Robust and performant controllers are essential for industrial applications. However, deriving controller parameters for complex and nonlinear systems is challenging and time-consuming. To facilitate automatic controller parametrization, this work presents a novel approach using deep reinforcement learning (DRL) with N-dimensional B-spline geometries (BSGs). We focus on the control of parameter-variant systems, a class of systems with complex behavior which depends on the operating conditions. For this system class, gain-scheduling control structures are widely used in applications across industries due to well-known design principles. Facilitating the expensive controller parametrization task regarding these control structures, we deploy an DRL agent. Based on control system observations, the agent autonomously decides how to adapt the controller parameters. We make the adaptation process more efficient by introducing BSGs to map the controller parameters which may depend on numerous operating conditions. To preprocess time-series data and extract a fixed-length feature vector, we use a long short-term memory (LSTM) neural networks. Furthermore, this work contributes actor regularizations that are relevant to real-world environments which differ from training. Accordingly, we apply dropout layer normalization to the actor and critic networks of the truncated quantile critic (TQC) algorithm. To show our approach's working principle and effectiveness, we train and evaluate the DRL agent on the parametrization task of an industrial control structure with parameter lookup tables.

This work presents an approach for parameter estimation of nonlinear systems by means of robust maximum entropy offline reinforcement learning (RL). The identification of parameter variant systems is a challenging problem in industrial applications. We address the parameter estimation on disturbed measurements with an actor-critic RL agent that is extended by a Lyapunov neural network. Accordingly, a robust soft actor-critic algorithm (RSAC) is applied to the parametrization problem. The policy is learned on test trajectories and can be applied to identify nonlinear system parameter maps for comparable system dynamics across an operating range. In a simulative study, the performance of the proposed concept is shown for a permanent magnet synchronous machine model in the d/q-frame formulation with current dependent and nonlinear flux linkages. The trained RL agent is evaluated on a different nonlinear machine parameter set under noisy measurements. The results indicate applicability to real-world tasks such as system parameter tracking.

In this contribution, an approach for modeling driving behavior in intersection scenarios, based on a hybrid dynamic game framework, is presented. Using the hybrid system model, the movement of the traffic agent is divided into maneuvers. Therefore, the decision-making process is a maneuver selection problem having reduced complexity compared to trajectory planning. While previous models used maneuver description with constant acceleration values, the presented approach models the maneuvers on a more macroscopic level using the well known Intelligent Driver Model. This has the advantage of creating more realistic acceleration profiles without increasing the computational complexity of the model. The maneuver selection process is modeled using the nash equilibrium concept of game theory. The resulting coupled optimization problem for each player is solved using an iterated best response algorithm determining the nash equilibrium. Finally, using simulation examples, it is shown that the presented model is capable of creating a variety of scenarios using different parameter sets as well as simulating scenarios with a high number of traffic participants.

In recent years, the amount of electric and electronic components in the automotive power network (APN) has increased significantly. With regard to autonomous driving, telecommunication, infotainment, and other comfort functionalities, a further growing complexity in the APN is emerging. Additionally, the still requested customization of cars leads to more variants, models, and decoupled development cycles in terms of software and hardware. To ensure a flexible APN design, this paper demonstrates a modular approach for the power management combining auction theory and service-oriented architecture (SOA) for APNs with multiple voltage levels. The presented algorithm facilitates distributed decision-making by the growing number of components in the network regarding their power consumption. Hence, the individual components adapt their behavior in terms of component states and the APN condition. Furthermore, the SOA paradigm ensures the scalability and fault tolerance of the proposed algorithm.

The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavy-duty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling approaches for the FCS, battery, ultracapacitor, and further relevant subsystems are briefly described. Afterward, common methodologies for EMSs are structured, presenting a new taxonomy for dynamic optimization-based EMSs from a control engineering perspective. Finally, the findings lead to a guideline toward holistic EMSs, encouraging the co-optimization of system design, and EMS development for FCHEVs. For the EMS, we propose a layered model predictive control (MPC) approach, which takes velocity planning, the mitigation of degradation effects, and the auxiliaries into account simultaneously.

A new concept for modeling the behavior and the interaction between drivers at intersections is presented. For this purpose a hybrid dynamic game is introduced using the concepts of game theory. The proposed hybrid game is used to model the interactive behavior in a junction scenario. The presented hybrid approach divides the modeling of motion into individual maneuvers. With this partition of motion, the decision process can be reduced to a simple maneuver selection that takes into account the motion of the vehicle without the need for solving complex coupled differential equations. In order to make computation of a solution feasible we propose a rule based adaption mechanism. Simulation is used to show the applicability of the developed hybrid dynamic game approach.

This paper presents a novel cooperative control algorithm for vehicle-manipulators (VMs) with a human operator. VMs usually operate in unstructured environments, which means that a full automation of the overall system, combing a vehicle and a robotic manipulator is currently very challenging. Therefore, human operator controlled VMs are state-of-the-art. With current developments in autonomous driving, the automation of the vehicle platform is within reach. A cooperative shared control between the autonomous platform and the human controlled manipulator can happen through the coupling motion between the vehicle platform and the manipulator. An autonomous vehicle platform can furthermore be used to support the human operator with the control of the manipulator. However, the future trajectory of the manipulator intended by the human operator is in general not known to the autonomous vehicle. The main question is thus how the autonomous vehicle should act in order to support the human controlled manipulator in following its unknown trajectory. To solve this problem, we propose an approach that characterizes the cooperation and the unknown errors with an algebraic equation. The novel approach is compared to cooperative control methods with known errors of the manipulator, based on the theory of differential games. The benefits of the proposed method are that no sensors for the environment perception and for the state measurements of the manipulator are necessary, which are demonstrated in simulations.

In a shared control system humans and machines cooperatively interact. From the control theoretic point of view this can be seen as a system which is controlled by several controllers that are either formed by a human or by a machine. Since all controllers influence the system they affect each other. However, the human parts are given and cannot be changed. Therefore, the question is how to design the non-human controller systematically stable and without experiments. In this paper a design concept for these controllers is proposed which is based on game theoretic modeling. We show that adding a controller to the overall system has to lead to a Nash equilibrium. We further show that remaining degrees of freedom may then be used to optimize the designed controller with respect to a certain global objective function that specifies the demands of the system designers. Based on this idea necessary and sufficient conditions for cooperative shared design are stated. Practical approaches to solve the design problem are presented for real world problems. An example shows the applicability of the concept.

Several publications have shown that it is beneficial to design a driver assistance system using a shared control structure. For the steering task this structure can be realized with a setup in which driver and automation can apply a torque on the steering wheel in parallel. Thereby both, driver and assistance system, interact with the vehicle and each other over the haptical channel. In the system the driver is given and cannot be changed. The question is how to design the assistance system controller as an ideal complement to the driver. In this paper a formal design concept is applied to this problem which utilizes the fact that adding a controller to the overall system has to lead to a Nash equilibrium. Remaining degrees of freedom are used to optimize the designed controller with respect to a global objective function that specifies overall system performance. We refer the concept as “cooperative shared control design”. For the concept driver and vehicle are modeled as a differential game. We show systematically that this concept can be used to determine the optimal assistance system if the driver characteristics are known. Simulations prove the applicability of this concept.