A discrete-time sliding mode formulation for automotive cold-start emission control

2014 American Control Conference

Edelberg

Hedrick

2014

Self Cite

Automotive powertrain control strategies are a key component of the software design process for vehicle systems. During implementation of control algorithms on real systems, errors often arise that prove costly if they are not detected until the Verification & Validation process. Thus, it is advantageous to mitigate potential uncertainties early on in the design. Specifically, in this paper, we present the control algorithm derivation with the direct incorporation of uncertainty from the system model. We incorporate an unknown parameter representing significant model uncertainty and design to that scenario using a nonlinear, discrete-time sliding control strategy. Additionally, we incorporate an adaptation law to estimate and update the unknown parameter online in order to decrease the control actuation effort. A Simulink representation of the cold start engine emissions process is used as a case study on which the control and adaptation strategy is demonstrated. Simulation results demonstrate that this adaptive formulation yields superior performance over its non-adaptive counterpart by successfully estimating the unknown model parameters and driving tracking error to zero in steady-state.

Section: B Adaptive Control Lawsmentioning

confidence: 99%

Section: A Description Of Engine Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Discrete adaptive sliding control of automotive powertrains

2014 American Control Conference

Edelberg

Hedrick

2014

Self Cite

Bridging the gap between designed and implemented controllers via adaptive robust discrete sliding mode control

Amini

Shahbakhti

Control Engineering Practice

et al. 2017

Bridging the gap between designed and implemented model-based controllers is a major challenge in the design cycle of industrial controllers. This gap is created due to (i) digital implementation of controller software that introduces sampling and quantization uncertainties, and (ii) uncertainties in the modeled plant's dynamics. In this paper, a new adaptive and robust modelbased control approach is developed based on a nonlinear discrete sliding mode controller (DSMC) formulation to mitigate implementation imprecisions and model uncertainties, that consequently minimizes the gap between designed and implemented controllers. The new control approach incorporates the predicted values of the implementation uncertainties into the controller structure. Moreover, a generic adaptation mechanism will be derived to remove the errors in the nonlinear modeled dynamics. The proposed control approach is illustrated on a nonlinear automotive engine control problem. The designed DSMC is tested in real-time in a processor-in-the-loop (PIL) setup using an actual electronic control unit (ECU). The verification test results show that the proposed controller design, under ADC and model uncertainties, can improve the tracking performance up to 60% compared to a conventional controller design.

Adaptive engine cold start emission control

2015 American Control Conference (ACC)

Neti

et al. 2015

Self Cite

Reduction of hydrocarbon emissions during the engine cold start process is a major design and control goal in the automotive industry in recent years, with considerable impact on not only the vehicle's fuel economy, but the environment as well. The key to producing the most emission efficient powertrain system is the ability to drive the engine variables to operate under certain behavioral parameters, that is, to drive the engine states to follow ideal desired trajectories. Therefore, the control target of driving down tracking error to produce ideal engine behavior is an important consideration in the control design process. Due to the highly nonlinear and transient nature of the engine cold start process, the choice was made in this work to employ a scalar sliding control technique. Additionally, in order to mitigate possible model uncertainty in real time, an adaptation update algorithm was incorporated. The combined algorithm was implemented on an engine test cell and experimentally validated. The work was inspired by the Verification & Validation procedure used in standard industry practice to reduce errors and uncertainties early on in the control design phase so as to produce desired engine behavior as soon as possible.