Data driven nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions

Diesel engines' reliable and durable structures, high torque generation capabilities at low speeds, and fuel consumption efficiencies make them irreplaceable for heavy-duty vehicles in the market. However, ine ciencies in the combustion process result in the release of emissions to the environment. In addition to the restrictive international regulations for emissions, the competitive demands for more powerful engines and increasing fuel prices obligate heavy-duty engine and vehicle manufacturers to seek for solutions to reduce the emissions while meeting the performance requirements. In line with these objectives, remarkable progress has been made in modern diesel engine systems such as air handling, fuel injection, combustion, and after-treatment. However, such systems utilize quite sophisticated equipment with a large number of calibratable parameters that increases the experimentation time and effort to find the optimal operating points. Therefore, a dynamic model-based transient calibration is required for an e cient combustion optimization which obeys the emission limits, and meets the desired power and efficiency requirements. This thesis is about developing optimizationoriented high delity nonlinear dynamic models for predicting heavy-duty diesel engine torque and combustion emissions. Contributions of the thesis are: (i) A new design of experiments is proposed where air-path and fuel-path input channels are excited by chirp signals with varying frequency pro les in terms of the number and directions of the sweeps. The proposed approach is a strong alternative to the steady-state experiment based approaches to reduce the testing time considerably and improve the modeling accuracy in both steady-state and transient conditions. (ii) A nonlinear nite impulse response (NFIR) model is developed to predict indicated torque by including the estimations of friction, pumping and inertia torques in addition to the torque measured from the engine dynamometer. (iii) Two different nonlinear autoregressive with exogenous input (NARX) models are proposed to predict NOx emissions. In the first structure, input regressor set for the nonlinear part of the model is reduced by an orthogonal least square (OLS) algorithm to increase the robustness and decrease the sensitivity to parameter changes, and linear output feedback is employed. In the second structure, only the previous output is used as the output regressor in the model due to the stability considerations. (iv) An analysis of model sensitivities to parameter changes is conducted and an easy-tointerpret map is introduced to select the best modeling parameters with limited testing time in powertrain development. (v) Soot (particulated matter) emission is predicted using LSTM type networks which provide more accurate and smoother predictions than NARX models. Experimental results obtained from the engine dynamometer tests show the e ectiveness of the proposed models in terms of prediction accuracies in both NEDC (New European Driving Cycle) and WHTC (World Harmonized Transient Cycle) cycle

Differential Dynamic Programming with Nonlinear Safety Constraints Under System Uncertainties

Safe operation of systems such as robots requires them to plan and execute trajectories subject to safety constraints. When those systems are subject to uncertainties in their dynamics, ensuring that the constraints are not violated is challenging. In this paper, we propose a safe trajectory optimization and control approach (Safe-CDDP) for systems under additive uncertainties and non-linear safety constraints based on constrained differential dynamic programming (DDP). The safety of the robot during its motion is formulated as chance-constraints with user-chosen probabilities of constraint satisfaction. The chance constraints are transformed into deterministic ones in DDP formulation by constraint tightening. To avoid over conservatism during constraint tightening, linear control gains of the feedback policy derived from the constrained DDP are used in the approximation of closed-loop uncertainty propagation in prediction. The proposed algorithm is empirically demonstrated on three different robot dynamics with up to 12 states and the results show the applicability of the approach for safety-aware applications.Comment: 7 pages, 4 figures, submitted to ICRA 202

Robust hovering control of a quadrotor using acceleration feedback

This paper presents a novel acceleration feedback control method for robust hovering of a quadrotor subject to aerodynamic disturbances. An acceleration based disturbance observer (ABDOB) is designed to reject disturbances acting on the positional dynamics of the quadrotor. In order to provide high stiffness against disturbances acting on the attitude dynamics, a nested position, velocity and inner acceleration feedback control structure that utilizes PID and PI type controllers is developed. To obtain reliable angular acceleration information, a cascaded estimation technique based on an extended Kalman filter (EKF) and a classical Kalman filter (KF) is proposed. EKF estimates the Euler angles and gyro biases by fusing the data from gyroscope, accelerometer and magnetometer. Compensated gyro data are then fed into a Kalman filter whose process model is derived from Taylor series expansion of angular velocities and accelerations where angular jerks are considered as stochastic inputs. The well-known kinematic relation between Euler angular rates and angular velocities is employed to estimate reliable Euler accelerations. Estimated Euler angles, rates and accelerations are then used as feedback signals in the nested attitude control structure. Performance of the proposed method is assessed by a high fidelity simulation model where uncertainties in the sensor measurements, e.g. sensor bias and noise, are also considered. Developed controllers that utilize estimated acceleration feedback provide extremely robust hovering results when the quadrotor is subject to wind gusts generated by Dryden wind model. Simulation results show that utilization of acceleration feedback in hovering control significantly reduces the deviations in the x-y position of the quadrotor

