Diesel Engine Air Path System Research Articles

Adaptive integral sliding mode fault‐tolerant control for diesel engine air path system via disturbance observer

AbstractThis paper proposes a fault‐tolerant control strategy for the air path system of a turbocharged diesel engine, considering the simultaneous presence of matched and mismatched external disturbances, additive and loss of effectiveness fault modes in the EGR and VGT subsystems. Firstly, a disturbance observer based on theory is designed for estimating additive faults and external disturbances, and the upper bound of observation error is obtained. Based on the observation information, an integral sliding mode surface is designed, and the effectiveness of the sliding mode surface is analyzed. For the loss of effectiveness faults in the actuators, a novel adaptive update sliding mode controller is designed. The proposed control algorithm can get the fault information of the system and achieve fault‐tolerant control of through controller reconstruction. Ultimately, the effectiveness of the proposed method is validated through simulations and comparative analysis.

Modeling and Control of Diesel Engine Emissions using Multi-layer Neural Networks and Economic Model Predictive Control

This paper presents the results of developing a multi-layer Neural Network (NN) to represent diesel engine emissions and integrating this NN into control design. Firstly, a NN is trained and validated to simultaneously predict oxides of nitrogen (NOx) and Soot using both transient and steady-state data. Based on the input-output correlation analysis, inputs to NN with the highest influence on the emissions are selected while keeping the NN structure simple. Secondly, a co-simulation framework is implemented to integrate the NN emissions model with a model of a diesel engine airpath system built in GT-Power and used to identify a low-order linear parameter-varying (LPV) model for emissions prediction. Finally, an economic supervisory model predictive controller (MPC) is developed using the LPV emissions model to adjust setpoints to an inner-loop airpath tracking MPC. Simulation results are reported illustrating the capability of the resulting controller to reduce NOx, meet the target Soot limit, and track the adjusted intake manifold pressure and exhaust gas recirculation (EGR) rate targets.

A Multi-objective Optimization Algorithm for Air-path System of Diesel Engines

Abstract Modern engine is a typical multi-objective control system. This paper proposed a multi-objective Bayesian optimization strategy to deal with the performance optimization for diesel engines. Since the objective functions of diesel engines are complicated and computationally expensive, Gaussian processes(GPs) are constructed by using the data collected from the diesel engine to approximate the real objective functions. Non-dominated sorting genetic algorithm II(NSGAII) leverages the Gaussian process to generate the Pareto-optimal solutions. The Gaussian process will be updated iteratively by Bayesian posterior information, which increases the reliability of the models. The acquisition function Expected Hyper Volume Improvement(EHVI), which can balance the trade-off between exploration and exploitation throughout the optimization process, is used to select the solutions for real computationally expensive multi-objective evaluation. The proposed algorithm is applied on a diesel engine, which shows its reliability and high efficiency. The metrics hypervolume(HV) and the control results demonstrate that the proposed algorithm has outstanding effects for performance optimization of diesel engine airpath.

Fault-tolerant control for turbocharged diesel engine air path via disturbance observer

ABSTRACT This paper investigates fault-tolerant control algorithm for diesel engine air path system with consideration of system parametric uncertainties, external disturbances and actuator faults. Initially, a finite-time disturbance observer is developed for estimating the synchronised uncertainties including parametric uncertainties and external disturbances. Based on the reconstructed information from the observer, a fault-tolerant control law is designed by using sliding mode control incorporated adaptive method. Furthermore, to attenuate the chattering problem caused by conventional sliding mode control method, the hyperbolic tangent function is utilised. A novel sliding variable is employed for improving the performance of the modified chattering-free control approach. The proposed fault-tolerant control algorithm is feasible for implementing and unnecessary caculations can be avoided, because the synchronised uncertainties and the actuator faults are tackled separately. Besides, the observer does not require the priori knowledge of the upper bounds of the synchronised uncertainties. Finally, simulation results and comparison are carried out and analysed for illustrating the effectiveness and validness of the proposed control schemes.

