Parameter Estimation of Linear Dynamical Systems with Gaussian Noise

We present a novel optimization-based method for parameter estimation of a time-varying dynamic linear system. This method optimizes the likelihood of the parameters given measured data using an optimization algorithm tailored to the structure of this maximum likelihood estimation problem. Some parameters of the covariance of process and measurement noise can also be estimated. This is particularly useful when offset-free Model Predictive Control with a linear disturbance model is performed. To reduce the complexity of the maximum likelihood estimation problem we also propose an approximate formulation and show how it is related to the actual problem. We present the advantages of the proposed approach over commonly used methods in the framework of Moving Horizon Estimation. We also present how to use Sequential Quadratic Programming efficiently for the optimization of our formulations. Finally, we show the performance of the proposed methods through numerical simulations. First, on a minimal example with only one parameter to be estimated, and second, on a system with heat and mass transfer. Both methods can successfully estimate the model parameters in these examples.Comment: Submitted to IEEE European Control Conference 2023 (ECC23). Contains 8 pages including 6 figure

Partitioned Quasi-Newton Approximation for Direct Collocation Methods and Its Application to the Fuel-Optimal Control of a Diesel Engine

The numerical solution of optimal control problems by direct collocation is a widely used approach. Quasi-Newton approximations of the Hessian of the Lagrangian of the resulting nonlinear program are also common practice. We illustrate that the transcribed problem is separable with respect to the primal variables and propose the application of dense quasi-Newton updates to the small diagonal blocks of the Hessian. This approach resolves memory limitations, preserves the correct sparsity pattern, and generates more accurate curvature information. The effectiveness of this improvement when applied to engineering problems is demonstrated. As an example, the fuel-optimal and emission-constrained control of a turbocharged diesel engine is considered. First results indicate a significantly faster convergence of the nonlinear program solver when the method proposed is used instead of the standard quasi-Newton approximation

Optimal Control of Diesel Engines: Numerical Methods, Applications, and Experimental Validation

In response to the increasingly stringent emission regulations and a demand for ever lower fuel consumption, diesel engines have become complex systems. The exploitation of any leftover potential during transient operation is crucial. However, even an experienced calibration engineer cannot conceive all the dynamic cross couplings between the many actuators. Therefore, a highly iterative procedure is required to obtain a single engine calibration, which in turn causes a high demand for test-bench time. Physics-based mathematical models and a dynamic optimisation are the tools to alleviate this dilemma. This paper presents the methods required to implement such an approach. The optimisation-oriented modelling of diesel engines is summarised, and the numerical methods required to solve the corresponding large-scale optimal control problems are presented. The resulting optimal control input trajectories over long driving profiles are shown to provide enough information to allow conclusions to be drawn for causal control strategies. Ways of utilising this data are illustrated, which indicate that a fully automated dynamic calibration of the engine control unit is conceivable. An experimental validation demonstrates the meaningfulness of these results. The measurement results show that the optimisation predicts the reduction of the fuel consumption and the cumulative pollutant emissions with a relative error of around 10% on highly transient driving cycles.ISSN:1024-123

An Equivalent Emission Minimization Strategy for Causal Optimal Control of Diesel Engines

One of the main challenges during the development of operating strategies for modern diesel engines is the reduction of the CO2 emissions, while complying with ever more stringent limits for the pollutant emissions. The inherent trade-off between the emissions of CO2 and pollutants renders a simultaneous reduction difficult. Therefore, an optimal operating strategy is sought that yields minimal CO2 emissions, while holding the cumulative pollutant emissions at the allowed level. Such an operating strategy can be obtained offline by solving a constrained optimal control problem. However, the final-value constraint on the cumulated pollutant emissions prevents this approach from being adopted for causal control. This paper proposes a framework for causal optimal control of diesel engines. The optimization problem can be solved online when the constrained minimization of the CO2 emissions is reformulated as an unconstrained minimization of the CO2 emissions and the weighted pollutant emissions (i.e., equivalent emissions). However, the weighting factors are not known a priori. A method for the online calculation of these weighting factors is proposed. It is based on the Hamilton–Jacobi–Bellman (HJB) equation and a physically motivated approximation of the optimal cost-to-go. A case study shows that the causal control strategy defined by the online calculation of the equivalence factor and the minimization of the equivalent emissions is only slightly inferior to the non-causal offline optimization, while being applicable to online control.ISSN:1996-107

