex equilibria forming a one-parameter family with locus approaching the trailing edge of the main plate and show that these equilibria are either unstable or neutrally stable. Using methods of linear control theory we find that the system dynamics linearized around these equilibria is both controllable and observable for almost all actuator and sensor locations. The design of the feedback control is based on the linear–quadratic–Gaussian (LQG) compensator. Computational results demonstrate the effectiveness of this control and the key finding of this study is that Kasper wing configurations are in general not only more controllable than their single-plate counterparts, but also exhibit larger basins of attraction under LQG feedback control. The feedback control is then applied to systems with additional perturbations added to the flow in the form of random fluctuations of the angle of attack and a vorticity shedding mechanism. Another important observation is that, in the presence of these additional perturbations, the control remains robust, provided the system does not deviate too far from its original state. Furthermore, except in a few isolated cases, introducing a vorticity-shedding mechanism enhanced the effectiveness of the control. Physical interpretation is provided for the results of the controllability and observability analysis as well as the response of the feedback control to different perturbations.
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Dissertations / Theses on the topic "Gaussian linear control systems with feedback"

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Hadad, Zarif M. "Structural properties of linear systems." Thesis, City University London, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.332557.

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Crews, Mark Conrad. "Robust multivariable feedback design for uncertain linear systems." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.305464.

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Shan, Xu Yi. "Bounded feedback and structural issues in linear multivariable systems." Thesis, City University London, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316131.

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Trimboli, Michael Scott. "Generalized Nyquist design for uncertain linear feedback systems." Thesis, University of Oxford, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.256373.

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Kafai, Ali. "Aspects of feedback and a local approach for linear systems." Thesis, Loughborough University, 1991. https://dspace.lboro.ac.uk/2134/27824.

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The effect of the implementation of constant output feedback on a general rational transfer function matrix has long been of interest. More recently, interest has been shown in the properness of closed-loop systems when such constant output feedback is applied to a general open-loop G(s) which is given either in terms of a state space realisation or as a matrix fraction description. In the first part of this work the effect of constant output feedback on a general composite system is considered and a simple sufficient condition for properness of such a system is derived.
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Hendeby, Gustaf. "Fundamental Estimation and Detection Limits in Linear Non-Gaussian Systems." Licentiate thesis, Linköping : Linköpings universitet, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4886.

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Irving, J. P. "Robust pole assignment via state feedback and its relationship to linear optimal control and output feedback pole assignment." Thesis, University of Salford, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252935.

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Syrmos, Vassilis L. "Feedback design techniques in linear system theory : geometric and algebraic approaches." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/13348.

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Yang, Bong-Jun. "Adaptive Output Feedback Control of Flexible Systems." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/5248.

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Neural network-based adaptive output feedback approaches that augment a linear control design are described in this thesis, and emphasis is placed on their real-time implementation with flexible systems. Two different control architectures that are robust to parametric uncertainties and unmodelled dynamics are presented. The unmodelled effects can consist of minimum phase internal dynamics of the system together with external disturbance process. Within this context, adaptive compensation for external disturbances is addressed. In the first approach, internal model-following control, adaptive elements are designed using feedback inversion. The effect of an actuator limit is treated using control hedging, and the effect of other actuation nonlinearities, such as dead zone and backlash, is mitigated by a disturbance observer-based control design. The effectiveness of the approach is illustrated through simulation and experimental testing with a three-disk torsional system, which is subjected to control voltage limit and stiction. While the internal model-following control is limited to minimum phase systems, the second approach, external model-following control, does not involve feedback linearization and can be applied to non-minimum phase systems. The unstable zero dynamics are assumed to have been modelled in the design of the existing linear controller. The laboratory tests for this method include a three-disk torsional pendulum, an inverted pendulum, and a flexible-base robot manipulator. The external model-following control architecture is further extended in three ways. The first extension is an approach for control of multivariable nonlinear systems. The second extension is a decentralized adaptive control approach for large-scale interconnected systems. The third extension is to make use of an adaptive observer to augment a linear observer-based controller. In this extension, augmenting terms for the adaptive observer can be used to achieve adaptation in both the observer and the controller. Simulations to illustrate these approaches include an inverted pendulum with its cart serially attached to two carts (one unmodelled), three spring-coupled inverted pendulums, and an inverted pendulum with its initial condition in a range in which a linear controller is destabilizing.
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Allen, Benjamin T. Gravagne Ian A. "Experimental investigation of a time scales linear feedback control theorem." Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5116.

