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

Tahir, Fatima; Ohtsuka, Toshiyuki; Shen, Tielong

doi:10.1109/cacs.2013.6734135

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…It might seem easy to define a performance index for the closed loop response as a similar index is defined for the OCP that is solved periodically, but there could be more aspects that are important for the control engineer that are not represented in the objective function of the OCP. Moreover, the OCP is usually nonlinear and is subject to inequality constraints, which make it impossible to find an analytical expression of the control law . In consequence, the performance index defined for the closed loop behavior of the system cannot be related explicitly with the tuning parameters of the controller.…”

Section: Introductionmentioning

confidence: 99%

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An Algorithm for Tuning NMPC Controllers with Application to Chemical Processes

Santamaria

Gómez

2016

Ind. Eng. Chem. Res.

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The advantages of linear/nonlinear model predictive control (N)MPC for dealing with the multiple input multiple output problem, for performing optimization and for handling constraints are well-known and because of that it has been applied widely in the chemical industry. However, there is a recurrent problem for this kind of controllers and it is how to define the best tuning parameters to achieve a good closed-loop response. This is an open question for research even when the common practice is to define the (N)MPC tuning parameters by trial and error or by the expertise of the control engineer. There are some works that propose a systematic definition of the (N)MPC tuning parameters, but most of them are restricted to certain prediction models or do not define all the tuning parameters. This papers presents a general tuning algorithm for (N)MPC controllers that allows the systematic definition of all the tuning parameter and it is not restricted to a specific prediction model or to a problem formulation. It is based on multiobjective optimization for the definition of the objective function weights and on the optimization of a closed loop performance index for the definition of the horizon lengths. Three cases of chemical processes are considered in detail to illustrate the application and advantages of the proposed (N)MPC tuning algorithm.

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Section: Introductionmentioning

confidence: 99%

“…Moreover, the OCP is usually nonlinear and is subject to inequality constraints, which make it impossible to find an analytical expression of the control law. 3 In consequence, the performance index defined for the closed loop behavior of the system cannot be related explicitly with the tuning parameters of the controller.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Tuning NMPC Controllers with Application to Chemical Processes

Santamaria

Gómez

2016

Ind. Eng. Chem. Res.

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“…Therefore, the final control law for throttle angle is determined by (12) when the optimal u * (t) is obtained. In this paper, the online optimization of MPC will be achieved by the C/GMRES algorithm, which can solve the linear equation instead of the Riccati differential equation in real time by combining the continuation method and the generalized minimal residual (GMRES) method [11].…”

Section: Mpc Schemementioning

confidence: 99%

“…In this context, a convenient tuning approach for MPC performance has the important practical significance with consideration of the time and cost. Recently, a tuning method for MPC performance from the viewpoint of the inverse linear quadratic (ILQ) regulator design is presented in [12] and [13], which becomes a inspiration for this paper. The remarkable advantage of the given method is that it can provide a single tuning parameter for the selection of the MPC weightings while guarantee the optimality of the feedback control system.…”

Section: Introductionmentioning

confidence: 99%

MPC-Based Speed Tracking Control Design for Spark-Ignition Engines

Kang

Tahir

Shen

et al. 2015

SICE Journal of Control, Measurement, and System Integration

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: This paper proposes an engine speed tracking controller based on the MPC scheme and investigates a tuning method for the practical control performance from the viewpoint of the ILQ design. First, the nonlinear system dynamics based on the engine mean-value model is transformed to the linear tracking error dynamics by the feedback linearization method. Then the MPC speed tracking controller is designed based on the derived tracking error dynamical model. To achieve the convenient adjustment of the control performance, the quadratic weightings in MPC performance index are calculated with respect to a single tuning parameter by means of the inverse optimality conditions of the LQ optimal problem. Moreover, an adaptive compensator is introduced to guarantee the control precision even if the predictive model is not very accurate. Experiments are conducted in the 3.5L-V6 gasoline engine test bed, the results demonstrate the good control effects for speed tracking by the proposed MPC controller and also confirm the validity of the proposed tuning approach.

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