Set-membership state estimation approaches have been developed in recent years to implement observers that allow for bounding the domains of possible system states in a guaranteed manner. For that purpose, it is neces...
Set-membership state estimation approaches have been developed in recent years to implement observers that allow for bounding the domains of possible system states in a guaranteed manner. For that purpose, it is necessary to describe the system dynamics either by continuous-time or discrete-time state equations in which uncertainty of parameters and initial conditions as well as the influence of noise are represented by bounded domains. The same holds for the system’s output equation, where measurement noise is again assumed to be bounded with measurement time instants that are typically assumed to be perfectly known. This latter assumption is removed in this paper to make the bounded-error state estimation approach applicable to a wide range of systems where measurement time instants are only imprecisely known. This is typically the case for distributedcontrolsystems in which sensor and actuator signals are transmitted via a communication network. As such, the wide classes of robotic multi-agent systems as well as distributed energy systems belong to this kind of models.
Many repetitive control problems are characterized by the fact that disturbances have the same effect in each successive execution of the same control task. Such disturbances comprise the lumped representation of unmo...
ISBN:
(数字)9783907144077
ISBN:
(纸本)9781665497336
Many repetitive control problems are characterized by the fact that disturbances have the same effect in each successive execution of the same control task. Such disturbances comprise the lumped representation of unmodeled parts of the open-loop system dynamics, a systematic model-mismatch or, more generally, deterministic yet unknown uncertainty. In such cases, well-known strategies for iterative learning control are based on enhancing the system behavior not only by exploiting data gathered during a single execution of the task but also using information from previous executions. The corresponding dual problem, namely, iterative learning state and disturbance estimation has not yet received the same amount of attention. However, it is obvious that improved estimates for the aforementioned states and disturbances which periodically occur in each execution will be a means to achieve an improved accuracy and, therefore, in future work also to optimize the control accuracy. In this paper, we present a joint design procedure for observer gains in two independent dimensions, a gain for processing information in the temporal domain during a single execution of the task (also named trial) and a gain for learning in the iteration domain (i.e., from trial to trial).
暂无评论