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Books on the topic "Gaussian linear control systems with feedback"

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Garg, Sanjay. Turbofan engine control system design using the LQG/LTR methodology. [Washington, D.C.]: National Aeronautics and Space Administration, 1989.

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Dorato, Peter. Linear-quadratic control: An introduction. Englewood Cliffs, N.J: Prentice Hall, 1995.

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Dorato, P. Linear-quadratic control: An introduction. Englewood Cliffs, N.J: Prentice Hall, 1995.

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Bongiorno, Joseph J., and Kiheon Park. Design of Linear Multivariable Feedback Control Systems. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44356-6.

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Rizvi, Syed Ali Asad, and Zongli Lin. Output Feedback Reinforcement Learning Control for Linear Systems. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15858-2.

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L, Vincent Thomas, ed. Modern control systems analysis and design. New York: J. Wiley, 1993.

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Yaniv, Oded. Quantitative feedback design of linear and nonlinear control systems. Boston: Kluwer Academic, 1999.

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Yaniv, Oded. Quantitative feedback design of linear and nonlinear control systems. Boston: Kluwer Academic, 1999.

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Yaniv, Oded. Quantitative Feedback Design of Linear and Nonlinear Control Systems. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-6331-7.

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Yaniv, Oded. Quantitative Feedback Design of Linear and Nonlinear Control Systems. Boston, MA: Springer US, 1999.

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Book chapters on the topic "Gaussian linear control systems with feedback"

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Sbarbaro, Daniel, and Roderick Murray-Smith. "Self-tuning Control of Non-linear Systems Using Gaussian Process Prior Models." In Switching and Learning in Feedback Systems, 140–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30560-6_6.

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Zhao, Ling, Yuanqing Xia, Hongjiu Yang, and Jinhui Zhang. "Linear Feedback Control." In Pneumatic Servo Systems Analysis, 49–59. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9515-5_4.

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Zhao, Ling, Yuanqing Xia, Hongjiu Yang, and Jinhui Zhang. "Linear Feedback Control." In Pneumatic Servo Systems Analysis, 303–17. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9515-5_22.

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Zhao, Ling, Yuanqing Xia, Hongjiu Yang, and Jinhui Zhang. "Linear Feedback Control." In Pneumatic Servo Systems Analysis, 165–77. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9515-5_13.

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Söderström, T. "Linear Quadratic Gaussian Control." In Discrete-time Stochastic Systems, 319–65. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0101-7_11.

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Antsaklis, Panos J., and A. Astolfi. "Linear State Feedback." In Encyclopedia of Systems and Control, 653–57. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5058-9_196.

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Antsaklis, Panos, and Alessandro Astolfi. "Linear State Feedback." In Encyclopedia of Systems and Control, 1–6. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5102-9_196-1.

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Antsaklis, P. J., and A. Astolfi. "Linear State Feedback." In Encyclopedia of Systems and Control, 1–5. London: Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-5102-9_196-2.

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Antsaklis, Panos J., and Alessandro Astolfi. "Linear State Feedback." In Encyclopedia of Systems and Control, 1130–34. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_196.

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Martin, Clyde F., and Mark Stamp. "Gaussian Quadrature and Linear Systems." In Computation and Control II, 263–77. Boston, MA: Birkhäuser Boston, 1991. http://dx.doi.org/10.1007/978-1-4612-0427-5_18.