Scheduling Parameter Reduction of Diesel Engine Air Path LPV Model by PCA and Autoencoder-Based Method

This study presents a method to reduce the number of scheduling variables in a linear parameter-varying (LPV) model of a diesel engine air path system. The reduction of these scheduling variables is very important because it exponentially decreases the computational complexity for the gain-scheduled LPV controller synthesis. Principal component analysis (PCA) and autoencoder (AE) based neural networks are applied to the LPV diesel engine’s air path model, and the relationship between the accuracy of the reproduced scheduling variables and the number of the reduced scheduling parameters is evaluated via conduction of numerical simulations.

Diesel engine air path control based on neural approximation of nonlinear MPC

This paper deals with a control design problem for a diesel engine air path system that has strong nonlinearity and requires multi-input and multi-output control to satisfy requirements and constraints. We focus on a neural network based approximation of nonlinear model predictive control (NMPC) for high-speed computation. Most neural approximation methods are verified only through simulation; further, the influence of approximation on the closed-loop performance has been not sufficiently discussed. In this study, we discuss this influence, and propose a new method to improve stability against degradation due to an approximation error. The control system is assembled using a neural network based controller, obtained by the proposed method, and an unscented Kalman filter. This system is verified both numerically and experimentally; the results demonstrate the capability of the proposed method to track the boost pressure, EGR rate, and pumping loss according to the reference values, and satisfy the constraints of compressor surge and choke. The high computation speed that can be achieved using a standard on-board ECU is also demonstrated using the approximated controller.

Machine Learning Based Real-time MPC Design for a Diesel Engine Air Path System

Machine Learning Based Real-time MPC Design for a Diesel Engine Air Path System

Model Predictive Control Of Diesel Engine Air Path With Actuator Delays

Existing results show that Model Predictive Control (MPC) can satify the desired settling time, zero steady-state error, and overshoot criteria for the MIMO control of the Diesel Engine Air Path (DEAP) system. However, the effect of actuator delay that considerably affects system performance, is not addressed sufficiently In this study, MPC of DEAP is extended with a delay term added to the actuators Exhaust Gas Recirculation valve and Variable Geometry Turbocharger valve on the plant model. A linear state-space model of the plant is obtained by using system identification techniques and the states of the identified model are extended due to delay term. It is shown that MPC performs better when the delay is taken into account in the algorithm.

A Nonlinear Feedforward Controller Design Taking Account of Dynamics of Turbocharger and Manifolds for Diesel Engine Air-Path System

Abstract In this study, we propose a design method of a model-based nonlinear feedforward controller for a diesel engine air-path system. First, a physical air-path model of the diesel engine was developed. Then, a feedforward controller was developed based on the inverse characteristics of the developed physical model. To improve the transient responses of the boost pressure and EGR ratio, the response delay of the turbocharger and manifolds dynamics were considered while designing the feedforward controller. The effectiveness of the proposed method was evaluated by conducting experiments using a real diesel engine. The experimental results indicated a good tracking performance for both the boost pressure and EGR ratio.

Modeling and Control of a Diesel Engine With Regenerative Hydraulic-Assisted Turbocharger

Diesel engines are of great challenges due to stringent emission and fuel economy requirements. Compared with the conventional turbocharger system, regenerative assisted system provides additional degrees-of-freedom for turbocharger speed control. Hence, it significantly improves control capability for exhaust-gas-recirculation (EGR) and boost pressure. This paper focuses on modeling and control of a diesel engine air-path system equipped with an EGR subsystem and a variable geometry turbocharger (VGT) coupled with a regenerative hydraulic-assisted turbocharger (RHAT). The challenges lie in the inherent coupling among EGR, turbocharger performance, and high nonlinearity of the engine air-path system. A control-oriented nonlinear RHAT system model is developed; and a linear quadratic (LQ) control design approach is proposed in this paper to regulate the EGR mass flow rate and boost pressure simultaneously and the resulting closed-loop system performance can be tuned by properly selecting the LQ control weighting matrices. Multiple LQ controllers with integral action are designed based on the linearized system models over a gridded engine operational map and the final gain-scheduling controller for a given engine operational condition is obtained by interpreting the neighboring LQ controllers. The gain-scheduling LQ controllers for both traditional VGT-EGR and VGT-EGR-RHAT systems are compared with the in-house baseline controller, consisting of two single-input and single-output (SISO) controllers, against the nonlinear plant. The simulation results show that the designed multi-input and multi-output LQ gain-scheduling controller is able to manage the performance trade-offs between EGR mass flow and boost pressure tracking. With the additional assisted and regenerative power available on the turbocharger shaft for the RHAT system, engine transient boost pressure performance can be significantly improved without compromising the EGR tracking performance, compared with the baseline control.

