Practical Issues in Nonlinear Model Predictive Control: Real-Time Optimization and Systematic Tuning

Ohtsuka, Toshiyuki; Ozaki, Kohei

doi:10.1007/978-3-642-01094-1_35

Cited by 6 publications

(9 citation statements)

References 20 publications

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“…However, this approach cannot be used for NMPC as the use of a nonlinear model converts the convex quadratic programming problem to a nonconvex nonlinear program. In another study [4], feedback linearization is combined with NMPC, and the proposed performance index consists of two tuning parameters that give a reasonable tradeoff between the transient responses of the controlled output and the control input. This tuning approach is simple but it depends on the input-output linearization of the given nonlinear system.…”

Section: Introductionmentioning

confidence: 99%

Tuning of Performance Index in Nonlinear Model Predictive Control by the Inverse Linear Quadratic Regulator Design Method

Tahir

Ohtsuka

2013

SICE Journal of Control, Measurement, and System Integration

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: This paper proposes the use of the inverse linear quadratic (ILQ) regulator design method for the efficient tuning of the performance index in nonlinear model predictive control (NMPC). First, a linear quadratic regulator is designed for the linearized model using the ILQ regulator design approach and then the inverse optimality conditions are applied to the designed regulator to tune the quadratic weights in the performance index of NMPC. After that, the NMPC algorithm is applied to the nonlinear model. This approach provides some tuning parameters that give a trade-off between the speed of the system's response and the magnitude of the control input. Moreover, this tuning methodology provides a free parameter that can be utilized to adjust the transient response as well as to obtain a balance between the magnitudes of the control inputs.Key Words : tuning of performance index, nonlinear model predictive control, inverse linear quadratic regulator design method, coupled three-tank system.

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Section: Introductionmentioning

confidence: 99%

Tuning of Performance Index in Nonlinear Model Predictive Control by the Inverse Linear Quadratic Regulator Design Method

Tahir

Ohtsuka

2013

SICE Journal of Control, Measurement, and System Integration

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“…where λ ∈ R nx denotes the costate, and µ ∈ R nc denotes the Lagrange multipliers associated with equality constraints. The first-order conditions necessary for the OCP are obtained by the calculus of variation as the Euler-Lagrange equations [31]:…”

Section: Real-time Nonlinear Model Predictive Controlmentioning

confidence: 99%

Nonlinear Model Predictive Control for Ecological Driver Assistance Systems in Electric Vehicles

Sajadi-Alamdari

Voos

Darouach

2019

Robotics and Autonomous Systems

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Battery Electric Vehicle (BEV) has one of the most promising drivetrain technology. However, the BEVs are facing the limited cruising range which generally reduces their share in the automotive market. Velocity profile, acceleration characteristics, road gradients, and drive techniques around curves have significant impacts on the energy consumption of the BEVs. A semi-autonomous ecological driver assistance system to regulate the velocity with energy-efficient techniques is proposed to address the limitation. The main contribution of this paper is the design of a real-time nonlinear model predictive controller with improved inequality constraints handling and economic penalty function to plan the online cost-effective cruising velocity. This system is based on the extended cruise control driver assistance system which controls the longitudinal velocity of the BEV in a safe and energy efficient manner by taking advantage of road slopes, effective drive around curves, and respecting the traffic regulation. A realtime optimisation algorithm is adapted and extended with economic objective function. Instead of the conventional Euclidean norms, deadzone penalty functions are proposed to achieve the economic objectives. In addition, the states $ This work is supported by the FNR "Fonds national de la Recherche" (Luxembourg) through the AFR "Aidesà la Formation-Recherche" Ph.D. grant scheme No. 7041503.

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“…However, this approach cannot be used for NMPC as the use of a nonlinear model converts the convex quadratic programming problem to a nonconvex nonlinear program. In another study [3], feedback linearization is combined with NMPC, and the proposed performance index consists of two tuning parameters that give a reasonable trade-off between the transient responses of the controlled output and the control input. This tuning approach is simple but it depends on the input-output linearization of the given nonlinear system.…”

Section: Introductionmentioning

confidence: 99%

Tuning of nonlinear model predictive controller for the speed control of spark ignition engines

Tahir

Ohtsuka

Shen

2013

2013 CACS International Automatic Control Conference (CACS)

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This paper presents the speed control of meanvalue model of spark ignition (SI) engines by efficiently tuning nonlinear model predictive controller (NMPC). The quadratic cost function in NMPC is tuned by applying the inverse optimality conditions on the linear quadratic regulator designed for the linearized model using the inverse linear quadratic (ILQ) regulator design method. This approach provides some tuning parameters that give a trade-off between the speed of the system's response and the magnitude of the control input. Effectiveness of the tuning method is elaborated in simulation results.

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Practical Issues in Nonlinear Model Predictive Control: Real-Time Optimization and Systematic Tuning

Cited by 6 publications

References 20 publications

Tuning of Performance Index in Nonlinear Model Predictive Control by the Inverse Linear Quadratic Regulator Design Method

Tuning of Performance Index in Nonlinear Model Predictive Control by the Inverse Linear Quadratic Regulator Design Method

Nonlinear Model Predictive Control for Ecological Driver Assistance Systems in Electric Vehicles

Tuning of nonlinear model predictive controller for the speed control of spark ignition engines

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