In this paper, the problem of simultaneous fault detection and control (SFDC) for linear switched systems in discrete- and continuous-time cases under a mixed H−/H∞ framework is considered. In essence, a single unit called detector/controller is designed, where the detector is an observer and the controller is an observer-based controller. The conventional mixed H−/H∞ problem is a conservative approach due to the selection of equal Lyapunov matrices. Extended linear matrix inequalities (LMIs) characterizations are used to reduce the conservativeness by the introduction of additional matrix variables, so as to eliminate the coupling of Lyapunov matrices with the system matrices. Indeed, the idea presented in this paper is based on the average dwell time (ADT) and conservatism reduction approaches, which lead to some sufficient conditions for solving the problem in terms of LMI feasibility conditions. Two examples are provided to demonstrate the effectiveness of the proposed method.

In this paper, we develop a novel and safe control design approach that takes demonstrations provided by a human teacher to enable a robot to accomplish complex manipulation scenarios in dynamic environments. First, an overall task is divided into multiple simpler subtasks that are more appropriate for learning and control objectives. Then, by collecting human demonstrations, the subtasks that require robot movement are modeled by probabilistic movement primitives (ProMPs). We also study two strategies for modifying the ProMPs to avoid collisions with environmental obstacles. Finally, we introduce a rule-base control technique by utilizing a finite-state machine along with a unique means of control design for ProMPs. For the ProMP controller, we propose control barrier and Lyapunov functions to guide the system along a trajectory within the distribution defined by a ProMP while guaranteeing that the system state never leaves more than a desired distance from the distribution mean. This allows for better performance on nonlinear systems and offers solid stability and known bounds on the system state. A series of simulations and experimental studies demonstrate the efficacy of our approach and show that it can run in real time. Note to Practitioners —This paper is motivated by the need to create a teach-by-demonstration framework that captures the strengths of movement primitives and verifiable, safe control. We provide a framework that learns safe control laws from a probability distribution of robot trajectories through the use of advanced nonlinear control that incorporates safety constraints. Typically, such distributions are stochastic, making it difficult to offer any guarantees on safe operation. Our approach ensures that the distribution of allowed robot trajectories is within an envelope of safety and allows for robust operation of a robot. Furthermore, using our framework various probability distributions can be combined to represent complex scenarios in the environment. It will benefit practitioners by making it substantially easier to test and deploy accurate, efficient, and safe robots in complex real-world scenarios. The approach is currently limited to scenarios involving static obstacles, with dynamic obstacle avoidance an avenue of future effort.

Purpose: The aim of this paper is to address the problem of fault detection (FD) of linear continuous‐time multi‐agent systems.Design/methodology/approach: A mixedH_∞/H₋formulation of the FD problem using semi‐decentralized filters is presented.Findings: It is shown that through a decomposition approach the drawbacks of the existing distributed FD design methods in multi‐agent systems can be effectively tackled. An extended linear matrix inequality (LMI) characterization is used to reduce the conservativeness of the design solution by introducing additional matrices in order to eliminate the couplings of the Lyapunov matrices with the agent's matrices.Research limitations/implications: It is shown that by applying the proposed decomposition approach the FD problem of multi‐agent systems can be solved by analyzing the problem of a set of decoupled systems whose order and complexity are equal to that of a single agent. This procedure will be useful for both simplifying the computational cost of the solution as well as for developing a fault detection filter having a semi‐decentralized architecture.Practical implications: Application of this methodology to a network of micro‐air vehicles (MAVs) illustrates the effectiveness and capabilities of the proposed design methodology.Social implications: The feasibility of the use of reliable and self‐healing network of unmanned systems, cooperative networks, and multi‐agent systems will be significantly enhanced and improved by the development of advanced fault detection and isolation (FDI) technologies.Originality/value: A semi‐decentralized fault detection (FD) methodology is developed for linear multi‐agent networked systems to reduce the order and complexity of the observers at each agent. A mixedH_∞/H₋formulation of the FD problem by using semi‐decentralized filters is presented. Using this approach each agent can not only detect itsownfaults but also is able to detect itsnearest neighboragents’ faults.