Two-Degree-of-Freedom H∞ Control of Diesel Engine Air Path System with Nonlinear Feedforward Controller

In this research, we developed a two-degree-of-freedom control system for a Diesel engine air path system. First, a physical air path model of the Diesel engine was developed. Then, a feedforward controller was designed based on the inverse of the developed physical model, thus reducing the computational effort in real-time calculation. The feedback controller was designed by using an H∞ control theory by regarding the plant nonlinearities and the response delay of the actuators as uncertainties to the nominal model. The feedforward and feedback controllers were implemented as a two-degree-of-freedom control scheme. Further, an anti-windup scheme was introduced in the feedback controller to reduce the overshoot in the intake manifold pressure response. The effectiveness of the proposed method was evaluated by conducting not only simulations using the nonlinear physical model, but also experiments using a real Diesel engine.

Real-time MPC Design Based on Machine Learning for a Diesel Engine Air Path System

This study investigated a control design method based on machine learning to achieve non-linear model predictive control (MPC) at a low computational load. In addition, we describe examples of the application of this method to a diesel engine air path system. The solution to the optimal control problem determined at each point in time by MPC depends on several parameters at that time. Thus, if the relationship between the solution and the parameters could be approximated in advance using machine learning, solving this problem online would become unnecessary, and the control computation time could be reduced. We designed a controller that operates the valves of the air path system using this method and used a simulation to verify that this resulted in a favorable tracking performance of the target values. The computation time of the approximated MPC controller was 0.022 ms.

An Application of C/GMRES Model Predictive Control to a Diesel Engine Air Path System

This paper considers an application of a C/GMRES-based model predictive control (MPC) method to a diesel engine air path system. The plant model is derived based on the physical first principle to explicitly take account of plant nonlinearities. Since the plant has unmeasurable states, we employ an extended Kalman filter to estimate them. Then we design a C/GMRES-MPC algorithm and apply it to a real engine. We demonstrate the effectiveness of the present method by showing experimental results.

Chattering-Free Sliding Mode Control for Diesel Engine Air Path System with Actuator Faults

In this paper, a chattering-free sliding mode control law is constructed for the diesel engine air path system with consideration of partial loss of actuator effectiveness and additive fault. Firstly, sliding mode method and adaptive technique are employed to develop a fault-tolerant controller. With the adaptation update law, there is no requirement of the priori knowledge of the upper bounds of the actuator faults. Then, to attenuate the chattering behavior and improve system performance, the boundary layer is introduced to modify the sliding mode control method. Rigorous theoretical analysis is presented based on Lyapunov stability theory, which demonstrates that the system trajectories converge to a small neighborhood around the origin. Finally, numerical simulation results are carried out to show the effectiveness and validness of the proposed algorithms.

An Application of C/GMRES Model Predictive Control to a Diesel Engine Air Path System

Abstract This paper considers an application of a C/GMRES-based model predictive control (MPC) method to a diesel engine air path system. The plant model is derived based on the physical first principle to explicitly take account of plant nonlinearities. Since the plant has unmeasurable states, we employ an extended Kalman filter to estimate them. Then we design a C/GMRES-MPC algorithm and apply it to a real engine. We demonstrate the effectiveness of the present method by showing experimental results.