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Conference papers on the topic "Gaussian linear control systems with feedback"

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Balci, Isin M., and Efstathios Bakolas. "Covariance Control of Discrete-Time Gaussian Linear Systems Using Affine Disturbance Feedback Control Policies." In 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021. http://dx.doi.org/10.1109/cdc45484.2021.9683236.

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Priess, M. Cody, Jongeun Choi, and Clark Radcliffe. "The Inverse Problem of Continuous-Time Linear Quadratic Gaussian Control With Application to Biological Systems Analysis." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6100.

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In this paper, we demonstrate two methods for solving the inverse problem of continuous-time LQG control. This problem can be defined as: given a known LTI system with feedback controller K and Kalman gain L, can we find the weighting matrices Q, R (for state and input, respectively) and estimated noise intensities W, V (for process and measurement noise, respectively) such that the LQG control synthesis problem using these weights generates K and L? We formulate a regularized version of this problem as a minimization problem subject to a set of Linear Matrix Inequalities (LMIs). If feasible, a unique exact solution to the inverse LQR problem exists. If the LMIs are infeasible, we show a gradient descent algorithm that will find Q, R, W, and V to minimize the error in the recovered gain matrices K and L. We demonstrate these techniques through several numerical examples and formulate a human postural control case study to which we intend to apply our proposed techniques.
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Xi, Handa, and Jing Sun. "Analysis and Feedback Control of Planar SOFC Systems for Fast Load Following in APU Applications." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14771.

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Solid Oxide Fuel Cell (SOFC) based Auxiliary Power Unit (APU) systems have many practical advantages given their high efficiency, low emissions and flexible fueling strategies. This paper focuses on model-based analysis and feedback control design for planar SOFC systems to achieve fast load following capability. A dynamic model is first developed for the integrated co-flow planar SOFC and CPOX (Catalytic Partial Oxidation) system aiming at APU applications. Simulation results illustrate that an open-loop system with optimal steady-state operating setpoints exhibits a slow transient power response when load increases. Feedback control is then explored to speed up the system response by controlling the flow rates of fuel and air supplies to the system. Model linearization, balanced truncation and Linear Quadratic Gaussian (LQG) approaches are used to derive the low-order observer-based controller. With the feedback controller developed, we show, through simulations, that the closed-loop system can have faster load following capability. Different feedback strategies are also considered and their impacts on closed-loop system performance are analyzed.
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Scruggs, Jeffrey T., Alexandros A. Taflanidis, and Wilfred D. Iwan. "Nonlinear Stochastic Controllers for Semiactive and Regenerative Structural Control Systems, With Guaranteed Quadratic Performance Margins." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95625.

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In many applications of vibration control, the circumstances of the application impose constraints on the energy available for the actuation of control forces. Semiactive dampers (i.e., viscous dampers with controllable coefficients) constitute the simplest example of such actuation in structural control applications. Regenerative Force Actuation (RFA) networks are an extension of semiactive devices, in which mechanical energy is first converted to electrical energy, which is then dissipated in a controllable resistive network. A fairly general class of semiactive and regenerative systems can be characterized by a differential equation which is bilinear (i.e., linear in state, linear in control input, but nonlinear in both). This paper presents a general approach to bilinear feedback control system design for semiactive and regenerative systems, which is analytically guaranteed to out-perform optimal linear viscous damping in stationary stochastic response, under the familiar Quadratic Gaussian performance measure. The design for full-state feedback and for the more practical case of noise-corrupted and incomplete measurements (i.e., output feedback) are separately discussed. Variants of the theory are shown to exist for other quadratic performance measures, including risk-sensitive and multi-objective frameworks. An illustrative application to civil engineering is presented.
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Attia, Tamer, Kevin Kochersberger, John Bird, and Steve C. Southward. "System Identification and Optimal Control of Half-Car Active Suspension System Using a Single Noisy IMU With Position Uncertainty." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5097.

