Control Oriented Modeling and Nonlinear Model Predictive Control of Advanced SI Engine System

2010 ◽  
Author(s):  
Tae-Kyung Lee ◽  
Zoran S. Filipi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Combustion Process ◽  
Nonlinear Model Predictive Control ◽  
Combustion Model ◽  
Ic Engine ◽  
Actuator Dynamics ◽  
Engine Simulation ◽  
Prediction Horizon

Control oriented model (COM) using crank-angle resolved flame propagation simulation and nonlinear model predictive control (NMPC) methodology for the purpose of transient control of HDOF engines are proposed in this paper. The nonlinear nature of the combustion process has been a challenge in building a reliable COM and engine simulation. Artificial neural networks (ANNs) are subsequently trained on the data generated with a quasi-D combustion model to create fast surrogate combustion models. System dynamics are augmented by manifold and actuator dynamics models. Then, NMPC for an internal combustion (IC) engine with a dual-independent variable valve timing (VVT) system is designed to achieve fast torque responses, to eliminate exhaust emissions penalty, and to track the optimal actuator response closely. The NMPC significantly improves engine dynamics and minimizes excursions of in-cylinder variables under highly transient operation. Dead-beat like control is achieved with selected prediction horizon and control horizon in the NMPC.

scholarly journals Nonlinear Model Predictive Control for the Swing-Up of a Rotary Inverted Pendulum

2004 ◽  
Vol 126 (3) ◽  
pp. 666-673 ◽  
Author(s):  
Sooyong Jung ◽  
John T. Wen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Inverted Pendulum ◽  
Optimal Solution ◽  
Nonlinear Model Predictive Control ◽  
Prediction Horizon ◽  
Experimental Implementation ◽  
Gradient Based ◽  
Rotary Inverted Pendulum

This paper presents the experimental implementation of a gradient-based nonlinear model predictive control (NMPC) algorithm to the swing-up control of a rotary inverted pendulum. The key attribute of the NMPC algorithm used here is that it only seeks to reduce the error at the end of the prediction horizon rather than finding the optimal solution. This reduces the computation load and allows real-time implementation. We discuss the implementation strategy and experimental results. In addition to NMPC based swing-up control, we also present results from a gradient based iterative learning control, which is the basis our NMPC algorithm.

Nonlinear Model Predictive Control Applied to a Generic Ocean-Wave Energy Extractor1

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Nathan Tom ◽  
Ronald W. Yeung
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Wave Energy ◽  
Nonlinear Model Predictive Control ◽  
Irregular Waves ◽  
Passive System ◽  
Prediction Horizon ◽  
Near Resonance ◽  
The Time Domain

This paper evaluates the theoretical application of nonlinear model predictive control (NMPC) to a model-scale point absorber for wave energy conversion. The NMPC strategy will be evaluated against a passive system, which utilizes no controller, using a performance metric based on the absorbed energy. The NMPC strategy was setup as a nonlinear optimization problem utilizing the interior point optimizer (IPOPT) package to obtain a time-varying optimal generator damping from the power-take-off (PTO) unit. This formulation is different from previous investigations in model predictive control, as the current methodology only allows the PTO unit to behave as a generator, thereby unable to return energy to the waves. Each strategy was simulated in the time domain for regular and irregular waves, the latter taken from a modified Pierson–Moskowitz spectrum. In regular waves, the performance advantages over a passive system appear at frequencies near resonance while at the lower and higher frequencies they become nearly equivalent. For irregular waves, the NMPC strategy leads to greater energy absorption than the passive system, though strongly dependent on the prediction horizon. It was found that the ideal NMPC strategy required a generator that could be turned on and off instantaneously, leading to sequences where the generator can be inactive for up to 50% of the wave period.

Nonlinear Parameters and State Estimation for Adaptive Nonlinear Model Predictive Control Design

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Hichem Salhi ◽  
Faouzi Bouani
Keyword(s):  
Model Predictive Control ◽  
State Estimation ◽  
Predictive Control ◽  
Nonlinear Model ◽  
State Space Model ◽  
Nonlinear Model Predictive Control ◽  
Control Law ◽  
Unknown Parameters ◽  
Prediction Horizon ◽  
Predictive Controller

This paper deals with an adaptive nonlinear model predictive control (NMPC) based estimator in cases of mismatch modeling, presence of perturbations and/or parameter variations. Thus, we propose an adaptive nonlinear predictive controller based on the second-order divided difference filter (DDF) for multivariable systems. The controller uses a nonlinear state-space model for parameters and state estimation and for the control law synthesis. Two nonlinear optimization layers are included in the proposed algorithm. The first optimization problem is based on the output error (OE) model with a tuning factor, and it is dedicated to minimize the error between the model and the system at each sample time by estimating unknown parameters when assuming that all system states are available. The second optimization layer is used by the centralized nonlinear predictive controller to generate the control law which minimizes the error between future setpoints and future outputs along the prediction horizon. The proposed algorithm leads to a good tracking performance with an offset-free output and an effectiveness in perturbation attenuation. Practical results on a real setup show the reliability of the proposed approach.

scholarly journals Experimental Validation of a Guaranteed Nonlinear Model Predictive Control

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 248
Author(s):  
Mohamed Fnadi ◽  
Julien Alexandre dit Sandretto
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Interval Analysis ◽  
Optimization Procedure ◽  
The State ◽  
Nonlinear Model Predictive Control ◽  
Control Input ◽  
Time Step ◽  
Prediction Horizon

This paper combines the interval analysis tools with the nonlinear model predictive control (NMPC). The NMPC strategy is formulated based on an uncertain dynamic model expressed as nonlinear ordinary differential equations (ODEs). All the dynamic parameters are identified in a guaranteed way considering the various uncertainties on the embedded sensors and the system’s design. The NMPC problem is solved at each time step using validated simulation and interval analysis methods to compute the optimal and safe control inputs over a finite prediction horizon. This approach considers several constraints which are crucial for the system’s safety and stability, namely the state and the control limits. The proposed controller consists of two steps: filtering and branching procedures enabling to find the input intervals that fulfill the state constraints and ensure the convergence to the reference set. Then, the optimization procedure allows for computing the optimal and punctual control input that must be sent to the system’s actuators for the pendulum stabilization. The validated NMPC capabilities are illustrated through several simulations under the DynIbex library and experiments using an inverted pendulum.

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