The paper on hand addresses the problem of guaranteed satisfaction of ellipsoidal state and input constraints when learning parametric controllers for discrete-time nonlinear but control-affine systems with additive bounded disturbances. The parametric controllers are proposed to be compositions of parametric functions (like neural networks), for which state-dependent transformations lead to ranges that are equal to sets of admissible control inputs derived from offline computed reachable state sets. In this way, constraint satisfaction is guaranteed for all control laws that can be learned through the choice of the parameter vector.

This paper aims at applying optimal control to investigate different vaccination strategies against the epidemic spread of viral diseases. Background of the study is the situation in the first half of 2021, when many countries started their vaccination procedures against the COVID-19 disease, but suffered from shortages of vaccines, such that the efficient distribution of the available amount of vaccine turned out to be crucial to mitigate the pandemic. The paper first suggests an extended version of a known model of virus spread in order include the vaccination process. Based on this model, the formulation and solution of optimization problems is used to determine how available vaccine should be distributed over different age-groups of the population to minimize virus spread. Effectiveness of the obtained strategies compared to standard ones is demonstrated in simulations. © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

This paper considers the problem of planning reference trajectories for cooperating autonomous vehicles. Our previous work [1] relied on mixed-integer quadratic programming (MIQP) for planning, while controllable sets of hybrid automata were used to assess feasibility. In this paper, we supersede MIQP in the online part and obtain plans by directly making use of controllable sets and adapting ideas from approximate multi-parametric programming. The run-time of the algorithm is not only lower, but also more predictable than that of MIQP solvers, which is crucial for real-time operation. Even though it returns suboptimal solutions, the algorithm also enables the use of smaller sampling times or longer planning horizons.

This paper aims at cooperatively resolving conflicts arising between several fully-autonomous, communicating vehicles in general on-road traffic scenarios. A hierarchical control scheme with two main units is proposed: A short-term planning unit determines obstacle-avoiding reference trajectories in a receding-horizon-fashion, based on solving mixed-integer quadratic programs (MIQP) for simplified vehicle dynamics. A novel cooperative conflict resolution scheme is proposed which orchestrates the plans of neighboring vehicles, trying to minimize a common cost function. Tracking controllers based on model predictive control (MPC) are then employed to track the references using a nonlinear vehicle model. The functionality of the proposed method is demonstrated in a simulation example.

For distributed discrete-event systems (DESs), which are specified by a set of coupled automata, the centralized synthesis for a composed plant model is often undesired due to a high computational effort and the need to subsequently split the result into local controllers. This paper proposes modeling and synthesis procedures to obtain optimal decentralized controllers in state-feedback form for distributed DES. In particular, this paper addresses the DES with priority structures, in which subsystems with high priorities are supplied with the output of subsystems with lower priority. If the subsystem dependencies have linear or treelike structures, the synthesis of the subsystem controllers can be accomplished separately. Any local controller is computed by algebraic computations, it communicates with controllers of adjacent subsystems, and it aims at transferring the corresponding subsystem into goal states with a minimal sum of transfer costs. As is shown for an example, the computational effort can be significantly reduced compared with the synthesis of centralized controllers following the composition of all subsystem models.

This paper proposes an approach to establish cooperative behavior within traffic scenarios involving only autonomously driving vehicles. The main idea is to employ principles of auction-based control to determine driving strategies by which the vehicles reach their driving goals, while adjusting their paths to each other and adhering to imposed constraints like traffic rules. Driving plans (bids) are repetitively negotiated among the control units of the vehicles (the auction) to obtain a compromise between separate (local) vehicle goals and the global objective to resolve the considered traffic scenario. The agreed driving plans serve as reference trajectories for local model-predictive controllers of the vehicles to realize the driving behavior. The approach is illustrated for a cooperative overtaking scenario comprising three vehicles.

