Kenneth R. Butts

Powertrain control verification benchmark

Industrial control systems are often hybrid systems that are required to satisfy strict performance requirements. Verifying designs against requirements is a difficult task, and there is a lack of suitable open benchmark models to assess, evaluate, and compare tools and techniques. Benchmark models can be valuable for the hybrid systems research community, as they can communicate the nature and complexity of the problems facing industrial practitioners. We present a collection of benchmark problems from the automotive powertrain control domain that are focused on verification for hybrid systems; the problems are intended to challenge the research community while maintaining a manageable scale. We present three models of a fuel control system, each with a unique level of complexity, along with representative requirements in signal temporal logic (STL). We provide results obtained by applying a state of the art analysis tool to these models, and finally, we discuss challenge problems for the research community.

Linear programming support vector regression with wavelet kernel: A new approach to nonlinear dynamical systems identification

Wavelet theory has a profound impact on signal processing as it offers a rigorous mathematical framework to the treatment of multiresolution problems. The combination of soft computing and wavelet theory has led to a number of new techniques. On the other hand, as a new generation of learning algorithms, support vector regression (SVR) was developed by Vapnik et al. recently, in which e-insensitive loss function was defined as a trade-off between the robust loss function of Huber and one that enables sparsity within the SVs. The use of support vector kernel expansion also provides us a potential avenue to represent nonlinear dynamical systems and underpin advanced analysis. However, for the support vector regression with the standard quadratic programming technique, the implementation is computationally expensive and sufficient model sparsity cannot be guaranteed. In this article, from the perspective of model sparsity, the linear programming support vector regression (LP-SVR) with wavelet kernel was proposed, and the connection between LP-SVR with wavelet kernel and wavelet networks was analyzed. In particular, the potential of the LP-SVR for nonlinear dynamical system identification was investigated.

Model predictive control allocation for overactuated systems - stability and performance

Overactuated systems often arise in automotive, aerospace, and robotics applications, where for reasons of redundancy or performance constraints, it is beneficial to equip a system with more control inputs than outputs. This necessitates control allocation methods that distribute control effort amongst many actuators to achieve a desired effect. Until recently, most methods have treated the control allocation as static in the sense that different dynamic authorities of the actuators were not taken into account. Recent advances have used model predictive control allocation (MPCA) to consider the dynamic authorities of the actuators over a receding horizon. In this paper, we consider the dynamic control allocation problem for overactuated systems where each actuator has different dynamic control authority and hard saturation limits. A modular control design approach is proposed, where the controller consists of an outer loop controller that synthesizes a desired virtual control input signal and an inner loop controller that uses MPCA to achieve the desired virtual control signal. We derive sufficient stability conditions for the composite feedback system and show how these conditions may be realized by imposing an additional constraint on the MPCA design. An automotive example is provided to illustrate the effectiveness of the proposed algorithm.

Predictive Control Allocation for a Thermal Management System Based on an Inner Loop Reference Model—Design, Analysis, and Experimental Results

This paper addresses the challenge of controlling an overactuated engine thermal management system where two actuators, with different dynamic authorities and saturation limits, are used to obtain tight temperature regulation. A modular control strategy is proposed that combines model predictive control allocation (MPCA) with the use of an inner loop reference model. This results in an inner loop controller that closely matches a dynamic specification for input-output performance while addressing actuator dynamics and saturation constraints. This paper presents the design and implementation strategy and illustrates the effectiveness of the proposed solution through real-time simulation and experimental results.

