Parameter tuning for vehicle controllers remains a costly and time-intensive challenge in automotive development. Traditional approaches rely on extensive real-world testing, making the process inefficient. We propose a multi-fidelity Bayesian optimization approach that efficiently learns optimal controller parameters by leveraging both low-fidelity simulation data and a very limited number of real-world experiments. Our approach significantly reduces the need for manual tuning and expensive field testing while maintaining the standard two-stage development workflow used in industry. The core contribution is the integration of an auto-regressive multi-fidelity Gaussian process model into Bayesian optimization, enabling knowledge transfer between different fidelity levels without requiring additional low-fidelity evaluations during real-world testing. We validate our approach through both simulation studies and real-world experiments. The results demonstrate that our method achieves high-quality controller performance with only very few real-world experiments, highlighting its potential as a practical and scalable solution for intelligent vehicle control tuning in industrial applications.

Understanding and modeling human driving behavior is essential to improve advanced driver assistance systems (ADAS) and traffic simulations. Traditional driver models are often limited in capturing context- and environment-dependent variations due to simplifying assumptions. The increasing availability of real-world driving data from connected vehicles enables the identification of context-adaptive model parameters, provided that strict privacy and anonymization measures are observed. We analyze data from 156 electric vehicles, each equipped with high-frequency data loggers, recording over 2000 signals per vehicle during a six-month period in uncontrolled environments. As a case study, we examine the approach behavior of a preceding vehicle, which is important for adaptive cruise control (ACC), and its dependence on temperature, light levels, and rain. Our findings suggest that cold temperatures and nighttime conditions increase time-headway and following distances, while the effects of rain appear less pronounced. In the long term, such datasets could refine context-aware driver models for simulation and ADAS calibration and enable personalized adaptive cruise control based on learned driving preferences. By integrating context- and environment-aware driver models, this approach bridges the gap between theoretical modeling and real-world driving behavior.

Secure information exchange of the devices among different domains for cyber-physical power systems (CPPSs) is important yet challenging. Conventional blockchain-based authentication schemes generally adopt single blockchain and signature algorithm, only achieving intradomain or interdomain authentication with lower efficiency, and always failing to meet the confidentiality requirement during information interaction in CPPSs. To address these issues, this paper proposes a cross-domain authentication scheme based on distributed two-layer collaborative blockchains for CPPSs. First, a two-layer-blockchain collaborative authentication architecture is designed, deploying edge servers and taking into account the distributed characteristic of CPPSs. Second, a signcryption algorithm is developed by combining elliptic curve cryptography (ECC) with certificateless cryptography (CLC), which guarantees both the confidentiality and non-repudiation of the block information simultaneously. Furthermore, upper-layer alliance blockchain and lower-layer private blockchain are formed and interact collaboratively via index and Merkle proof, achieving intradomain and interdomain authentication with higher efficiency. Finally, a security analysis and experimental results are presented to superiorly demonstrate the security features and performance in comparison to other schemes in literature.

This article concerns nonlinear model predictive control (MPC) with guaranteed feasibility of inequality path constraints (PCs). For MPC with PCs, the existing methods, such as direct multiple shooting, cannot guarantee feasibility of PCs because the PCs are enforced at finitely many time points only. Therefore, this article presents a novel MPC framework that is capable of not only achieving stability control but also guaranteeing feasibility of PCs during the rolling optimization stages of MPC. Under the above MPC framework, an algorithm is first proposed by applying the semi-infinite programming technique to the rolling optimization of MPC. However, it takes heavy computational time to achieve guaranteed feasibility of PCs. Therefore, to guarantee feasibility of PCs meanwhile effectively reducing the computation burden of the closed-loop system, an event-triggered sampling mechanism is constructed in the above path-constrained MPC algorithm. Moreover, sufficient conditions are given for asymptotic convergence of the closed-loop systems. Finally, the effectiveness of the proposed results is illustrated via a cart-damper-spring system.

