Model-based control for automotive applications

The number of distributed control systems in modern vehicles has increased exponentially over the past decades. Today’s performance improvements and innovations in the automotive industry are often resolved using embedded control systems. As a result, a modern vehicle can be regarded as a complex mechatronic system. However, control design for such systems, in practice, often comes down to time-consuming online tuning and calibration techniques, rather than a more systematic, model-based control design approach. The main goal of this thesis is to contribute to a corresponding paradigm shift, targeting the use of systematic, model-based control design approaches in practice. This implies the use of control-oriented modeling and the specification of corresponding performance requirements as a basis for the actual controller synthesis. Adopting a systematic, model-based control design approach, as opposed to pragmatic, online tuning and calibration techniques, is a prerequisite for the application of state-of-the-art controller synthesis methods. These methods enable to achieve guarantees regarding robustness, performance, stability, and optimality of the synthesized controller. Furthermore, from a practical point-of-view, it forms a basis for the reduction of tuning and calibration effort via automated controller synthesis, and fulfilling increasingly stringent performance demands. To demonstrate these opportunities, case studies are defined and executed. In all cases, actual implementation is pursued using test vehicles and a hardware-in-the-loop setup. • Case I: Judder-induced oscillations in the driveline are resolved using a robustly stable drive-off controller. The controller prevents the need for re-tuning if the dynamics of the system change due to wear. A hardware-in-the-loop setup, including actual sensor and actuator dynamics, is used for experimental validation. • Case II: A solution for variations in the closed-loop behavior of cruise control functionality is proposed, explicitly taking into account large variations in both the gear ratio and the vehicle loading of heavy duty vehicles. Experimental validation is done on a heavy duty vehicle, a DAF XF105 with and without a fully loaded trailer. • Case III: A systematic approach for the design of an adaptive cruise control is proposed. The resulting parameterized design enables intuitive tuning directly related to comfort and safety of the driving behavior and significantly reduces tuning effort. The design is validated on an Audi S8, performing on-the-road experiments. • Case IV: The design of a cooperative adaptive cruise control is presented, focusing on the feasibility of implementation. Correspondingly, a necessary and sufficient condition for string stability is derived. The design is experimentally tested using two Citroën C4’s, improving traffic throughput with respect to standard adaptive cruise control functionality, while guaranteeing string stability of the traffic flow. The case studies consider representative automotive control problems, in the sense that typical challenges are addressed, being variable operating conditions and global performance qualifiers. Based on the case studies, a generic classification of automotive control problems is derived, distinguishing problems at i) a full-vehicle level, ii) an in-vehicle level, and iii) a component level. The classification facilitates a characterization of automotive control problems on the basis of the required modeling and the specification of corresponding performance requirements. Full-vehicle level functionality focuses on the specification of desired vehicle behavior for the vehicle as a whole. Typically, the required modeling is limited, whereas the translation of global performance qualifiers into control-oriented performance requirements can be difficult. In-vehicle level functionality focuses on actual control of the (complex) vehicle dynamics. The modeling and the specification of performance requirements are typically influenced by a wide variety of operating conditions. Furthermore, the case studies represent practical application examples that are specifically suitable to apply a specific set of state-of-the-art controller synthesis methods, being robust control, model predictive control, and gain scheduling or linear parameter varying control. The case studies show the applicability of these methods in practice. Nevertheless, the theoretical complexity of the methods typically translates into a high computational burden, while insight in the resulting controller decreases, complicating, for example, (online) fine-tuning of the controller. Accordingly, more efficient algorithms and dedicated tools are required to improve practical implementation of controller synthesis methods

Adaptive Model Predictive Control for Engine-Driven Ducted Fan Lift Systems using an Associated Linear Parameter Varying Model

