Uncertainty Weighting Function Research Articles

Attitude dynamic tracking control of an agile satellite using a fixed-structured H∞ synthesis method

It is a challenging control problem for an agile satellite to observe a rapidly moving target with high dynamic tracking precision. This paper presents a robust and high-accuracy attitude control law for an agile satellite with flexible solar arrays. Model uncertainties caused by vibration of flexible appendages are described as an uncertainty weighting function in the framework of robust H∞ control. However, using the traditional H∞ synthesis method, the resulting dynamic output-feedback controller is not suitable for practical implementation due to its complicated structure and high order. To deal with this issue, an H∞ controller with a fixed proportional-integral-derivative (PID) structure is proposed, and the PID control gains are determined by solving a set of linear matrix inequalities with minimizing the H∞ norm of the feedback control system. Moreover, a feedforward compensator is included to further improve the dynamic performance of the system, achieving a faster response with a smaller dynamic tracking error. Simulations are carried out to demonstrate that the proposed control system can achieve high-precision and rapid maneuver tracking performance for abruptly changing attitude command signals in presence of modeling uncertainties.

Research on the Heading Control of Underwater Vehicle Under Hover Condition

A guidance and control algorithm for the heading control of a large underwater vehicle in hover state is proposed. The complexity of the flow field makes it difficult to build an accurate mathematical model for heading control under hover condition. Furthermore, the turning motion in the hover state causes additional vertical hydrodynamic and pitching moments which may affect the control performance of depth and pitching, and even bring safety problems. An $H_\infty $ synthesis method is proposed, in which guidance weighting function is designed for the heading control constraints, sensitivity weighting function for the tracking performance requirements, and uncertainty weighting function for modeling error. Firstly, the hydrodynamic characteristics and constraints of hover and steering are analyzed, and the mathematical model is established. Secondly, based on the analysis of the affection of steering motion on depth and trim state, a control strategy is proposed to suppress interference by setting steering speed and limiting acceleration, which improves the safety of the control algorithm. And then, a $H_\infty $ robust control algorithm based on three kinds of weighting function is designed. Finally, the method is simulated by using submarine model and validated by underwater vehicle experiment.The research in this article has potential application value for safety and low noise performance for the control of large underwater platform and manned submarines.

H∞ Lower Order Controller for AC Servo System of Multiple Rockets

Design problem of a H∞ robust lower order controller of an AC servo system of multiple rockets is studied. It is pointed out that the problem is actually H∞ robust performance design problem, H∞ robust stability and performance requirements (enhance rapidity, shorten overshoot and static error). It is also recommended to represent the robust performance design as H∞ robust control problem and solve H∞ low-order controller, if the uncertainty weighting function and performance weighting function of system bandwidth and interference attenuation are properly modified. Solved the H∞ low-order controller. The simulation results show that the robust H∞ lower order control system has advantages of small overshoot and short setup time. It can also improve control system’s anti-disturbance ability. The simulation example of the H∞ lower order control is also provided to demonstrate the effectively of the proposed design method. The H∞ lower order controller is easy for engineering implementation.

Linear parameter varying (LPV) based robust control of type-I diabetes driven for real patient data

Due to increasing prevalence of diabetes as well as increasing management costs, the artificial control of diabetes is a highly important task. Model-based design allows finding more effective solutions for the individual treatment of diabetic patients, but robustness is an important property that can be hardly guaranteed by the already developed individualized control algorithms. Modern robust control (known as H∞) theory represents an efficient possibility to solve robustness requirements in a general way based on exact mathematical formulation (Linear Matrix Inequalities) combined with knowledge-based expertise (through real patient data, uncertainty weighting functions can be formulated). When the difference between the nominal model and real patient dynamics is bounded and known, this approach becomes highly reliable. However, this requirement poses the greatest limitation since a model always represents an approximation of the complex physiological process. Consequently, the uncertainty formulation of the neglected dynamics becomes crucial as robust methods are very sensitive to them. In order to formulate them, large amount of real patient data and medical expertise is needed to cover the different life-style scenarios (especially the worst-case ones) that define the control space by the accumulated knowledge. On the other hand, H∞–based methods represent linear control techniques; hence their direct nonlinear application is important for a physiological process. The paper presents a roadmap of using modern robust control in diabetes focusing on nonlinear model-based interpretation: how the weighting functions should be selected based on (knowledge-based) medical expertise, the direct nonlinear applicability of the method taking additional advantage of the recently emerged Linear Parameter Varying (LPV) methodology, robust performance investigation and switching control possibilities. During the control characteristics discussion, the trade-off between the medical knowledge-based empiricism and exact control engineering formulation is introduced through different examples computed under MATLAB on real diabetic patient data.