Trajectory Optimization on Matrix Lie Groups with Differential Dynamic Programming and Nonlinear Constraints

Matrix Lie groups are an important class of manifolds commonly used in control and robotics, and the optimization of control policies on these manifolds is a fundamental problem. In this work, we propose a novel approach for trajectory optimization on matrix Lie groups using an augmented Lagrangian-based constrained discrete Differential Dynamic Programming. The method involves lifting the optimization problem to the Lie algebra in the backward pass and retracting back to the manifold in the forward pass. In contrast to previous approaches which only addressed constraint handling for specific classes of matrix Lie groups, the proposed method provides a general approach for nonlinear constraint handling for generic matrix Lie groups. We also demonstrate the effectiveness of the method in handling external disturbances through its application as a Lie-algebraic feedback control policy on SE(3). Experiments show that the approach is able to effectively handle configuration, velocity and input constraints and maintain stability in the presence of external disturbances.Comment: 10 pages, 7 figure

Learning Based High-Level Decision Making for Abortable Overtaking in Autonomous Vehicles

Autonomous vehicles are a growing technology that aims to enhance safety, accessibility, efficiency, and convenience through autonomous maneuvers ranging from lane change to overtaking. Overtaking is one of the most challenging maneuvers for autonomous vehicles, and current techniques for autonomous overtaking are limited to simple situations. This paper studies how to increase safety in autonomous overtaking by allowing the maneuver to be aborted. We propose a decision-making process based on a deep Q-Network to determine if and when the overtaking maneuver needs to be aborted. The proposed algorithm is empirically evaluated in simulation with varying traffic situations, indicating that the proposed method improves safety during overtaking maneuvers. Furthermore, the approach is demonstrated in real-world experiments using the autonomous shuttle iseAuto.Comment: 11 pages, 16 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Predicting NOx emissions in diesel engines via sigmoid NARX models using a new experiment design for combustion identification

Diesel engines are still widely used in heavy-duty engine industry because of their high energy conversion efficiency. In recent decades, governmental institutions limit the maximum acceptable hazardous emissions of diesel engines by stringent international regulations, which enforces engine manufacturers to find a solution for reducing the emissions while keeping the power requirements. A reliable model of the diesel engine combustion process can be quite useful to search for the best engine operating conditions. In this study, nonlinear modeling of a heavy-duty diesel engine NOx emission formation is presented. As a new experiment design, air-path and fuel-path input channels were excited by chirp signals where the frequency profile of each channel is different in terms of the number and the direction of the sweeps. This method is proposed as an alternative to the steady-state experiment design based modeling approach to substantially reduce testing time and improve modeling accuracy in transient operating conditions. Sigmoid based nonlinear autoregressive with exogenous input (NARX) model is employed to predict NOx emissions with given input set under both steady-state and transient cycles. Models for different values of parameters are generated to analyze the sensitivity to parameter changes and a parameter selection method using an easy-to-interpret map is proposed to find the best modeling parameters. Experimental results show that the steady-state and the transient validation accuracies for the majority of the obtained models are higher than 80% and 70%, respectively

Estimating soot emission in diesel engines using gated recurrent unit networks

In this paper, a new data-driven modeling of a diesel engine soot emission formation using gated recurrent unit (GRU) networks is proposed. Different from the traditional time series prediction methods such as nonlinear autoregressive with exogenous input (NARX) approach, GRU structure does not require the determination of the pure time delay between the inputs and the output, and the number of regressors does not have to be chosen beforehand. Gates in a GRU network enable to capture such dependencies on the past input values without any prior knowledge. As a design of experiment, 30 different points in engine speed - injected fuel quantity plane are determined and the rest of the input channels, i.e., rail pressure, main start of injection, equivalence ratio, and intake oxygen concentration are excited with chirp signals in the intended regions of operation. Experimental results show that the prediction performances of GRU based soot models are quite satisfactory with 77% training and 57% validation fit accuracies and normalized root mean square error (NRMSE) values are less than 0.038 and 0.069, respectively. GRU soot models surpass the traditional NARX based soot models in both steady-state and transient cycles

Robust trajectory control of an unmanned aerial vehicle using acceleration feedback

In this work, acceleration feedback is utilised in a hierarchical control structure for robust trajectory control of a quadrotor helicopter subject to external disturbances where reference attitude angles are determined through a nonlinear optimisation algorithm. Furthermore, an acceleration-based disturbance observer (AbDOB) is designed to estimate disturbances acting on the positional dynamics of the quadrotor. For the attitude control, nested position, velocity, and inner acceleration feedback loops consisting of PID and PI type controllers are developed to provide high stiffness against external disturbances. Reliable angular acceleration is estimated through a cascaded filter structure. Simulation results show that the proposed controllers provide robust trajectory tracking performance when the aerial vehicle is subject to wind gusts generated by the Dryden wind model along with the uncertainties and measurement noise. Results also demonstrate that the reference attitude angles calculated through nonlinear optimisation are smooth and within the desired bounds