Supervisory control synthesis leverages the nonblocking property to show liveness of the supervised system. This property is particularly weak when system models include fault behavior, reconfiguration, or multiple control goals. To capture a more suitable nonblocking property for such system models, this paper introduces modal and multimodal nonblocking. These novel nonblocking variants impose a restriction on the states visited on the path towards a marked state. Synthesis algorithms are presented to construct modal and multimodal nonblocking supervisors. The novel nonblocking variants are illustrated with three intuitive examples, inspired by real synthesis problems encountered while applying supervisory control synthesis to safety-critical water infrastructures. A comparison is made between the novel nonblocking variants and established nonblocking variants to show that they are distinct. Additionally, where possible, conditions are formulated under which one variant implies the other.
In this paper, we address supervisory control in discrete-event systems with event forcing. In particular, partial observation of the supervisor is considered. Unlike traditional supervisory control, which relies on event enablement and disablement, the forcing mechanism allows a…
Advanced control of heating and cooling systems can substantially reduce energy costs and pollution. However, real-world adoption of popular algorithms among researchers, such as model predictive control (MPC) and reinforcement learning (RL), remains limited due in part to their…
We address the problem of robust controller synthesis for a class of linear temporal logic (LTL) specifications over families of perturbed systems using symbolic control techniques. Given a dynamical system, a specification, and a symbolic controller synthesized using the fixed-p…
The displacement of synchronous generation by inverter-based resources is accelerating power system frequency dynamics beyond the response capability of conventional automatic generation control. This paper presents Autonomous Grid Generation Control with Decision Transformers, a…
This paper develops a data-enabled primal-dual framework for learning optimal control policies for unknown linear discrete-time systems from online data. The proposed approach views the data-dependent control synthesis problem as a time-varying semidefinite program (SDP) whose co…
We consider the problem of synthesizing robust feedback controllers for discrete-time linear systems that ensure the satisfaction of context-dependent linear temporal logic specifications in the presence of additive bounded disturbances. Building on existing results that reduce c…