A discrete-time adaptive control approach for uncertain linear multivariable networked systems is proposed. It is capable of dealing with unknown time delays introduced by a communication network between a plant and a controller. Based on the idea of model reference adaptive control, two adaptive laws are presented to reduce the conservativeness that is usually introduced when the time delays are unknown. The stability of the closed-loop system is proven. Simulation examples provide a comparison of the proposed techniques to a nonadaptive algorithm.

Summary: A robustifying strategy for constrained linear multivariable systems is proposed. A combination of tracking model predictive control with output integral sliding mode techniques is used to completely reject bounded matched perturbations. It can be guaranteed that all constraints on inputs, states, and outputs are satisfied although only output information is used. Finally, real‐world experiments with an unstable plant are presented in order to demonstrate the validity and the effectiveness of the proposed approach.

The Internet of Things (IoT) extends the Internet to include also wireless embedded computers that are often equipped with sensors and actuators to monitor and control their physical environment. The IoT is increasingly used for safety-critical applications such as smart factories or networked cars, where a failure of the IoT may lead to catastrophic consequences. The IoT is therefore in urgent need of dependability, where reliability, availability, and security properties can be guaranteed even in harsh environments (e.g., radio interference) and under deliberate attacks (e.g., exploiting side channels). In this paper we give an overview of recent research activities in the LEAD project “Dependable Internet of Things in Adverse Environments” towards a dependable IoT, specifically dependable wireless communication and localization using Ultra-Wide-Band technology, secure execution of real-time software, protocol testing and verification, and dependable networked control. We also present the TruckLab testbed, where our research results can be integrated and validated in a platooning use case. In this testbed, model trucks are automatically controlled to follow a lead truck.

This paper presents a new detectability criterion and a new observer design method for a class of linear time varying systems. The concept is based on the stabilization of the system’s non-negative Lyapunov exponents. The proposed observer design guarantees exponential convergence of the estimation error. Compared to classical techniques, the computational complexity of the resulting observer is drastically reduced if the number of non-negative Lyapunov exponents is small compared to the system order. A numerical simulation example demonstrates the effectiveness of the proposed approach.