The supervisory controllers used in manufacturing processes to establish nominal sequences of production steps can theoretically be synthesized based on the supervisory control theory (SCT). The fact, that in practice manual design procedures are preferred over the SCT, is (among other reasons) due to the fact that the models used in the SCT are of an abstract type rather than reflecting the signal flow within a discretely controlled process, and that sets of permissible behaviors are obtained rather than selected (optimal) behaviors within this set. This paper proposes a modeling and synthesis scheme for discrete control which aims at retaining the closed loop structure of the controlled process. An algebraic representation of the discrete event dynamics is chosen to enable the transfer of concepts from continuous control to the supervisory control task. In addition, the scheme considers that the plant to be controlled often comprises continuous quantities (from which events need to be extracted), and that larger manufacturing processes typically have a modular and hierarchical structure.

Modeling and predicting human behavior is indispensable when industrial robots interacting with human operators are to be manipulated safely and efficiently. One challenge is that human operators tend to follow different motion patterns, depending on their intention and the structure of the environment. This precludes the use of classical estimation techniques based on kinematic or dynamic models, especially for the purpose of long-term prediction. In this paper, we propose a method based on Hidden Markov Models to predict the region of the workspace that is possibly occupied by the human within a prediction horizon. In contrast to predictions in the form of single points such as most likely human positions as obtained from previous approaches, the regions obtained here may serve as safety constraints when the robot motion is planned or optimized. This way one avoids collisions with a probability not less than a predefined threshold. The practicability of our method is demonstrated by successfully and accurately predicting the motion of a human arm in two scenarios involving multiple motion patterns.

This paper considers the task of path planning for articulated robots such that the end effector is driven optimally between two points in the workspace while collision with dynamic obstacles is avoided. Compared to path planning in the configuration space, approaches in the workspace save the computationally expensive step of mapping obstacles from the workspace into the configuration space. The method presented here builds on a problem formulation as a mixed-integer program considering time-varying constraints resulting from moving obstacles, as well as state and input constraints depending on the region of the work space. The method is applied to a two-link robot with static and moving obstacles and is evaluated for different situations.

The safety of the planned paths of autonomous cars with respect to the movement of other traffic participants is considered. Therefore, the stochastic occupancy of the road by other vehicles is predicted. The prediction considers uncertainties originating from the measurements and the possible behaviors of other traffic participants. In addition, the interaction of traffic participants, as well as the limitation of driving maneuvers due to the road geometry, is considered. The result of the presented approach is the probability of a crash for a specific trajectory of the autonomous car. The presented approach is efficient as most of the intensive computations are performed offline, which results in a lean online algorithm for real-time application.

This contribution proposes a link between the specification of supervisory controllers by Sequential Function Charts (SFC) and the verification of embedded systems with hybrid dynamics. The SFC are transformed into modular timed automata using a procedure based on graph grammars. The resulting controller model is composed with a hybrid automaton (with possibly nonlinear continuous dynamics) that models the plant behavior. In order to verify safety properties of the composed system algorithmically, a tool implementing the recently proposed approach of counterexample guided model checking is employed. The procedure is illustrated for a processing system example.

This paper discusses important hybrid aspects of chemical processing plants. It is outlined that discrete phenomena occur both on the physical level and in the control of these plants. As the dynamics of the transformations of energy and material are predominantly continuous, large and complex hybrid systems arise. We focus on three different aspects of dealing with such systems: (1) Modeling and simulation of hybrid systems for the design and optimization of plants, controllers and operating strategies. We present powerful simulation environments that have been developed in recent years. (2) Validation of plant instrumentation and discrete controllers. These systems are largely responsible for the safe and economic operation of chemical plants and the protection of the workforce, and the environment. Techniques for the verification of discrete controllers for continuous processes are discussed, which are based on a discrete approximation of the continuous dynamics. (3) Scheduling of batch plants. For plants that are operated in a discontinuous fashion, the timing and sequencing of the operations are very important for the efficient use of the equipment. This leads to large mixed-integer optimization problems. For a typical example, we show how the process and the constraints can be modeled and describe an efficient solution algorithm.