We propose control and communication strategies for nonlinear networked control systems subject to state and input constraints. The objective is to steer the state of the system toward a prescribed ...target set in finite time (reachability), while at the same time remaining inside a safety set for all time (safety). By leveraging the notion of the δ-input-to-state stability (ISS) control Lyapunov function, we derive a sufficient condition to generate a communication scheduling, such that the resulting state trajectory guarantees reachability and safety. Moreover, in order to alleviate computational burden, we present a way to find a suitable communication scheduling by implementing abstraction schemes and standard graph search methodologies. Simulation examples validate the effectiveness of the proposed approach.
In this paper, we investigate a model-free design of decentralized event-triggered mechanism for networked control systems (NCSs). The approach aims at simultaneously tuning the optimal parameters ...for the controller and the event-triggered condition, such that a prescribed cost function can be minimized. To achieve this goal, we employ the Bayesian optimization (BO), which is known to be an automatic tuning framework for finding the optimal solution to the black-box optimization problem. Thanks to its efficient search strategy for the global optimum, the BO allows us to design the event-triggered mechanism with relatively a small number of experimental evaluations. This is particularly suited for NCSs where network resources such as the limited life-time of battery powered devices are limited. Some simulation examples illustrate the effectiveness of the approach.
This paper proposes a novel path planning algorithm designed for efficient lunar water exploration. The proposed algorithm leverages a Bayesian network-based sensor model in combination with the RRT* ...algorithm. It is well-known that water is highly likely to be present in permanently shadowed regions (PSRs). However, during exploration within these regions, the rover must escape to illuminated areas within a limited time to recharge its battery. Therefore, it is crucial to develop a path planning algorithm that ensures both timely battery recharging and efficient water exploration. To address this challenge, we first introduce a sensor model based on a Bayesian network for the neutron spectrometer. Subsequently, we propose an algorithm that utilizes this sensor model and the RRT* algorithm to effectively explore water on the lunar surface. The paper provides a numerical simulation, using real data about time-variant illumination areas and time-invariant PSRs on the lunar surface, to illustrate the effectiveness of the proposed algorithm. The results reveal that the algorithm accurately and efficiently detects water under realistic illumination conditions.
Bicycles are frequently involved in traffic collisions with vehicles, particularly when sudden changes in direction occur. This paper presents a robust risk-predictive braking policy to ensure ...collision avoidance in all possible crossing behaviors of a bicycle. The policy controls the vehicle to follow an upper limit of the safe speed before the bicycle changes direction, ensuring that the vehicle can stop in time by the advanced emergency braking system before a collision occurs in any situation. The upper limit of the safe speed is the solution of an intractable robust optimization problem. Therefore, a scenario approach is adapted to develop a tractable approximate problem for the original robust optimization problem. The feasibility and optimality of the problem reduction are theoretically proved. A bisection method-based fast algorithm is designed to solve the approximate problem of the original robust optimization problem, making it applicable in practical scenarios. The convergence of the algorithm is also proven. The effectiveness of the proposed method is validated through hardware-in-the-loop simulations using CarMaker.
In recent years, event-triggered control has emerged as a promising strategy for addressing resource constraints in networked control systems (NCSs), such as limited life-time of battery capacity. ...This paper explores the development of a periodic event-triggered controller through a model-free design, assuming unknown plant dynamics. The design of the event-triggered controller employs an emulation-based approach, which is divided into solving two sub-problems: the problem for designing parameters for the time-triggered (periodic) control law, and the problem for designing parameters for the event-triggered condition. In particular, both problems will be solved through the usage of two distinct Bayesian optimization algorithms. The first problem is addressed by adapting basic Bayesian optimization to include network utilization by considering the number of communication time steps during optimization. The second problem employs constrained Bayesian optimization to incorporate explicit performance constraints within the optimization process. A numerical example is provided to demonstrate the effectiveness of the proposed method.
In this paper, we propose a new aperiodic formulation of model predictive control for nonlinear continuous-time systems. Unlike earlier approaches, we provide event-triggered conditions without using ...the optimal cost as a Lyapunov function candidate. Instead, we evaluate the time interval when the optimal state trajectory enters a local set around the origin. The obtained event-triggered strategy is more suitable for practical applications than the earlier approaches in two directions. First, it does not include parameters (e.g., Lipschitz constant parameters of stage and terminal costs) which may be a potential source of conservativeness for the event-triggered conditions. Second, the event-triggered conditions are necessary to be checked only at certain sampling time instants, instead of continuously. This leads to the alleviation of the sensing cost and becomes more suitable for practical implementations under a digital platform. The proposed event-triggered scheme is also validated through numerical simulations.
This article investigates the design of self-triggered controllers for networked control systems (NCSs), where the dynamics of the plant are unknown a priori . To deal with the unknown transition ...dynamics, we employ the Gaussian process (GP) regression in order to learn the dynamics of the plant. To design the self-triggered controller, we formulate an optimal control problem, such that the optimal control and communication policies can be jointly designed based on the GP model of the plant. Moreover, we provide an overall implementation algorithm that jointly learns the dynamics of the plant and the self-triggered controller based on a reinforcement learning framework. Finally, a numerical simulation illustrates the effectiveness of the proposed approach.
In safety-critical engineering applications, such as robust prediction against adversarial noise, it is necessary to quantify neural networks' uncertainty. Interval neural networks (INNs) are ...effective models for uncertainty quantification, giving an interval of predictions instead of a single value for a corresponding input. This article formulates the problem of training an INN as a chance-constrained optimization problem. The optimal solution of the formulated chance-constrained optimization naturally forms an INN that gives the tightest interval of predictions with a required confidence level. Since the chance-constrained optimization problem is intractable, a sample-based continuous approximate method is used to obtain approximate solutions to the chance-constrained optimization problem. We prove the uniform convergence of the approximation, showing that it gives the optimal INN consistently with the original ones. Additionally, we investigate the reliability of the approximation with finite samples, giving the probability bound for violation with finite samples. Through a numerical example and an application case study of anomaly detection in wind power data, we evaluate the effectiveness of the proposed INN against existing approaches, including Bayesian neural networks, highlighting its capability to significantly improve the performance of applying INNs for regression and unsupervised anomaly detection.
In this paper, we propose a self-triggered formulation of model predictive control for continuous-time nonlinear input-affine networked control systems. Our control method specifies not only when to ...execute control tasks but also provides a way to discretize the optimal control trajectory into several control samples, so that the reduction of communication load will be obtained. Stability analysis under the sample-and-hold implementation is also given, which guarantees that the state converges to a terminal region where the system can be stabilized by a local state feedback controller. Some simulation examples validate our proposed framework.
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