Ducted fan lift systems (DFLSs) powered by two-stroke aviation piston engines present a challenging control problem due to their complex multivariable dynamics. Current controllers for these systems typically rely on proportional-integral algorithms combined with data tables, which rely on accurate models and are not adaptive to handle time-varying dynamics or system uncertainties. This paper proposes a novel adaptive model predictive control (AMPC) strategy with an associated linear parameter varying (LPV) model for controlling the engine-driven DFLS. This LPV model is derived from a global network model, which is trained off-line with data obtained from a general mean value engine model for two-stroke aviation engines. Different network models, including multi-layer perceptron, Elman, and radial basis function (RBF), are evaluated and compared in this study. The results demonstrate that the RBF model exhibits higher prediction accuracy and robustness in the DFLS application. Based on the trained RBF model, the proposed AMPC approach constructs an associated network that directly outputs the LPV model parameters as an adaptive, robust, and efficient prediction model. The efficiency of the proposed approach is demonstrated through numerical simulations of a vertical take-off thrust preparation process for the DFLS. The simulation results indicate that the proposed AMPC method can effectively control the DFLS thrust with a relative error below 3.5%

Nonlinear Optimal Generalized Predictive Functional Control applied to quasi-LPV model of automotive electronic throttle

A Nonlinear Optimal Generalized Predictive Functional Control algorithm is presented for the control of quasi linear parameter varying state-space systems. A scalar automotive electronic throttle body is simulated to demonstrate typical results. The controller structure is specified in a restricted structure form including a set of pre-specified linear transfer-functions and a vector of gains that are found to minimize a GPC cost-index. This approach enables a range of classical controller structures to be used in the feedback loop such as extended PI, PID or of a more general transfer-function form. The controller is introduced along with a dynamic cost-weighting tuning future. A simulation is used to validate the performance of the restricted structure controller for regulation and tracking problems assessed against automotive performance standards

Nonlinear predictive restricted structure control

This thesis defines new developments in predictive restricted structure control for industrial applications. It begins by describing the augmented system for both state-space and polynomial model descriptions. These descriptions can contain the plant model, the disturbance model, and any additional essential model subsystems. It then describes the predictive restricted structure control solution for both linear and nonlinear systems in state-space form. The solution utilizes the recent development in nonlinear predictive generalized minimum variance by adding a general operator subsystem that defines nonlinear system along with the linear or the linear parameter varying output subsystem. The next contribution is the polynomial predictive restricted structure control algorithm for polynomial linear parameter varying model that may result from nonlinear equations or experimental data-driven model identification. This algorithm utilizes the generalised predictive control method to approximate and control nonlinear systems in the linear parameter varying system inputoutput transfer operator matrices. The solution is simple in unconstrained and constrained optimization solutions and required a small computing capacity. Four examples have been chosen to test the algorithms for different nonlinear characteristics. In the first three examples, state-space versions of the algorithm for the linear, the quasi-linear parameter varying and the state-dependent were employed to control the quadruple tank process, electronic throttle body, and the continuous stirred tank reactors. In the last example, the polynomial linear parameter varying restricted structure controller is used to control automotive variable camshaft timing system.This thesis defines new developments in predictive restricted structure control for industrial applications. It begins by describing the augmented system for both state-space and polynomial model descriptions. These descriptions can contain the plant model, the disturbance model, and any additional essential model subsystems. It then describes the predictive restricted structure control solution for both linear and nonlinear systems in state-space form. The solution utilizes the recent development in nonlinear predictive generalized minimum variance by adding a general operator subsystem that defines nonlinear system along with the linear or the linear parameter varying output subsystem. The next contribution is the polynomial predictive restricted structure control algorithm for polynomial linear parameter varying model that may result from nonlinear equations or experimental data-driven model identification. This algorithm utilizes the generalised predictive control method to approximate and control nonlinear systems in the linear parameter varying system inputoutput transfer operator matrices. The solution is simple in unconstrained and constrained optimization solutions and required a small computing capacity. Four examples have been chosen to test the algorithms for different nonlinear characteristics. In the first three examples, state-space versions of the algorithm for the linear, the quasi-linear parameter varying and the state-dependent were employed to control the quadruple tank process, electronic throttle body, and the continuous stirred tank reactors. In the last example, the polynomial linear parameter varying restricted structure controller is used to control automotive variable camshaft timing system

