Disturbance rejection in space applications: Problems and solutions

Canuto, Enrico; Molano-Jimenez, Andres; Montenegro, Carlos Perez

doi:10.1016/j.actaastro.2011.09.010

Cited by 16 publications

(13 citation statements)

References 15 publications

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“…T v as in [12], where 0.01 T s . The design state equation (4), whose block diagram is in Figure 2, right, has the following vectors and matrices 1 2 …”

Section: 2mentioning

confidence: 98%

“…According to [11] and [12], the model q e is associated to the former set and g e to the latter one. In this way the uncertainty estimator is decoupled, and may exploit different sampling rates.…”

Section: 2mentioning

confidence: 99%

“…The SISO case has been treated in [1], [2] and [3]. A case study, spacecraft attitude control accompanies the paper (see also [12]). …”

Section: Introductionmentioning

confidence: 99%

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Embedded Model Control: Design separation under uncertainty

Canuto

Montenegro

Colangelo

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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The key signal of the Embedded Model Control (EMC) is the model error, measured minus model output, which is used by Internal Model Control and in a more efficient way by Active Disturbance Rejection Control (ADRC). EMC extends ADRC along four lines: a generic disturbance dynamics, design of the noise layout (where uncertainty enters the model), dynamic uncertainty estimation, a design degree of freedom similar to Youla parameterization. The latter freedom is shown to favor separation between control and estimation under uncertainty, a principle hidden in ADRC. As a result, control law can be practically designed model-based, only limited by command saturation. The main theorems are developed in some detail starting from two model classes (design and embedded model), that are essential to define uncertainty. The paper extends part of previous results from SISO to MIMO systems, but some theorems and the free feedback dynamics are original. Theory is accompanied by a study case.

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“…T v as in [12], where 0.01 T s . The design state equation (4), whose block diagram is in Figure 2, right, has the following vectors and matrices 1 2 …”

Section: 2mentioning

confidence: 98%

Section: 2mentioning

confidence: 99%

See 1 more Smart Citation

Embedded Model Control: Design separation under uncertainty

Canuto

Montenegro

Colangelo

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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“…Therefore, good navigation which can estimate the complete vehicle states accurately is essential for the pinpoint landing. Furthermore, the navigation completed with disturbance estimation is required for a better navigation performance (Canuto et al, 2012). This paper will focus on obtaining the complete accurate vehicle states by constructing an innovative navigation scheme.…”

Section: Introductionmentioning

confidence: 98%

An innovative navigation scheme of powered descent phase for Mars pinpoint landing

Qin

Zhu

Cui

et al. 2014

Advances in Space Research

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“…Since the filter feedback is static, closedloop stability demands all the state variables, also the noise-free ones, to be excited by a feedback component. EMC follows a different way: noise layout must be designed as in [30] and [31] according to the desired uncertainty class; embedded model should not be corrupted by parasitic noise. Since noise plays the role of the uncertainty input, this way offers flexibility and efficiency in the real-time uncertainty estimation and discrimination.…”

Section: Introductionmentioning

confidence: 99%

The four-tank control problem: Comparison of two disturbance rejection control solutions

2017

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The paper aims to compare and prove a pair of disturbance/uncertainty rejection control laws for the well-known four tank control problems. Control requirements are expressed in terms of a set point sequence as it usual in the literature. Uncertainty class is defined as the union of four sub-classes: unknown disturbance, parametric uncertainty, measurement errors and neglected dynamics. Modelling and design allow insight of the dynamic properties of the problem. They are formulated by a pair of theorems which fix the range of application. Theorem are confirmed by the results simulated runs, and indicate the correct way to further broaden control design applicability. Disturbance rejection (better uncertainty) design is deployed using the Embedded Model Control methodology: only unknown disturbance and parametric uncertainty can be rejected, whereas neglected dynamics effects must be filtered. As a result, simple performance and stability inequality can be formulated in the frequency domain and lead to closed-loop pole placement. Inequalities are such to reveal whether pole placement is feasible and how feasibility can be recovered, an issue which at authors knowledge is rarely encountered in the literature. Simulated runs prove the design procedure.

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Disturbance rejection in space applications: Problems and solutions

Cited by 16 publications

References 15 publications

Embedded Model Control: Design separation under uncertainty

Embedded Model Control: Design separation under uncertainty

An innovative navigation scheme of powered descent phase for Mars pinpoint landing

The four-tank control problem: Comparison of two disturbance rejection control solutions

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