State estimation of stochastic non-linear hybrid dynamic system using an interacting multiple model algorithm

Elenchezhiyan, M.; Prakash, J.

doi:10.1016/j.isatra.2015.06.005

Cited by 17 publications

(13 citation statements)

References 21 publications

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“…For the numerical simulation, two sets of experiments were simulated: (1) the standard IMM [9] and CPIMM algorithms; (2) the IMM-UKF [43], IMM-NAUKF connecting the standard IMM with NAUKF, CPIMM-UKF connecting the CPIMM with standard UKF, and CPIMM-NAUKF algorithms. The experimental environment was set as Intel i7 computer with 4-core, 64-bit, 2.4 GHz, 8 GB RAM, and MATLAB R2013a software.…”

Section: Resultsmentioning

confidence: 99%

A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults

et al. 2017

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Featured Application: The work is very effective and applicable in the fields of the maneuvering target tracking and navigations system design.Abstract: Aimed at solving the problem of decreased filtering precision while maneuvering target tracking caused by non-Gaussian distribution and sensor faults, we developed an efficient interacting multiple model-unscented Kalman filter (IMM-UKF) algorithm. By dividing the IMM-UKF into two links, the algorithm introduces the cubature principle to approximate the probability density of the random variable, after the interaction, by considering the external link of IMM-UKF, which constitutes the cubature-principle-assisted IMM method (CPIMM) for solving the non-Gaussian problem, and leads to an adaptive matrix to balance the contribution of the state. The algorithm provides filtering solutions by considering the internal link of IMM-UKF, which is called a new adaptive UKF algorithm (NAUKF) to address sensor faults. The proposed CPIMM-NAUKF is evaluated in a numerical simulation and two practical experiments including one navigation experiment and one maneuvering target tracking experiment. The simulation and experiment results show that the proposed CPIMM-NAUKF has greater filtering precision and faster convergence than the existing IMM-UKF. The proposed algorithm achieves a very good tracking performance, and will be effective and applicable in the field of maneuvering target tracking.

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Section: Resultsmentioning

confidence: 99%

A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults

et al. 2017

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“…In comparison to previous strategies that deal with similar control problems (e.g., [17,21,22,24,25,27]), the proposed controllers present the advantage of avoiding a stochastic modeling, as it is typically required to deal with systems where the parameters are contaminated by white noise. The trade-off between the response speed and the stability is solved through a simple Monte Carlo experiment.…”

Section: Discussionmentioning

confidence: 99%

“…Considering the value of the partial derivatives of the neural network (see (27)) and by substitution in (B.10), it yields Δ̃= −̃(x , ) (x , ) . From (B.19), it follows that when = 0, tends to zero when tends to infinity.…”

Section: δ̃= − (̂(mentioning

confidence: 99%

“…Additionally, due to the inherent nature of a bioprocess, a control scheme based on a stochastic model seems to be adequate. The specific growth rate can be modeled as affected by a white noise representing environment uncertainties, and the mathematical model consists of a set of stochastic differential equations [27] (some applications can be consulted in [28,29]). The application of this approach onto a real plant is quite difficult due to its complex design and implementation.…”

Section: Introductionmentioning

confidence: 99%

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Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

et al. 2017

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The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.

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“…To reduce the complexity of tuning a complex anaerobic digestion model, a particle swarm optimizationbased smart algorithm was developed to estimate all parameters [33]. Elenchezhiyan and Prakash [34] formulated state estimation schemes for nonlinear hybrid dynamic systems subjected to stochastic state disturbances and random errors in measurements using interacting multiple-model algorithms. In Pantano et al's study [35], the problem of optimal profiles tracking control under uncertainties for a fed-batch bioprocess with two control actions is addressed.…”

Section: Introductionmentioning

confidence: 99%

Multivariable Tracking Control of a Bioethanol Process under Uncertainties

Fernández

Pantano

Serrano

et al. 2020

Mathematical Problems in Engineering

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Bioethanol is one of the most studied alternative fuels nowadays. Due to its production process complexity and the low quality of the mathematical models that describe it, a reliable controller is needed to maximize the fuel production and minimize its environmental impact, even in the presence of uncertainty. Here, a controller for tracking optimal profiles considering model errors and external perturbations is proposed. This work is an improvement of a previously presented technique. To reduce the earlier mentioned uncertainties’ effect during the fermentation, some tracking error integrators are added in the control action calculation. This simple modification ensures the tracking error convergence to zero, even in the presence of uncertainties (demonstration available). Different tests are carried out and a performance comparison with the original controller is shown to highlight improvements in the tracking error of up to 98% when integrators are incorporated. Furthermore, a classical PI controller is contrasted with the proposed techniques.

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State estimation of stochastic non-linear hybrid dynamic system using an interacting multiple model algorithm

Cited by 17 publications

References 21 publications

A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults

A Cubature-Principle-Assisted IMM-Adaptive UKF Algorithm for Maneuvering Target Tracking Caused by Sensor Faults

Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

Multivariable Tracking Control of a Bioethanol Process under Uncertainties

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