State estimation and nonlinear predictive control of autonomous hybrid system using derivative free state estimators

Prakash, J.; Patwardhan, Sachin C.; Shah, Sirish L.

doi:10.1016/j.jprocont.2010.04.001

Cited by 60 publications

(45 citation statements)

References 14 publications

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“…Out of different state estimation schemes, UKF based scheme is the mostly suggested technique for hybrid systems as it is suitable for highly nonlinear systems [8,9,[20][21][22]. The nonlinear and derivative free nature [21] of this algorithm makes the efficient state estimation of highly complex and nonlinear systems like hybrid systems possible.…”

Section: State Estimation Algorithmsmentioning

confidence: 99%

“…2 [22]. Different notations used in this section are similar to that in [20] Dimension and description of variables used in the algorithms are given below.…”

Section: State Estimation Algorithmsmentioning

confidence: 99%

“…In hybrid systems, for the controller design, its mode switching property has also to be taken in to account. For such processes, model based control schemes are proposed in the literature [7][8][9][20][21][22] for obtaining the satisfactory control of the output variables.…”

Section: Estimator Based Controller [22]mentioning

confidence: 99%

“…These state estimation methods were also used in estimating the unknown process noise covariance [18] and discrete state variables [19]. The nonlinear model predictive control (NMPC) in [20] and fault tolerant model predictive control in [21], for hybrid systems, utilized this in its state estimation part. UKF based inverse dynamics controller was developed in [22] for controlling the non-measurable states of the hybrid systems.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Experimental Investigations on Performance Comparison of EKF and ANN Based Controllers with UKF Based Controller for Autonomous Hybrid Systems with Uncertainties

Vellayikot¹,

Vaidyan²

2016

Journal of Electrical Engineering and Technology

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-This paper proposes autonomous hybrid system (AHS) with estimator based inverse dynamics controller along with extended kalman filter and artificial neural network based state estimators ensuring best performance and robustness by minimum ISE in controlling non-measurable state variables of autonomous hybrid systems. With the help of experimental setup of benchmark model of AHS (hybrid three-tank system), the detailed performance comparison of these proposed methods was made both qualitatively and quantitatively with that of existing method (AHS with unscented kalman filter and inverse dynamics controller (UKFC)) under different operating conditions such as servo and regulatory operations, process-model parameter uncertainties, initial condition mismatch, and different types of faults in the system, subsequently the results are reported.

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Section: State Estimation Algorithmsmentioning

confidence: 99%

“…2 [22]. Different notations used in this section are similar to that in [20] Dimension and description of variables used in the algorithms are given below.…”

Section: State Estimation Algorithmsmentioning

confidence: 99%

Section: Estimator Based Controller [22]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Experimental Investigations on Performance Comparison of EKF and ANN Based Controllers with UKF Based Controller for Autonomous Hybrid Systems with Uncertainties

Vellayikot¹,

Vaidyan²

2016

Journal of Electrical Engineering and Technology

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show abstract

“…In view of these, model predictive control (MPC) can be a suitable choice and has been intensively studied [21][22][23][24][25][26][27][28]. However, though MPC has low requirements for process model accuracy and its control performance is good [29][30][31][32][33][34][35], its implementation is not as simple as PI-PD controller.…”

Section: Introductionmentioning

confidence: 99%

Dynamic matrix control optimization based new PIPD type control for outlet temperature in a coke furnace

Zhou

Zhang

2015

Chemometrics and Intelligent Laboratory Systems

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State estimation and non‐linear model based control of a continuous stirred tank reactor using unscented Kalman filter

Panda

Prakash

2017

Can J Chem Eng

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In this paper a non-linear model based control (NMBC) scheme for the continuous stirred tank reactor (CSTR) process is proposed. The proposed control law is a function of the model state(s) and the measured output variable(s). Performance comparison of the proposed NMBC scheme with that of non-linear model predictive control scheme (NMPC) is reported. From the extensive simulation studies on the CSTR process, it can be inferred that servo and regulatory performances of the proposed NMBC scheme is found to closely match the NMPC scheme, with the proposed control scheme being computationally less intensive. In addition, a non-linear model based control scheme using an unscented Kalman filter (UKF-NMBC) and an inferential control scheme (INF-UKF-NMBC) for the CSTR process affected by random disturbances and random errors in measurements are formulated. Performance comparisons of the UKF-NMBC with UKF based NMPC and INF-AUKF-NMBC with INF-AUKF-NMPC, highlighting offset free servo performance, rejection of unmeasured disturbance, and robustness to model-plant mismatch are reported.

show abstract

State estimation and nonlinear predictive control of autonomous hybrid system using derivative free state estimators

Cited by 60 publications

References 14 publications

Experimental Investigations on Performance Comparison of EKF and ANN Based Controllers with UKF Based Controller for Autonomous Hybrid Systems with Uncertainties

Experimental Investigations on Performance Comparison of EKF and ANN Based Controllers with UKF Based Controller for Autonomous Hybrid Systems with Uncertainties

Dynamic matrix control optimization based new PIPD type control for outlet temperature in a coke furnace

State estimation and non‐linear model based control of a continuous stirred tank reactor using unscented Kalman filter

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