Model-based angiogenic inhibition of tumor growth using modern robust control method

Cancer is one of the most destructive and lethal illnesses of the modern civilization. In the last decades, clinical cancer research shifted toward molecular targeted therapies which have limited side effects in comparison to conventional chemotherapy and radiation therapy. Antiangiogenic therapy is one of the most promising cancer treatment methods. The dynamical model for tumor growth under angiogenic stimulator/inhibitor control was posed by Hahnfeldt et al. in 1999; it was investigated and partly modified many times. In this paper, a modified version of the originally published model is used to describe a continuous infusion therapy. In order to generalize individualized therapies a robust control method is proposed using H∞ methodology. Uncertainty weighting functions are determined based on the real pathophysiological case and simulations are performed on different tumor volumes to demonstrate the robustness of the proposed method.

Model-based Angiogenic Inhibition of Tumor Growth using Modern Robust Control Method

Cancer is one of the most destructive and lethal illnesses of the modern civilization. In the last decades, clinical cancer research shifted towards molecular targeted therapies which have limited side effects in comparison to conventional chemotherapy and radiation therapy. Anti-angiogenic therapy is one of the most promising cancer treatment methods. The dynamical model for tumor growth under angiogenic stimulator/inhibitor control was posed by Hahnfeldt et al. (1999), and it was investigated and partly modified many times. In this paper, a modified version of the originally published model is used in order to describe a continuous infusion therapy. To generalize individualized therapies a robust control method is proposed using ℋ ∞ methodology. Uncertainty weighting functions are determined based on the real pathophysiological case and simulations are performed on different tumor volumes to demonstrate the robustness of the proposed method.

Linear parameter-varying techniques for control of a magnetic bearing system

In this paper, a linear parameter-varying (LPV) control design method is evaluated experimentally on an active magnetic bearing (AMB) system. A speed-dependent LPV model of the AMB system is derived. Model uncertainties are identified using artificial neural networks, and an uncertainty weighting function is approximated for LPV control synthesis. Experiments are conducted to verify the robustness of LPV controllers for a wide range of rotational speed. This LPV control approach eliminates the need for gain-scheduling, and provides better performance than the traditional proportional-integral-derivative control for high-speed operation.

Application of Two-Degree-of-Freedom Control to Multi-Axis Electrodynamic Shaking System Using-Synthesis and Adaptive Filter

In this paper, we propose a method of applying a two-degree-of-freedom (2DOF) control since such control of a multi-axis electrodynamic shaking system requires no iteration control and has real-time performance. We first introduce an ideal mathematical model and an uncertainty weighting function for the system, and design a feedback controller using μ-synthesis. Moreover, we adopt a 2DOF controller to improve the transient response. Then, an adaptive filter is added when control performance still cannot be satisfied. An adaptive filter is inserted in series in a feedforward block and is structured to compensate the difference between a nominal model and an actual plant. Also, uncertainty of the system is considered, and an adaptive filter based on the H∞ filter is employed. Lastly, we inspect the performance of the controller experimentally, using a resonant specimen. The proposed controller is confirmed to have achieved a superior performance in such a situation.

APPLICATION OF TWO-DEGREE-OF-FREEDOM CONTROL TO MULTI-AXIS ELECTRO-DYNAMIC SHAKING SYSTEM USING μ-SYNTHSIS AND ADAPTIVE FILTER

In this paper, we propose a method to apply a Two-Degree-of-Freedom (2DOF) control as such attempt of controlling a Multi-Axis Electro-dynamic shaking system has no requirement for iteration control and has real-time performance. We first introduce an ideal mathematical model and uncertainty weighting function for the system, and a feedback controller is designed using μ-synthesis. And we constitute 2DOF controller for a purpose to improve transient response. Then, an adaptive filter is added when control performance still can't be satisfied. Lastly, we show the performance of the controller by experiment using a resonant specimen.

Robust Control of Multi-Axis Electro-Dynamic Shaking System by 2-Degree-of-Freedom Control Using .MU.-Synthesis in Feedback Control.

In this paper, we propose a method to apply a Two-Degree-of-Freedom control with adaptive filter as such attempt of controlling a Multi-Axis Electro-Dynamic shaking system has no requirement for iteration control and has real-time performance. An adaptive filter is inserted in series to a feed-forward block and is structured to compensate difference between a nominal model and an actual plant. We first introduce an ideal mathematical model and uncertainty weighting function for the system, and a feedback controller is designed using μ-synthesis. And we constitute Two-Degree-of-Freedom controller for a purpose to improve transient response. Then, an adaptive filter is added when control performance still can't be satisfied. Lastly, we inspect the performance of the controller by experiment using an actual equipment loaded with a resonant specimen. An experiment without adaptive filter is done to show stability of the system. And the proposed controller is confirmed to have achieved a superior performance by using the adaptive filter.