Pressure model based coordinated control of VGT and dual-loop EGR in a diesel engine air-path system

This paper describes a pressure-model-based coordinated control method of a variable geometry turbine (VGT) and dual-loop exhaust gas recirculation (EGR) in a diesel engine air-path system. Conventionally, air fraction or burnt gas fraction states are controlled for the control of dual-loop EGR systems, but fraction control is not practical since sensors for fractions are not available on production engines. In fact, there is still great controversy over how best to select control outputs for dual-loop EGR systems. In this paper, pressure and mass flow states are chosen as control outputs without fraction states considering the availability and reliability of sensors. A coordinated controller based on the simple control-oriented model is designed with practical aspects, which is applicable for simultaneous operations of high pressure (HP) EGR, low pressure (LP) EGR, and VGT. In addition, the controller adopts the method of input-output linearization using back-stepping to solve the chronic problems of conventional pressure-based controllers such as coupling effects between operations of HP EGR, and VGT. The control performance is verified by simulation based on the proven GT-POWER model of a heavy-duty 6000cc diesel engine air-path.

Fuzzy fault tolerant predictive control for a diesel engine air path

This paper proposes a Fuzzy Fault Tolerant Predictive Control (FFTPC) with integral action method for a class of nonlinear systems. The Takagi-Sugeno (T-S) fuzzy approach is introduced as a modelling technique in order to consider the active control methods adapted to linear models. The proposed control strategy is based on a combination between Parallel Distributed Compensation (PDC) control law and Model Predictive Control (MPC) where the T-S fuzzy aspect uses the Unmeasurable Premise Variables (UPV). A T-S fuzzy observer provides an L2 norm estimation of system state vector and faults. The controller and observer gains are obtained by solving Linear Matrix Inequalities (LMIs) derived from the Lyapunov theory. The validity of the proposed Fault Tolerant Control (FTC) strategy is illustrated through an application to a Diesel Engine Air Path (DEAP) system.

An Application of Reference Governor to a Diesel Engine Air Path System: Implementation of a Multi-Variable Reference Modification Algorithm

This paper considers an application of a reference governor (RG) to the air path system of an automotive diesel engine. The air path system can be represented as a multi-input-multi-output (MIMO) nonlinear system which has hardware interaction between fresh air and exhaust gas paths resulting from a turbocharger and an exhaust gas recirculation (EGR). The system has several constraints which should be considered in control design, such as maximal boost pressure, turbine speed and EGR min-max constraints. In order to explicitly deal with the constraints, we employ an online RG to modify boost pressure and the EGR rate references so that their predicted values ensure the constraints. Using a prediction model obtained via system identification the RG algorithm is established based on a planar search combined with the gradient descent method and a bisectional search. An experimental result using a real engine is shown to demonstrate the effectiveness of the present method.

Sliding Mode Control of Diesel Engine Air-path System With Dual-loop EGR and VGT Based on the Reduced-order Model

This paper presents the design of a model-based controller for the diesel engine air-path system. The controller is implemented based on a reduced order model consisting of only pressure and power dynamics with practical concerns. To deal with the model uncertainties effectively, a sliding mode controller, which is robust to model uncertainties, is proposed for the air-path system. The control performance of the proposed control scheme is verified through simulation with the valid plant model of a 6,000cc heavy duty diesel engine.

An LPV Adaptive Observer for Updating a Map Applied to an MAF Sensor in a Diesel Engine.

In this paper, a new method for mass air flow (MAF) sensor error compensation and an online updating error map (or lookup table) due to installation and aging in a diesel engine is developed. Since the MAF sensor error is dependent on the engine operating point, the error model is represented as a two-dimensional (2D) map with two inputs, fuel mass injection quantity and engine speed. Meanwhile, the 2D map representing the MAF sensor error is described as a piecewise bilinear interpolation model, which can be written as a dot product between the regression vector and parameter vector using a membership function. With the combination of the 2D map regression model and the diesel engine air path system, an LPV adaptive observer with low computational load is designed to estimate states and parameters jointly. The convergence of the proposed algorithm is proven under the conditions of persistent excitation and given inequalities. The observer is validated against the simulation data from engine software enDYNA provided by Tesis. The results demonstrate that the operating point-dependent error of the MAF sensor can be approximated acceptably by the 2D map from the proposed method.