Extended Kalman Filter Based Nonlinear Model Predictive Control

Lee, Jay H.; Ricker, N. Lawrence

doi:10.1021/ie00030a013

Cited by 297 publications

(75 citation statements)

References 15 publications

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“…With some modifications we adopted the QDMC algorithm described in Garcia and Morshedi (1986). Although QDMC is a linear controller, with some modifications it can be applied to nonlinear plants (Lee 1994). By successive linearization of the nonlinear mechanical system at the working point, future control inputs are found by solving a linear QP.…”

Section: Quadratic Dynamic Matrix Controlmentioning

confidence: 99%

An alternative approach to synthesizing bipedal walking

Kooij

Jacobs²,

Koopman

et al. 2003

Biological Cybernetics

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Based on mechanical analysis, three gait descriptors are found which should be controlled to generate cyclic gait of a seven-link humanoid biped in the sagittal plane: (i) step length, (ii) step time, and (iii) the velocity of the center of mass (CoM) at push off. Two of these three gait descriptors can be chosen independently, since the CoM moves almost ballistically during the swing phase. These gait descriptors are formulated as end-point conditions and are regulated by a model predictive controller. In addition, continuous controls at the trunk and knees are implemented to maintain the trunk upright and to ensure weight bearing. The model predictive controller is realized by quadratic dynamic matrix control, which offers the possibility of including constraints that are exposed by the environment and the biped itself. Specifying step length and CoM velocity at push off, the controller generates a symmetric and stable gait. The proposed control scheme serves as a general-purpose solution for the generation of a bipedal gait. The proposed model contains fewer parameters than other models, and they are all directly related to determinants of bipedal gait: step length, trunk orientation, step time, walking velocity, and weight bearing. The proposed control objectives and the model of humanoid bipedal walking have potential applications in robotics and rehabilitation engineering.

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Section: Quadratic Dynamic Matrix Controlmentioning

confidence: 99%

An alternative approach to synthesizing bipedal walking

Kooij

Jacobs²,

Koopman

et al. 2003

Biological Cybernetics

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“…The feedback controller is designed using the Model Predictive Control (MPC) method (Lee and Ricker, 1994), and the general control structure of which is shown in figure 7.…”

Section: Model Predictive Feedback Control Modulementioning

confidence: 99%

“…(2) To reduce the effect of both system nonlinearity and parametric uncertainty in order to follow the driver's commands and maintain the body horizontal attitude better by using the model predictive control (MPC) (Lee and Ricker, 1994); (3) To assign the target driving torques and the target aircushion pressures by using sequential quadratic programming (SQP) method; (4) To realize the target driving torques and the target aircushion pressures by using an air-cushion inverse neural network model and a PID controller.…”

Section: Introductionmentioning

confidence: 99%

Controller design for a semi-track air-cushion vehicle

Xie

et al. 2015

Int.J Automot. Technol.

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To enhance the trafficability of semi-track air-cushion vehicle (STACV), a prototype is manufactured and a heirarchy controller is designed. The control objectives are, 1) to minimize the total power consumption; 2) to follow the driver's commands and maintain the body attitude; 3) to assign and realize the generalized forces. The controller is proposed by using carefully designed controller configuration, neural network, Model Predictive Control (MPC) method, hybrid generalized extremal optimization (HGEO), etc. Finally, the effectiveness of the proposed controller is verified by experiments under two typical operation conditions.

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“…Extended Kalman filtering (EKF): As the name indicates, EKF is an extension of Kalman filtering (KF) (Lee and Ricker 1994), where the model is linearized to estimate the covariance matrix of the state vector. As in KF, the state vector x k is estimated by minimizing a weighted covariance matrix of the estimation error, i.e.,…”

Section: Description Of State Estimation Techniquesmentioning

confidence: 99%

Model-based and model-free filtering of genomic data

Nounou

Mansouri

2013

Netw Model Anal Health Inform Bioinforma

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Biological measurements are a rich source of information about the biological phenomena that are represented. For example, time-series dynamic genomic or metabolic microarray data can be used to construct dynamic genetic regulatory network models, which can be used to better understand the interactions among different genes within the biological system and to design intervention strategies to cure or manage major diseases. Unfortunately, biological measurements are usually highly contaminated with errors that mask the important features in the data and limit their applicability. Therefore, these noisy measurements need to be filtered to enhance their usefulness in practice. In this work, various model-based and model-free data filtering techniques are used to denoise (or filter) genomic data. In the availability of a dynamic model representing the biological system, state estimation techniques, such as extended Kalman filtering (EKF), unscented Kalman filtering (UKF), and particle filtering (PF) are used to filter the measured data. When a model is not available, on the other hand, low-pass as well as multiscale filtering techniques will be utilized. Low-pass filters include the mean and exponentially weighted moving average filters, while the multiscale filters include several online as well as batch wavelet-based thresholding techniques. In this paper, the performances of all filtering techniques will be demonstrated and compared through their application using simulated time-series metabolic data contaminated with white noise. The results show clear advantages for the model-based over the model-free filtering techniques, and that the PF outperforms other modelbased methods. The results also show that in the absence of a model of the biological system, the model-free filtering techniques, especially multiscale filtering, can also provide acceptable performances. Online multiscale (OLMS) filtering is shown to outperform low-pass filtering, and the batch multiscale methods, i.e., translation invariant (TI) and boundary corrected TI (BCTI) provide enhanced smoothness, with improved ability of BCTI over TI at the edges. From a biological perspective, the model-based and online model-free filtering techniques can be used when filtering is needed online, such as within an intervention framework to cure diseases, while the batch model-free filtering techniques can be used within a modeling framework to enhance the quality of the estimated biological models.

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Extended Kalman Filter Based Nonlinear Model Predictive Control

Cited by 297 publications

References 15 publications

An alternative approach to synthesizing bipedal walking

An alternative approach to synthesizing bipedal walking

Controller design for a semi-track air-cushion vehicle

Model-based and model-free filtering of genomic data

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