Towards combining nonlinear and predictive control of diesel engines

The paper presents the results of a model predictive controller development for diesel engine air path control. The objective is to regulate the intake manifold pressure (MAP) and exhaust gas recirculation (EGR) rate estimate to the specified set-points by coordinated control of the Variable Geometry Turbine (VGT), EGR valve, and EGR throttle. The approach combines elements of nonlinear control (partial nonlinear inversion) and explicit Model Predictive Control (MPC) based on reduced order linear models. The application of nonlinear control renders the model “more linear”, which facilitates the application of predictive control and is shown to reduce the computational complexity. For example, after the nonlinear inversion, the DC gain reversal that is known to complicate the development of effective feedback controllers in diesel engines disappears. The number of controller zones that cover the engine operating region is reduced. The ability of the controller to handle input constraints and intake manifold pressure constraints while following the specified set-points is demonstrated and validated using nonlinear model simulations. Preliminary experimental results are reported.

Emission and drivability tradeoffs in a variable cam timing SI engine with electronic throttle

Tradeoffs between low feedgas emissions and smooth brake torque are discussed in the context of an engine equipped with variable camshaft timing (VCT). The use of VCT lowers the generation of feedgas emissions but adversely affects the torque response. However, with the addition of an electronic throttle and knowledge of online torque, the tradeoffs between emissions and drivability in the VCT engine can be lessened; conventional (nonVCT) engine torque response can be achieved while simultaneously preserving most of the emissions benefits gained by using VCT.

Linear Programming SVM-ARMA

As an emerging non-parametric modeling technique, the methodology of support vector regression blazed a new trail in identifying complex nonlinear systems with superior generalization capability and sparsity. Nevertheless, the conventional quadratic programming support vector regression can easily lead to representation redundancy and expensive computational cost. In this paper, by using the l1 norm minimization and taking account of the different characteristics of autoregression (AR) and the moving average (MA), an innovative nonlinear dynamical system identification approach, linear programming SVM-ARMA2K, is developed to enhance flexibility and secure model sparsity in identifying nonlinear dynamical systems. To demonstrate the potential and practicality of the proposed approach, the proposed strategy is applied to identify a representative dynamical engine model.

_{\rm 2K}

Automotive embedded control systems are a vital aspect of modern automotive development, but the considerable complexity of these systems has made quality checking a challenging endeavor. Simulation-based checking approaches are attractive, as they often scale well with the complexity of the system design. This paper presents an overview of simulation-guided techniques that can be used to increase the confidence in the quality of an automotive powertrain control system design. We discuss the relationship between simulation-based approaches and the broader areas of verification and powertrain control design. Also, we discuss new software tools that use simulation-guided approaches to address various aspects of automotive powertrain control design verification. We conclude by considering ongoing challenges in developing new simulation-guided tools and applying them in a powertrain control development context.

With Application in Engine System Identification

Abstract The paper presents the results of a rate-based model predictive controller development for diesel engine air path control. The objective is to regulate the intake manifold pressure and EGR rate estimate to the specified set-points by coordinated control of the Variable Geometry Turbine (VGT), EGR valve, and EGR throttle. The approach utilizes rate-based reduced order linear models and Model Predictive Control (MPC). Potential benefits of using a rate-based approach versus conventional MPC methods are illustrated. Most notably, it is found that using a rate-based approach greatly extends the operating range of a single linear MPC controller on the nonlinear model. The performance of the rate-based MPC controller versus conventional MPC approaches subject to input and state constraints while following the specified set-points is evaluated using nonlinear model simulations. Strategies to reduce the computational complexity of explicit MPC are exemplified.

Simulation-guided approaches for verification of automotive powertrain control systems

This paper explores MPC for an 8th order air path dynamic model for an automotive diesel engine system. The relatively high order of the system makes the MPC real-time implementation on a practical engine control unit (ECU) infeasible, thereby motivating the model order reduction effort. For the MPC problem with terminal state penalty, Riccati-balanced truncation is used to find a reduced order model for the design of MPC. The presence of a direct feedthrough term from the input to the output in the model requires special treatment and is addressed. We present the analysis of the MPC designs for different reduced order models, compare the reduced order models through open-loop and closed-loop reduction methods, and demonstrate the performance of the resulting linear MPC on a proprietary nonlinear diesel model.