Autonomous vehicle control using a kinematic Lyapunov-based technique with LQR-LMI tuning

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper presents the control of an autonomous vehicle using a Lyapunov-based technique with a LQR-LMI tuning. Using the kinematic model of the vehicle, a non-linear control strategy based on Lyapunov theory is proposed for solving the control problem of autonomous guidance. To optimally adjust the parameters of the Lyapunov controller, the closed loop system is reformulated in a linear parameter varying (LPV) form. Then, an optimization algorithm that solves the LQR-LMI problem is used to determine the controller parameters. Furthermore, the tuning process is complemented by adding a pole placement constraint that guarantees that the maximum achievable performance of the kinematic loop could be achieved by the dynamic loop. The obtained controller jointly with a trajectory generation module are in charge of the autonomous vehicle guidance. Finally, the paper illustrates the performance of the autonomous guidance system in a virtual reality environment (SYNTHIA) and in a real scenario achieving the proposed goal: to move autonomously from a starting point to a final point in a comfortable way.Peer ReviewedPostprint (author's final draft

Direct learning ofLPVcontrollers from data

In many control applications, it is attractive to describe nonlinear (NL) and time-varying (TV) plants by linear parametervarying (LPV) models and design controllers based on such representations to regulate the behaviour of the system. The LPV system class offers the representation of NL and TV phenomena as a linear dynamic relationship between input and output signals, which relationship is dependent on some measurable signals, e.g., operating conditions, often called as scheduling variables. For such models, powerful control synthesis tools are available, but the way how to systematically convert available first principles models to LPV descriptions of the plant, to efficiently identify LPV models for control from data and to understand how modeling errors affect the control performance are still subject of undergoing research. Therefore, it is attractive to synthesize the controller directly from data without the need of modeling the plant and addressing the underlying difficulties. Hence, in this paper, a novel data-driven synthesis scheme is proposed in a stochastic framework to provide a practically applicable solution for synthesizing LPV controllers directly from data. Both the cases of fixed order controller tuning and controller structure learning are discussed and two different design approaches are provided. The effectiveness of the proposed methods is also illustrated by means of an academic example and a real application based simulation case study

Adaptive Robust Vehicle Motion Control for Future Over-Actuated Vehicles

International audienceMany challenges still need to be overcome in the context of autonomous vehicles. These vehicles would be over-actuated and are expected to perform coupled maneuvers. In this paper, we first discuss the development of a global coupled vehicle model, and then we outline the control strategy that we believe should be applied in the context of over-actuated vehicles. A gain-scheduled H ∞ controller and an optimization-based Control Allocation algorithms are proposed. High-fidelity co-simulation results show the efficiency of the proposed control logic and the new possibilities that could offer. We expect that both car manufacturers and equipment suppliers would join forces to develop and standardize the proposed control architecture for future passenger cars

Composite Adaptive Internal Model Control: Theory and Applications to Engine Control

