A moving horizon‐based approach for least‐squares estimation

Robertson, Donald; Lee, Jay H.; Rawlings, James B.

doi:10.1002/aic.690420811

Cited by 314 publications

(168 citation statements)

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“…CEKF is an alternative state estimator based on optimization, originated from MHE, introduced by [10], for a horizon length equals to zero [1]. The basic equations of CEKF can be divided, like in the EKF, in prediction and updating stages [2].…”

Section: Constrained Extended Kalman Filter Estimationmentioning

confidence: 99%

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Data treatment and analysis for on-line dynamic process optimization

Salau

Tonel

Trierweiler

et al. 2008

Computer Aided Chemical Engineering

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The filters tuning is a crucial issue due the need to quantify the accuracy of the model in terms of the process noise covariance matrix for process characterized by structural uncertainties which are time-varying. Thus, approaches to time-varying covariances were studied and included to a traditional EKF and an optimization-based state estimators constrained EKF (CEKF) formulations. The results for these approaches have shown a significant improvement in filters performance. Furthermore, the performance of these estimators as a transient data reconciliation technique has been appraised and the results have shown the CEKF suitability for this proposes.

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Section: Constrained Extended Kalman Filter Estimationmentioning

confidence: 99%

“…The benefits of them arise due to the possibility to consider states physical constraints into an optimization problem [1,2]. An important issue in applying state estimators is the appropriate choice of the process and measurement noise covariances.…”

Section: Introductionmentioning

confidence: 99%

Data treatment and analysis for on-line dynamic process optimization

Salau

Tonel

Trierweiler

et al. 2008

Computer Aided Chemical Engineering

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“…The result was improved by Michalska and Mayne (1993) by relaxing the equality constraints with a terminal constraint set and a suboptimal solution can be found without compromising the stability of the MPC. This allows the MPC to be feasible for many applications and also inspired many researches to further develop many types of robust MPC with a modified terminal penalty (Chisci, et al, 1996;Robertson, et al, 1996;Chen and Allgöwer, 1998). The survey conducted by Mayne (2000) provided an abundant review about the stability methodologies of the MPC, which is regarded as the foundation of MPC stability research.…”

Section: Stability Issues For the Mpc 25mentioning

confidence: 99%

“…Despite being less popular than the MPC, the MHE is still suggested as a practical method to incorporate inequality constraints in estimation for many applications, e.g. (Robertson, et al, 1996). Also the MHE is proven to be a powerful tool to deal with the quantization noise, data loss and time delay introduced in the WCS, e.g.…”

Section: Moving Horizon Estimator and 1-bit Processingmentioning

confidence: 99%

1-Bit processing based model predictive control for fractionated satellite missions

Bai

2014

Acta Astronautica

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In this thesis, a 1-bit processing based Model Predictive Control (OBMPC) structure is proposed for a fractionated satellite attitude control mission. Despite the appealing advantages of the MPC algorithm towards constrained MIMO control applications, implementing the MPC algorithm onboard a small satellite is certainly challenging due to the limited onboard resources. The proposed design is based on the 1-bit processing concept, which takes advantage of the affine relation between the 1-bit state feedback and multi-bit parameters to implement a multiplier free MPC controller.As multipliers are the major power consumer in online optimization, the OBMPC structure is proven to be more efficient in comparison to the conventional MPC implementation in term of power and circuit complexity. The system is in digital control nature, affected by quantization noise introduced by Δ∑ modulators. The stability issues and practical design criteria are also discussed in this work.Some other aspects are considered in this work to complete the control system. Firstly, the implementation of the OBMPC system relies on the 1-bit state feedbacks. Hence, 1-bit sensing components are needed to implement the OBMPC system. While the ∆∑ modulator based Microelectromechanical systems (MEMS) gyroscope is considered in this work, it is possible to implement this concept into other sensing components.Secondly, as the proposed attitude mission is based on the wireless inter-satellite link (ISL), a state estimator is required. However, conventional state estimators will once again introduce multi-bit signals, and compromise the simple, direct implementation of the OBMPC controller. Therefore, the 1-bit state estimator is also designed in this work to satisfy the requirements of the proposed fractionated attitude control mission.The simulation for the OBMPC is based on a 2U CubeSat model in a fractionated satellite structure, in which the payload and actuators are separated from the controller and controlled via the ISL. Matlab simulations and FPGA implementation based performance analysis shows that the OBMPC is feasible for fractionated satellite missions and is advantageous over the conventional MPC controllers. V VI

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“…Two commonly used state estimation methods for nonlinear systems are the extended Kalman filter (EKF) 11 and the moving horizon state estimation (MHE). 12 To handle nonlinearity, in EKF, successive linearization of the nonlinear system is performed every sampling time. The MHE is an optimization-based state estimator that can handle nonlinear systems and system constraints.…”

mentioning

confidence: 99%

Distributed Extended Kalman Filtering for Wastewater Treatment Processes

Zeng

Liu

Zou

et al. 2016

Ind. Eng. Chem. Res.

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A wastewater treatment plant is a large-scale nonlinear system including a series of biological reactors and a settler. In this work, we propose a distributed state estimation scheme for wastewater treatment processes in the context of extended Kalman filtering. Specifically, we consider a wastewater treatment process that includes a fivecompartment reactor and an ideal splitter. First, we present a method to design the sensor network for the process and then discuss how the process may be decomposed into subsystems for distributed state estimation. We present a detailed design of the distributed filters and a detailed distributed state estimation algorithm to coordinate the actions of the different filters. Without loss of generality, we consider the entire system as being decomposed into two subsystems. The proposed approach can be extended in a straightforward fashion to include more subsystems. The distributed scheme is compared with the corresponding centralized extended Kalman filtering scheme under different weather conditions. Simulation results show that the distributed scheme can give comparable estimation performance to the centralized scheme or even better performance than the centralized scheme. Also, the distributed estimation scheme is shown to have more stable performance under different noise conditions. ■ INTRODUCTIONWastewater treatment is an important step in water recycling, and it involves complex biological and physical phenomena. A wastewater treatment plant (WWTP) is typically a large-scale nonlinear system including a series of biological reactors and a settler. The effluent quality of a wastewater treatment plant is closely related to the sustainability of the environment and normally is regulated by environmental legislations. The significant variability of the influent flow and the composition of the flow to a WWTP poses significant challenges in the associated control system design.Different process control schemes have been reported for WWTPs. Proportional−integral (PI) control is one of the most commonly used strategies 1,2 because of its simplicity in design and implementation. Feedforward control has also been implemented together with PI control to improve the control performance. 3 While PI control can achieve the stability requirement in the operation, it cannot handle constraints and is not optimal. Model predictive control (MPC) is an online optimization-based approach and can take input and state constraints explicitly into account. 4 MPC as an optimal control method has also been widely used in the control of WWTPs. , an economic MPC algorithm was applied to a WWTP, and the economic MPC performance was compared with PI and regular MPC. It was shown that economic MPC is able to achieve improved effluent quality.While there are many results on the control system design for WWTPs, relatively less attention has been given to the state estimation of WWTPs. State estimation is a process of constructing system states based on output measurements and a system model. Sta...

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A moving horizon‐based approach for least‐squares estimation

Cited by 314 publications

References 31 publications

Data treatment and analysis for on-line dynamic process optimization

Data treatment and analysis for on-line dynamic process optimization

1-Bit processing based model predictive control for fractionated satellite missions

Distributed Extended Kalman Filtering for Wastewater Treatment Processes

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