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An active suspension based on Linear Quadratic Gaussian (LQG) optimal controller is an effective system for enhancing the ride comfort and handling characteristics of a vehicle. LQG requires a good plant model for success, and this may be difficult to extract using a single inertial measurement device in the presence of noise. This paper presents a method for estimating the vehicle states by measuring both the vehicle bounce and pitch accelerations using an Inertial Measurement Unit (IMU) with position uncertainty relative to the sprung mass center of gravity. Frequency domain methods are used for System Identification (SysId). The state estimation is based on channel-by-channel model estimation using uncorrelated random excitation which is applied to the front wheels, rear wheels, front actuator, and rear actuator. An anti-aliasing filter eliminates false response harmonics and a Kalman filter is used to estimate the current states of the actual plant and the LQR block for the full-states-feedback controller. The controllers and observer are implemented in simulation using a four degree-of-freedom half car linear model.
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Orzechowski, Pawel K., Steve Gibson, and Tsu-Chin Tsao. "Disturbance Rejection by Optimal Feedback Control in a Laser Beam Steering System." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-60253.

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This paper presents an optimal control design and experimental implementation for pointing and disturbance rejection in a laser steam steering system. The linear quadratic Gaussian (LQG) controller, which includes a stochastic disturbance model, as well as integral action, was designed and implemented to compensate for disturbances due to atmospheric turbulence in the optical path and mechanical vibration of the laser and optical components. The control design also considers the situation where the stochastic disturbances applied to the two beam axes are correlated and renders a multi-input-multi-output (2-by-2) output feedback controller. The experimental system consists of a two-axis tilt mirror driven by piezo-electric actuators for controlling the laser beam, a second actuated tilt mirror to generate disturbances, a position sensing device that senses the location of the beam on a target plane, and a real time computer for digital control. System identification is used to determine a state space model of the beam steering system for use in control system design. Experimental results are presented to demonstrate the effectiveness of the LQG optimal disturbance rejection for the prescribed stochastic disturbances.
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Balas, Mark J. "Adaptive Control of Nonminimum Phase Systems Using Sensor Blending With Application to Launch Vehicle Control." In ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/smasis2012-7921.

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The goal of this paper is to investigate the use of a very simple direct adaptive controller in the guidance of a large, flexible launch vehicle. The adaptive controller, requiring no on-line information about the plant other than sensor outputs, would be a more robust candidate controller in the presence of unmodeled plant dynamics than a model-based fixed gain linear controller. NASA’s seven-state FRACTAL academic model for ARES I-X was employed as an example launch vehicle on which to develop the controller. To better understand the difficult dynamic issues, we started with a simplified model that incorporated the inherent instability of the plant and the nonminimum phase nature of the dynamics: an inverted pendulum with an attachable slosh tank. We formulated controllers for this simplified plant with slosh dynamics using control algorithms developed only on a reduced–order model consisting of the rigid body dynamics without slosh. The controllers must be designed to reject three different persistent input disturbances: persistent pulse, step, and sine. We assumed that only position feedback was available, and that rates would have to be estimated. For comparison, a fixed gain linear controller was developed using the well-known Linear Quadratic Gaussian methodology employing state estimation to obtain rate estimates. For a stable adaptive controller, we used direct adaptive control theory developed by Balas, et al. For this theory we need CB > 0 and a minimum-phase open-loop transfer function. We employed a new transmission zero selection method to develop a blended output shaping matrix which would satisfy these conditions robustly. We used approximate differentiation filters to obtain rates for the adaptive controller. Again for comparison, we redesigned the LQG controller to use the same blended output matrix and filters. Following the work on the pendulum, the same method was applied to develop an adaptive controller for the FRACTAL launch vehicle model. An adaptive controller stabilizes a rigid body version of FRACTAL over a very long timeline while exceeding all reasonable state and output limits.
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Zhuang, Ye, Haojie Sun, Yingchun Qi, Weiguang Fan Fan, and Hui Ye. "Semi-Active Reinforcement Learning Suspension Control for the Off-Road Vehicles." In 11th Asia-Pacific Regional Conference of the ISTVS. International Society for Terrain-Vehicle Systems, 2022. http://dx.doi.org/10.56884/bcor8152.

