This paper considers linear discrete-time systems with additive disturbances, and designs a Model Predictive Control (MPC) law incorporating dynamic feedback gain to minimise quadratic cost function subject single chance constraint. The is selected online we provide two selection methods based on minimising upper bounds predicted costs. constraint defined as discounted sum of violation probabil...

Resource allocation plays a critical role to enhance the performance of cognitive orthogonal frequency division multiplexing (OFDM) network. However, due to lack the cooperation between cognitive network and primary network, the channel state information (CSI) between cognitive radio (CR) user and primary user (PU) could not be estimated precisely. In this work, a resource allocation problem ov...

We present an extension to Experience-driven Predictive Control (EPC) that leverages a Gaussian belief propagation strategy to compute an uncertainty set bounding the evolution of the system state in the presence of time-varying state uncertainty. This uncertainty set is used to tighten the constraints in the predictive control formulation via a chance constrained approach, thereby providing a ...

Constraint-admissible sets have been widely used in the study of control of systems with hard constraints. This paper proposes a generalization of the maximal constraint admissible set for constrained linear discrete time system to the case where chance or probabilistic constraints are present. Defined in the most obvious way, the maximal probabilistic constraint-admissible set is not invariant...

In our day-to-day discourse on uncertainty, words like belief, chance, plausible, likelihood and probability are commonly encountered. Often, these words are used interchangeably, because they are intended to encapsulate some loosely articulated notions about the unknowns. The purpose of this paper is to propose a framework that is able to show how each of these terms can be made precise, so th...

In this paper, we consider an n-player non-cooperative game where the random payoff function of each player is defined by its expected value and her strategy set a joint chance constraint. The constraint vectors are independent. We case when probability distribution vector belongs to subset elliptical distributions as well it finite mixture from subset. propose convex reformulation derive bound...

If there are grey variables in the constraint conditions in uncertain programming, the programming is called grey chance constrained programming. In this paper we provide the concept of grey chance constrained programming. When the whitenization weight functions of grey variables are known and the distribution functions are constructed, we studied the certainty equivalence solution of grey chan...

This paper studies the problem of distributionally robust model predictive control (MPC) using total variation distance ambiguity sets. For a discrete-time linear system with additive disturbances, we provide conditional value-at-risk reformulation MPC optimization that is in expected cost and chance constraints. The constraint over-approximated as simpler, tightened reduces computational burde...

We study joint chance constraints where the distribution of the uncertain parameters is onlyknown to belong to an ambiguity set characterized by the mean and support of the uncertaintiesand by an upper bound on their dispersion. This setting gives rise to pessimistic (optimistic)ambiguous chance constraints, which require the corresponding classical chance constraints to bes...

This report proposes a new two-stage optimization method for robust Model Predictive Control (RMPC) with Gaussian disturbance and state estimation error. Since the disturbance is unbounded, it is impossible to achieve zero probability of violating constraints. Our goal is to optimize the expected value of a objective function while limiting the probability of violating any constraints over the ...