scholarly journals LQG Control for Dynamic Positioning of Floating Caissons Based on the Kalman Filter

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6496
Author(s):  
Jose Joaquin Sainz ◽  
Elías Revestido Herrero ◽  
Jose Ramon Llata ◽  
Esther Gonzalez-Sarabia ◽  
Francisco J. Velasco ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Control Systems ◽  
Control Strategy ◽  
Monte Carlo Study ◽  
Linear Quadratic ◽  
Marine Structures ◽  
Linear Quadratic Gaussian ◽  
Sea Bed ◽  
Lqg Control ◽  
Precision And Accuracy

This paper presents the application of an linear quadratic gaussian (LQG) control strategy for concrete caisson deployment for marine structures. Currently these maneuvers are carried out manually with the risk that this entails. Control systems for these operations with classical regulators have begun to be implemented. They try to reduce risks, but they still need to be optimized due to the complexity of the dynamics involved during the sinking process and the contact with the sea bed. A linear approximation of the dynamic model of the caisson is obtained and an LQG control strategy is implemented based on the Kalman filter (KF). The results of the proposed LQG control strategy are compared to the ones given by a classic controller. It is noted that the proposed system is positioned with greater precision and accuracy, as shown in the different simulations and in the Monte Carlo study. Furthermore, the control efforts are less than with classical regulators. For all the reasons cited above, it is concluded that there is a clear improvement in performance with the control system proposed.

A Control Strategy for Intelligent Stamp Forming Tooling

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
William J. Emblom ◽  
Klaus J. Weinmann
Keyword(s):  
Fuzzy Logic ◽  
Control Systems ◽  
Control Strategy ◽  
Pid Controller ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Linear Quadratic ◽  
Blank Holder ◽  
Loop Control ◽  
Stamp Forming

This paper describes the development and implementation of closed-loop control for oval stamp forming tooling using MATLAB®’s SIMULINK® and the dSPACE®CONTROLDESK®. A traditional PID controller was used for the blank holder pressure and an advanced controller utilizing fuzzy logic combining a linear quadratic gauss controller and a bang–bang controller was used to control draw bead position. The draw beads were used to control local forces near the draw beads. The blank holder pressures were used to control both wrinkling and local forces during forming. It was shown that a complex, advanced controller could be modeled using MATLAB’s SIMULINK and implemented in DSPACE CONTROLDESK. The resulting control systems for blank holder pressures and draw beads were used to control simultaneously local punch forces and wrinkling during the forming operation thereby resulting in a complex control strategy that could be used to improve the robustness of the stamp forming processes.

LQG Control and Robustness Study for a Prestressed Membrane With Bimorph Actuators

2014 ◽  
Author(s):  
Ipar Ferhat ◽  
Cornel Sultan
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Weak Form ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Nominal System ◽  
Order Form ◽  
Lqg Control ◽  
Bimorph Actuators ◽  
Element Method

Linear Quadratic Gaussian (LQG) control is developed for a prestressed square membrane with bimorph actuators attached to it. The membrane is modeled using the finite element method and the membrane is assumed to be clamped on all edges. After obtaining the mass, damping, stiffness and input matrices in second order form using the weak form Finite Element Method (FEM), the problem is represented in first order form to develop the LQG controller. To study the robustness of the system, the control and observer gain matrices developed for the nominal system are applied to systems obtained from the nominal system by modifying material properties and prestress.

Adaptive linear quadratic gaussian (LQG) control of a bioreactor

2007 ◽  
Vol 53 (2) ◽  
pp. 133-141 ◽  
Author(s):  
G. Roux ◽  
B. Dahhou ◽  
K. Najim ◽  
I. Queinnec
Keyword(s):  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Lqg Control

Motion control of a cruise ship by using active stabilizing fins

2011 ◽  
Vol 225 (4) ◽  
pp. 311-324 ◽  
Author(s):  
J-H Kim ◽  
Y-H Kim
Keyword(s):  
Motion Control ◽  
Control Algorithm ◽  
Roll Motion ◽  
Cruise Ship ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Lqg Control ◽  
Induced Motion ◽  
The Time Domain ◽  
Wave Induced

The present study considers the motion control of a cruise ship by using active stabilizing fins. One or two pairs of stabilizing fins are equipped to reduce the roll and/or pitch motions of the cruise ship. Each fin is controlled by algorithms based on proportional–integral–derivative (PID) and linear quadratic Gaussian (LQG) control. Numerical analysis of the wave-induced motion of a cruise ship with stabilizing fins is carried out by using the time-domain ship motion program which has been developed through this study. The resultant motion response as the performance of each controller is compared between different control algorithms. Based on the present simulation results, the stabilizing fin can be considered a good instrument to reduce pitch motion as well as roll motion of the present cruise ship model. The present results show that the PID control algorithm, a simple but practical algorithm, can be an appropriate method to reduce the roll motion in a moderate sea state, while the LQG control algorithm shows good performance in reducing not only the roll motion but also the coupled roll and pitch motions simultaneously in all of environmental conditions considered.

Review of Linear Stochastic Optimal Control Systems and Applications

1989 ◽  
Vol 111 (4) ◽  
pp. 399-403 ◽  
Author(s):  
H. V. Panossian
Keyword(s):  
Control Systems ◽  
Stochastic Optimal Control ◽  
Equivalence Principle ◽  
Additive Noise ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Basic Difference ◽  
Advantages And Disadvantages ◽  
Gaussian System ◽  
And Behavior

Many complex control systems can be modeled by linear ordinary differential/difference equations with multiplicative and additive noise. The characteristics and behavior of such systems are different from a regular linear quadratic Gaussian system, the basic difference being the inapplicability of the separation or the certainty equivalence principle. Control systems with multiplicative and additive noise are reviewed herein and the fundamental results, in continuous and discrete-time setting, are presented. Furthermore, the advantages and disadvantages are underlined and the need for further research is pointed out.

Risk-sensitive linear/quadratic/gaussian control

1981 ◽  
Vol 13 (4) ◽  
pp. 764-777 ◽  
Author(s):  
P. Whittle
Keyword(s):  
Optimal Control ◽  
Kalman Filter ◽  
Risk Aversion ◽  
Separation Theorem ◽  
Linear Quadratic ◽  
Criterion Function ◽  
Linear Quadratic Gaussian ◽  
State Estimate ◽  
Certainty Equivalence ◽  
Risk Sensitive

The conventional linear/quadratic/Gaussian assumptions are modified in that minimisation of the expectation of cost G defined by (2) is replaced by minimisation of the criterion function (5). The scalar –θ is a measure of risk-aversion. It is shown that modified versions of certainty equivalence and the separation theorem still hold, that optimal control is still linear Markov, and state estimate generated by a version of the Kalman filter. There are also various new features, remarked upon in Sections 5 and 7. The paper generalises earlier work of Jacobson.

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