Simultaneous State and Parameter Estimation using Receding-horizon Nonlinear Kalman Filter

Rangegowda, Pavanraj H.; Valluru, Jayaram; Patwardhan, Sachin C.; Mukhopadhyay, Siddhartha

doi:10.1016/j.ifacol.2018.09.335

Cited by 14 publications

(9 citation statements)

References 8 publications

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“…The pneumatic tire is modeled by a linear spring of stiffness k t . The vertical tire stiffness k t is the unknown parameter, which is modeled as the state variable for random walk model [17]. This state-space model can be represented as a global structure…”

Section: State-space Model Of Quarter-car Suspension Systemsmentioning

confidence: 99%

New Indirect Tire Pressure Monitoring System Enabled by Adaptive Extended Kalman Filtering of Vehicle Suspension Systems

2021

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This paper presents a new indirect tire pressure monitoring system (TPMS) based on adaptive extended Kalman filtering with unknown input (AEKF-UI) estimation of vehicle suspension systems. The suggested methodology is based on the explicit correlation between tire pressure and tire stiffness and is available in real time. AEKF-UI is used to simultaneously estimate the time-varying parameter (tire stiffness) of vehicle suspension systems and the road roughness using an unknown input estimator. Simulation studies demonstrate that the proposed algorithm can simultaneously estimate tire stiffness (i.e., tire inflation pressure) variation and unknown road roughness input. The feasibility and effectiveness of the proposed estimation algorithm are verified through a laboratory-level experiment. This study offers a potential application for an alternative indirect TPMS and the estimation of unknown road roughness used for automotive controller design.

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Section: State-space Model Of Quarter-car Suspension Systemsmentioning

confidence: 99%

New Indirect Tire Pressure Monitoring System Enabled by Adaptive Extended Kalman Filtering of Vehicle Suspension Systems

2021

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“…State and/or Parameter Receding or Moving Horizon Observers RHO (see [Michalska et al (1995)], [Muske et al (1995) [Rangegowda et al (2018)]) provide an estimate of both x and θ of the true x and θ by minimizing the output prediction error in the least-square sense over a past receding horizon defined by horizon T, at each time ₖ:…”

Section: The Receding or Moving Horizon Observer Formulationmentioning

confidence: 99%

“…• The measurement operator related to crowd sensors (individuals carrying smartphones), and the time-varying locations of these crowd sensors are denoted as (t) = (μc(t)) z(t), ∈ ℝ c and μc(t), respectively, where Nc is the number of crowd sensors. [Alamir et al (2003)], [Mohd Ali et al (2015)], [Rangegowda et al (2018)] When one considers systems governed by PDEs, the individual measurement operator j , = s,m,c, for any sensor j is defined as follows:…”

Section: Optimal Monitoring Architecturementioning

confidence: 99%

Towards Optimal Architectures for Hazard Monitoring Based on Sensor Networks and Crowdsensing

Duby

2020

IDRiM Journal

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The monitoring of hazards through the ability to detect events and predict spatial and temporal evolution of dynamical hazards still remains a big challenge for dynamic disaster risk assessment and mitigation. The goal of this paper is to show how well established methods arising from the control theory can positively contribute to dynamic risk assessment improvement through an effective hazard monitoring. More precisely, the objective is threefold. Firstly, the design of an optimal monitoring architecture is proposed based on the combination of optimal sensor placement and receding horizon observer design. In this paper, the focus is only made on model-based and data-driven approaches. The benefit of using sensor networks and crowdsensing techniques is also discussed. Secondly, the paper seeks to identify the application areas that can benefit from both optimal sensor location techniques and receding horizon observers, while reviewing already existing references. Thirdly, some personal contributions illustrating the proposed approach are presented. In particular, two case studies are presented: one considers the dynamic positioning of drones for monitoring air pollution, the other is dedicated to the early detection of a wildfire outbreak.

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“…window parameter estimator can be found in Valluru et al 39 Also, alternate simultaneous state and parameter estimation schemes based on assumption 2 are available in the literature. 46,47 At each instant k, after invoking the chosen simultaneous state and parameter estimation scheme, we set θ k = θ ̂k|k , and the updated parameter vector θ k is used in NMPC and RTO formulations discussed in the subsequent subsections.…”

Section: Details Of Maximum a Priori (Map) Version Of The Movingmentioning

confidence: 99%

An Integrated Frequent RTO and Adaptive Nonlinear MPC Scheme Based on Simultaneous Bayesian State and Parameter Estimation

Valluru

Patwardhan

2019

Ind. Eng. Chem. Res.

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Changes in process parameters and measured/unmeasured disturbances shift the optimal point at which the economic benefits are maximized. Combinations of real time optimization (RTO) techniques, which periodically determine the economically optimum operating point using steady state optimization, and model predictive control (MPC) have been widely employed in the process industry for operating process plants optimally in the face of drifting disturbances and/or parameters. Due to the long wait time between two successive RTO invocations and model inconsistency between the RTO and MPC layers, the conventional RTO schemes can end up operating the plant suboptimally if the process parameters/unmeasured disturbances change significantly during the wait time. Recently proposed frequent RTO approaches attempt to address this difficulty by increasing the frequency of RTO invocation. An online update of the steady state model employed by the RTO layer using dynamic operating data is a major concern in implementation of frequent RTO. Use of a dynamic model based state and parameter estimation for maintenance of the model used in frequent RTO has not received much attention in the literature. In this work, it is proposed to develop a novel integrated frequent RTO and adaptive nonlinear MPC (NMPC) approach for operating a unit operation in a economically optimal manner. A dynamic mechanistic model based simultaneous state and parameter estimation scheme is used as a common link between the RTO and the NMPC components. Estimates of the drifting unmeasured disturbances/parameters generated by the state and parameter estimator are used to update the steady state model used for frequent RTO and the dynamic model used for predictions in NMPC. Use of a single model for carrying out RTO as well as control tasks eliminates difficulties that arise due to the mismatch between models used for RTO and control. The efficacy of the proposed integrated optimizing control scheme is demonstrated by conducting simulation studies on benchmark systems from the literature. Analysis of simulation results reveals that the proposed integrated online optimizing control scheme maintains these systems at their respective economically optimal operating points in the presence of drifting disturbances/parameters.

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Simultaneous State and Parameter Estimation using Receding-horizon Nonlinear Kalman Filter

Cited by 14 publications

References 8 publications

New Indirect Tire Pressure Monitoring System Enabled by Adaptive Extended Kalman Filtering of Vehicle Suspension Systems

New Indirect Tire Pressure Monitoring System Enabled by Adaptive Extended Kalman Filtering of Vehicle Suspension Systems

Towards Optimal Architectures for Hazard Monitoring Based on Sensor Networks and Crowdsensing

An Integrated Frequent RTO and Adaptive Nonlinear MPC Scheme Based on Simultaneous Bayesian State and Parameter Estimation

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