To meet customer demands for vehicle performance and to satisfy increasingly stringent emission standard, powertrain control strategies have become more complex and sophisticated. As a result, controller development and calibration have presented a time-consuming and costly challenge to the automotive industry. This thesis aims to develop new control methodologies with reduced calibration effort. Internal model control (IMC) lends itself to automotive applications for its intuitive control structure with simple tuning philosophy. A few applications of IMC to the boost-pressure control problem have been reported, however, none offered an implementable and easy-to-calibrate solution. Motivated by the need to develop robust and easily calibratable control technologies for boost-pressure control of turbocharged gasoline engines, this thesis developed new control design methodologies in the IMC framework. Two directions are pursued: adaptive IMC (AIMC) and nonlinear IMC. A plant model and a plant inverse are explicit components of IMC. In the presence of plant-model uncertainty, combining the IMC structure with parameter identification through the certainty equivalence principle leads to adaptive IMC (AIMC), where the plant model is identified and the plant inverse is derived by inverting the model. We propose the composite AIMC (CAIMC), which identifies the model and the inverse in parallel, and reduces the tracking error through the online identification. ``Composite" refers to the simultaneous identifications. The constraint imposed by the stability of an n-th order model is nonconvex, and it is re-parameterized as a linear matrix inequality. The parameter identification problem with the stability constraint is reformulated as a convex programming problem. Stability proof and asymptotic performance are established for CAIMC of a general n-th order plant. CAIMC is applied to the boost-pressure control problem of a turbocharged gasoline engine. It is first validated on a physics-based high-order and nonlinear proprietary turbocharged gasoline engine Simulink model, and then validated on a turbocharged 2L four-cylinder gasoline engine on a Ford Explorer EcoBoost. Both simulations and experiments show that CAIMC is not only effective, but also drastically reduces the calibration effort compared to the traditional PI controller with feedforward. Nonlinear IMC is presented in the context of the boost-pressure control of a turbocharged gasoline engine. To leverage the available tools for linear IMC design, the quasi-linear parameter varying (quasi-LPV) models are explored. A new approach for nonlinear inversion, referred to as the structured quasi-LPV model inverse, is developed and validated. A fourth-order nonlinear model which sufficiently describes the dynamic behavior of the turbocharged engine is used as the design model, and the IMC controller is derived based on the structured quasi-LPV model inverse. The nonlinear IMC is applicable when the nonlinear system has a special structural property and has not been generalized yet. Simulations on a high-fidelity turbocharged engine model are carried out to show the feasibility of the proposed nonlinear IMC.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136978/1/connieqz_1.pd

EASILY VERIFIABLE CONTROLLER DESIGN WITH APPLICATION TO AUTOMOTIVE POWERTRAINS

Bridging the gap between designed and implemented model-based controllers is a major challenge in the design cycle of industrial controllers. This gap is mainly created due to (i) digital implementation of controller software that introduces sampling and quantization imprecisions via analog-to-digital conversion (ADC), and (ii) uncertainties in the modeled plant’s dynamics, which directly propagate through the controller structure. The failure to identify and handle these implementation and model uncertainties results in undesirable controller performance and costly iterative loops for completing the controller verification and validation (V&V) process. This PhD dissertation develops a novel theoretical framework to design controllers that are robust to implementation imprecision and uncertainties within the models. The proposed control framework is generic and applicable to a wide range of nonlinear control systems. The final outcome from this study is an uncertainty/imprecisions adaptive, easily verifiable, and robust control theory framework that minimizes V&V iterations in the design of complex nonlinear control systems. The concept of sliding mode controls (SMC) is used in this study as the baseline to construct an easily verifiable model-based controller design framework. SMC is a robust and computationally efficient controller design technique for highly nonlinear systems, in the presence of model and external uncertainties. The SMC structure allows for further modification to improve the controller robustness against implementation imprecisions, and compensate for the uncertainties within the plant model. First, the conventional continuous-time SMC design is improved by: (i) developing a reduced-order controller based on a novel model order reduction technique. The reduced order SMC shows better performance, since it uses a balanced realization form of the plant model and reduces the destructive internal interaction among different states of the system. (ii) developing an uncertainty-adaptive SMC with improved robustness against implementation imprecisions. Second, the continuous-time SMC design is converted to a discrete-time SMC (DSMC). The baseline first order DSMC structure is improved by: (i) inclusion of the ADC imprecisions knowledge via a generic sampling and quantization uncertainty prediction mechanism which enables higher robustness against implementation imprecisions, (ii) deriving the adaptation laws via a Lyapunov stability analysis to overcome uncertainties within the plant model, and (iii) developing a second order adaptive DSMC with predicted ADC imprecisions, which provides faster and more robust performance under modeling and implementation imprecisions, in comparison with the first order DSMC. The developed control theories from this PhD dissertation have been evaluated in real-time for two automotive powertrain case studies, including highly nonlinear combustion engine, and linear DC motor control problems. Moreover, the DSMC with predicted ADC imprecisions is experimentally tested and verified on an electronic air throttle body testbed for model-based position tracking purpose