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Vertical vibration of the terrain vehicle involves its comfort, maneuverability, and fatigue life of the key components. The type of road surface encountered by the vehicle is very complex and difficult to measure. Besides, the damper system has a strong non-linearity, which makes the design of the vehicle suspension controller difficult. Compared with the active suspension, the semiactive suspension has the advantages of low energy consumption and high safety, therefore this paper uses semi-active magneto-rheological damper and reinforcement learning technology, from the comfort point of view, to solve the suspension random optimal control problem. It is expected to improve the comfort of the terrain-vehicle with intelligent, low-power control method. This paper applies Gaussian Process (GP) technology to learn and model the nonlinear part of the terrain vehicle system, and then carries out the design of the reinforcement learning control strategy, establishes a linear control law with the suspension deflection, the sprung mass velocity, and the unsprung mass velocity as the feedback variables. The introduced reinforcement learning algorithm learns the appropriate feedback gain during the interaction process with the system, and then uses the learned feedback gain to carry out system simulation and experimental analysis. The paper compares the reinforcement learning algorithm with other classical semi-active control algorithms under the input of random, bump and sinusoidal pavement, and the analysis results show that the reinforcement learning algorithm introduced in the paper has excellent control effect in the full frequency band and can provide good comfort for terrain vehicles under the condition of low power consumption.
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9

Asokanthan, Samuel F., Ye Tian, and Tianfu Wang. "Active Roll Control of Heavy Single Unit Vehicles Employing MEMS Angular Rate Sensors." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35797.

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The present paper is concerned with the use of active roll control to improve the roll stability of heavy road-vehicles and the application of Micro-electro-mechanical System (MEMS) angular rate sensors in the feedback monitoring. For this purpose, mathematical models that represent the roll/yaw dynamics for a torsionally rigid Single Unit Vehicle (SUV) is presented. The state-space models that represent the vehicle dynamics are also developed for the purpose of performing numerical simulations. A linear Quadratic Gaussian (LQG) based controller, using Kalman estimator to estimate certain states, is employed to design a full-state active roll control system. A mathematical model that represents the dynamic behavior of a low-cost MEMS gyroscope is derived for the purpose of investigating the suitability of applying this class of angular rate sensor in the roll control of heavy vehicles. Some reliability issues related to MEMS sensors, such as noise and drift, are introduced and included in vehicle dynamic models.
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Borrás Pinilla, Carlos, and Alan Javier González Diaz. "On the Dynamics and Optimal Control of the Rotational Inverted Pendulum." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-69171.

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Abstract The rotational inverted pendulum (Furuta pendulum) was designed and built in the laboratory of the school of mechanical engineering of the Universidad Industrial de Santander as a learning challenge of implementing control strategies for graduate students. In this research work, it’s developed a full dynamics model using Lagrangian formulation. The math dynamic model includes real physic parameters, the mass of each component, inertia terms, dimensions, viscous frictions, and DC high torque motor dynamics. With a linearized state-space model of the system dynamics, the PID approach and the optimal control gain for the linear quadratic regulator (LQR) controller and the linear quadratic Gaussian (LQG) regulator were computed and the dynamic and disturbance response were evaluated and compared for each control strategies to determine the best performance: a lower percentage of Peak Overshoot (PO). Steady-State Error (Ess), lower settling time (Settling Time (Ts)) and control (u) cost. PID controller presented the lowest performance in the control response to disturbances, unlike LQR, this does not take into account the cost-performance ratio, the controllers LQG and LQR modeled in the state space produce a feedback gain matrix which allows the Furuta pendulum to have regulated behavior making the pendulum quickly find the equilibrium point.
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