This paper designs a model predictive control (MPC) law for constrained linear systems with stochastic additive disturbances and noisy measurements, minimising discounted cost subject to expectation constraint. It is assumed that sensor data lost known probability. Taking into account the losses modelled by Bernoulli process, we parameterise predicted policy as an affine function of future obse...

Symbolic control is an abstraction-based controller synthesis approach that provides, algorithmically, certifiable-by-construction controllers for cyber-physical systems. approaches usually assume full-state information available which not suitable many real-world applications with partially-observable states or output information. This article introduces a framework output-feedback symbolic co...

In this paper, a novel method is developed for designing the output feedback controller for Static Synchronous Series Compensator (SSSC). In the proposed method, the problem of selecting the output feedback gains for the SSSC controllers is changed into an optimization problem with a time domain-based objective function.Then, it is solved by using the particle swarm optimization (PSO) algorithm...