Observers for stabilization of systems with matched uncertainty

Jabbari, Faryar; Benson, R. W.

doi:10.1007/bf02169519

Cited by 19 publications

(5 citation statements)

References 15 publications

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“…Lemma 3 [22]. For any n ¥ m matrices X and Y and for any scalar a > 0 and b > 0, we have: Let b1 > 0, b2 > 0, and h > 0 be adjustable parameters.…”

Section: A C I a Amentioning

confidence: 99%

“…Generally speaking, for any b1 and b2 where a solution to the algebraic Riccati equation (22) exists (which is used for the state feedback gain), we can find an h large enough such that a solution to (23) exits. This means that a high gain compensator can be used to accomplish the task.…”

Section: G H P P D G H P D D P H Hmentioning

confidence: 99%

“…One can see that the system satisfies the three assumptions. We choose the measurable variable s as Given Q11 = Q22 = I4, R = 1, b1 = 0.5, b2 = 10, and h = 10 for solving Algebraic Riccati equations (22) and (23), we can obtain: s 0 0 5 = . Fig.…”

Section: Numerical Examplementioning

confidence: 99%

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Disturbance Observer Based Dynamic Output Feedback Controller Design for Linear Uncertain Systems

Chang

Wu²

2012

Asian Journal of Control

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For linear multiple input multiple output (MIMO) systems with mismatched parameter uncertainties and matched nonlinear perturbations, a compensator‐based controller based on the disturbance estimation algorithm is considered in this paper. We design a reduced‐order high‐gain observer to estimate the effect of matched perturbation, and introduce the estimation information into the controller design. Using singular perturbation theory, the proposed control law can guarantee robust stability of the closed‐loop system. Moreover, the peaking phenomenon in the control input during the transient time can be effectively avoided using our method. Finally, the feasibility of the proposed method is illustrated by a numerical example.

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“…Lemma 3 [22]. For any n ¥ m matrices X and Y and for any scalar a > 0 and b > 0, we have: Let b1 > 0, b2 > 0, and h > 0 be adjustable parameters.…”

Section: A C I a Amentioning

confidence: 99%

Section: G H P P D G H P D D P H Hmentioning

confidence: 99%

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Disturbance Observer Based Dynamic Output Feedback Controller Design for Linear Uncertain Systems

Chang

Wu²

2012

Asian Journal of Control

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“…Meanwhile, Zhang et al [11] have built a H ∞ high-order PI flter to estimate the actuator faults and attenuate the perturbation impact. On the other hand, too few studies are mentioned for the impact of parametric uncertainty [12] on PI estimator, which causes estimator instability by generating state x in the dynamics of estimation error _ e [13]. Currently, the common estimation method in the study of automotive vibration can be mentioned as: the usual Kalman estimator (KE) [7,[14][15][16][17]; KE is understood as optimal regression data processing algorithm.…”

Section: Introductionmentioning

confidence: 99%

Designing a Kalman–Bucy State Estimator for a Full Vertical Car Model

Hung

Tấn

2023

Mathematical Problems in Engineering

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Currently, mechatronic systems are commonly used in automotive control systems to improve ride comfort and road safety characteristics. In these control systems, the sensors used to determine the input signals of controllers are very important to help the calculation process and give the correct control output signals for actuators. However, not all input signals can be easily measured directly by sensors or their cost is too high. This paper proposes one using the Kalman–Bucy estimator to estimate the state variables of a two-axle car to study the automotive suspension system. The disturbance input is the road profile at the four tires, while the car speed is considered from 40 km/h to 120 km/h. This research only uses four signals, namely, the vertical displacement of the four unsprung masses, through the estimator it can determine 14 variables of the vector state of the general vertical model of the car. The advantage of this research is that it is possible to reduce the number of the sensors but still ensure that the necessary input signals are obtained for studying the full car model. The signal estimation through some sensors available on the car is necessary for practice. Survey results show that the signal quantification through the Kalman–Bucy estimator unit has an accuracy of over 96% compared to the original car’s signals.

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“…The equilibrium point of the direct output voltage regulation system will then become unstable. In robust control area, it is very common to maintain the minimum phase property of the uncertain systems within the variation ranges of the uncertainties [8][9][10].…”

Section: Introductionmentioning

confidence: 99%