We present a method for enhancing autonomous system capabilities by optimizing both planning and control layers over a moving horizon. Traditional reliance on simplified models is overcome by reformulating the planning problem into a robust optimization issue and leveraging multiple operational choices. Constraint satisfaction and collision avoidance are assured through a technique called constraint back off, which adapts to the system's operational mode, derived from precise motion primitives. The path planning problem is then converted into a solvable mixed-integer linear optimization problem. Using existing robust model predictive control strategies for the control layer allows for handling uncertainties and rapid response to changing obstacles. Simulation results validate the efficacy of our approach.

Dynamic optimization problems are pervasive in various fields, ranging from chemical process control to aerospace, autonomous driving, physics, robotics, and beyond. These problems involve optimizing a dynamic system considering inputs, parameters, constraints, and a cost function. For dynamic optimization, two broad classes of strategies emerge: deterministic and heuristic methods. Deterministic optimization methods leverage the analytical properties of the problem, generating a sequence of points that converge to the optimal solution. These techniques are suitable when explicit models and constraints are available and easy to evaluate. On the other hand, heuristic approaches treat the problem as a black box, relying on iterative improvements to a fitness function. They are employed for complex problems with challenging system models or significant uncertainty.

We propose and investigate the combined scheduling and output feedback controller design for multiple cyber-physical systems using a shared communication network. A scheduler determines for which system a communication should take place at a specific time instant, i.e., from which system the measurements should be taken and used to determine a new input sequence to be applied. This input sequence is computed by an output feedback using model predictive control (MPC) and Kalman filtering. The scheduling is based on a comparison of the expected performance for the different plants considering the benefit of taking a measurement. This requires to calculate the expected performance, which depends on the input determined by the nonlinear predictive control law and thus on the yet unknown measurement. Different methods to enable an efficient computation of the expected performance are presented.

Model predictive control (MPC) is an optimization-based strategy for high-performance control that is attracting increasing interest. While MPC requires the online solution of an optimization problem, its ability to handle multivariable systems and constraints makes it a very powerful control strategy specially for MPC of embedded systems, which have an ever increasing amount of sensing and computation capabilities. We argue that the implementation of MPC on field programmable gate arrays (FPGAs) using automatic tools is nowadays possible, achieving cost-effective successful applications on fast or resource-constrained systems. The main burden for the implementation of MPC on FPGAs is the challenging design of the necessary algorithms. We outline an approach to achieve a software-supported optimized implementation of MPC on FPGAs using high-level synthesis tools and automatic code generation. The proposed strategy exploits the arithmetic operations necessaries to solve optimization problems to tailor an FPGA design, which allows a tradeoff between energy, memory requirements, cost, and achievable speed. We show the capabilities and the simplicity of use of the proposed methodology on two different examples and illustrate its advantages over a microcontroller implementation.

SUMMARY: Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if-then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLab(TM)-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity. AVAILABILITY: ADMIT, tutorials and illustrative examples are available free of charge for non-commercial use at http://ifatwww.et.uni-magdeburg.de/syst/ADMIT/

Cellular metabolism continuously processes an enormous range of external compounds into endogenous metabolites and is as such a key element in human physiology. The multifaceted physiological role of the metabolic network fulfilling the catalytic conversions can only be fully understood from a whole-body perspective where the causal interplay of the metabolic states of individual cells, the surrounding tissue and the whole organism are simultaneously considered. We here present an approach relying on dynamic flux balance analysis that allows the integration of metabolic networks at the cellular scale into standardized physiologically-based pharmacokinetic models at the whole-body level. To evaluate our approach we integrated a genome-scale network reconstruction of a human hepatocyte into the liver tissue of a physiologically-based pharmacokinetic model of a human adult. The resulting multiscale model was used to investigate hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication at a systems level. The specific models simultaneously integrate multiple layers of biological organization and offer mechanistic insights into pathology and medication. The approach presented may in future support a mechanistic understanding in diagnostics and drug development.