Dynamic Feedforward Control of a Diesel Engine Based on Optimal Transient Compensation Maps

To satisfy the increasingly stringent emission regulations and a demand for an ever lower fuel consumption, diesel engines have become complex systems with many interacting actuators. As a consequence, these requirements are pushing control and calibration to their limits. The calibration procedure nowadays is still based mainly on engineering experience, which results in a highly iterative process to derive a complete engine calibration. Moreover, automatic tools are available only for stationary operation, to obtain control maps that are optimal with respect to some predefined objective function. Therefore, the exploitation of any leftover potential during transient operation is crucial. This paper proposes an approach to derive a transient feedforward (FF) control system in an automated way. It relies on optimal control theory to solve a dynamic optimization problem for fast transients. A partially physics-based model is thereby used to replace the engine. From the optimal solutions, the relevant information is extracted and stored in maps spanned by the engine speed and the torque gradient. These maps complement the static control maps by accounting for the dynamic behavior of the engine. The procedure is implemented on a real engine and experimental results are presented along with the development of the methodology

Numerical Optimal Control of Turbo Dynamic Ventricular Assist Devices

The current paper presents a methodology for the derivation of optimal operating strategies for turbo dynamic ventricular assist devices (tVADs). In current clinical practice, tVADs are typically operated at a constant rotational speed, resulting in a blood flow with a low pulsatility. Recent research in the field has aimed at optimizing the interaction between the tVAD and the cardiovascular system by using predefined periodic speed profiles. In the current paper, we avoid the limitation of using predefined profiles by formulating an optimal-control problem based on a mathematical model of the cardiovascular system and the tVAD. The optimal-control problem is solved numerically, leading to cycle-synchronized speed profiles, which are optimal with respect to an arbitrary objective. Here, an adjustable trade-off between the maximization of the flow through the aortic valve and the minimization of the left-ventricular stroke work is chosen. The optimal solutions perform better than constant-speed or sinusoidal-speed profiles for all cases studied. The analysis of optimized solutions provides insight into the optimized interaction between the tVAD and the cardiovascular system. The numerical approach to the optimization of this interaction represents a powerful tool with applications in research related to tVAD control. Furthermore, patient-specific, optimized VAD actuation strategies can potentially be derived from this approach

Numerical Optimal Control of Turbo Dynamic Ventricular Assist Devices

The current paper presents a methodology for the derivation of optimal operating strategies for turbo dynamic ventricular assist devices (tVADs). In current clinical practice, tVADs are typically operated at a constant rotational speed, resulting in a blood flow with a low pulsatility. Recent research in the field has aimed at optimizing the interaction between the tVAD and the cardiovascular system by using predefined periodic speed profiles. In the current paper, we avoid the limitation of using predefined profiles by formulating an optimal-control problem based on a mathematical model of the cardiovascular system and the tVAD. The optimal-control problem is solved numerically, leading to cycle-synchronized speed profiles, which are optimal with respect to an arbitrary objective. Here, an adjustable trade-off between the maximization of the flow through the aortic valve and the minimization of the left-ventricular stroke work is chosen. The optimal solutions perform better than constant-speed or sinusoidal-speed profiles for all cases studied. The analysis of optimized solutions provides insight into the optimized interaction between the tVAD and the cardiovascular system. The numerical approach to the optimization of this interaction represents a powerful tool with applications in research related to tVAD control. Furthermore, patient-specific, optimized VAD actuation strategies can potentially be derived from this approach