Stochastic MPC with Dynamic Feedback Gain Selection and Discounted Probabilistic Constraints

This paper considers linear discrete-time systems with additive disturbances, and designs a Model Predictive Control (MPC) law incorporating a dynamic feedback gain to minimise a quadratic cost function subject to a single chance constraint. The feedback gain is selected from a set of candidates generated by solutions of multiobjective optimisation problems solved by Dynamic Programming (DP). We provide two methods for gain selection based on minimising upper bounds on predicted costs. The chance constraint is defined as a discounted sum of violation probabilities on an infinite horizon. By penalising violation probabilities close to the initial time and ignoring violation probabilities in the far future, this form of constraint allows for an MPC law with guarantees of recursive feasibility without an assumption of boundedness of the disturbance. A computationally convenient MPC optimisation problem is formulated using Chebyshev's inequality and we introduce an online constraint-tightening technique to ensure recursive feasibility. The closed loop system is guaranteed to satisfy the chance constraint and a quadratic stability condition. With dynamic feedback gain selection, the conservativeness of Chebyshev's inequality is mitigated and closed loop cost is reduced with a larger set of feasible initial conditions. A numerical example is given to show these properties.Comment: 14 page

Reconfigurable predictive control for redundantly actuated systems with parameterised input constraints

A method is proposed for on-line recon guration of the terminal constraint used to provide theoretical nominal stability guarantees in linear model predictive control (MPC). By parameterising the terminal constraint, its complete reconstruction is avoided when input constraints are modi ed to accommodate faults. To enlarge the region of feasibility of the terminal control law for a certain class of input faults with redundantly actuated plants, the linear terminal controller is de ned in terms of virtual commands. A suitable terminal cost weighting for the recon gurable MPC is obtained by means of an upper bound on the cost for all feasible realisations of the virtual commands from the terminal controller. Conditions are proposed that guarantee feasibility recovery for a de ned subset of faults. The proposed method is demonstrated by means of a numerical example.The research leading to these results has received function from the European Union Seventh Framework Programme FP7/2007{2013 under grant agreement no. 314 544.This is the accepted manuscript. The final version is available from Elsevier at http://www.sciencedirect.com/science/article/pii/S0167691114000127

Multiobjective economic MPC of constrained non‐linear systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166264/1/cth2bf00058.pd

Data-driven distributed MPC of dynamically coupled linear systems

In this paper, we present a data-driven distributed model predictive control (MPC) scheme to stabilise the origin of dynamically coupled discrete-time linear systems subject to decoupled input constraints. The local optimisation problems solved by the subsystems rely on a distributed adaptation of the Fundamental Lemma by Willems et al., allowing to parametrise system trajectories using only measured input-output data without explicit model knowledge. For the local predictions, the subsystems rely on communicated assumed trajectories of neighbours. Each subsystem guarantees a small deviation from these trajectories via a consistency constraint. We provide a theoretical analysis of the resulting non-iterative distributed MPC scheme, including proofs of recursive feasibility and (practical) stability. Finally, the approach is successfully applied to a numerical example

Stabilising Model Predictive Control for Discrete-time Fractional-order Systems

In this paper we propose a model predictive control scheme for constrained fractional-order discrete-time systems. We prove that all constraints are satisfied at all time instants and we prescribe conditions for the origin to be an asymptotically stable equilibrium point of the controlled system. We employ a finite-dimensional approximation of the original infinite-dimensional dynamics for which the approximation error can become arbitrarily small. We use the approximate dynamics to design a tube-based model predictive controller which steers the system state to a neighbourhood of the origin of controlled size. We finally derive stability conditions for the MPC-controlled system which are computationally tractable and account for the infinite dimensional nature of the fractional-order system and the state and input constraints. The proposed control methodology guarantees asymptotic stability of the discrete-time fractional order system, satisfaction of the prescribed constraints and recursive feasibility

A general dissipativity constraint for feedback system design, with emphasis on MPC

A ‘General Dissipativity Constraint’ (GDC) is introduced to facilitate the design of stable feedback systems. A primary application is to MPC controllers when it is preferred to avoid the use of ‘stabilising ingredients’ such as terminal constraint sets or long prediction horizons. Some very general convergence results are proved under mild conditions. The use of quadratic functions, replacing GDC by ‘Quadratic Dissipation Constraint’ (QDC), is introduced to allow implementation using linear matrix inequalities. The use of QDC is illustrated for several scenarios: state feedback for a linear time-invariant system, MPC of a linear system, MPC of an input-affine system, and MPC with persistent disturbances. The stability that is guaranteed by GDC is weaker than Lyapunov stability, being ‘Lagrange stability plus convergence’. Input-to-state stability is obtained if the control law is continuous in the state. An example involving an open-loop unstable helicopter illustrates the efficacy of the approach in practice.National Research Foundation Singapor

Stability and performance in MPC using a finite-tail cost

In this paper, we provide a stability and performance analysis of model predictive control (MPC) schemes based on finite-tail costs. We study the MPC formulation originally proposed by Magni et al. (2001) wherein the standard terminal penalty is replaced by a finite-horizon cost of some stabilizing control law. In order to analyse the closed loop, we leverage the more recent technical machinery developed for MPC without terminal ingredients. For a specified set of initial conditions, we obtain sufficient conditions for stability and a performance bound in dependence of the prediction horizon and the extended horizon used for the terminal penalty. The main practical benefit of the considered finite-tail cost MPC formulation is the simpler offline design in combination with typically significantly less restrictive bounds on the prediction horizon to ensure stability. We demonstrate the benefits of the considered MPC formulation using the classical example of a four tank system

Model Predictive Control with and without Terminal Weight: Stability and Algorithms

This paper presents stability analysis tools for model predictive control (MPC) with and without terminal weight. Stability analysis of MPC with a limited horizon but without terminal weight is a long-standing open problem. By using a modified value function as an Lyapunov function candidate and the principle of optimality, this paper establishes stability conditions for this type of widely spread MPC algorithms. A new stability guaranteed MPC algorithm without terminal weight (MPCS) is presented. With the help of designing a new sublevel set defined by the value function of one-step ahead stage cost, conditions for checking its recursive feasibility and stability of the proposed MPC algorithm are presented. The new stability condition and the derived MPCS overcome the difficulties arising in the existing terminal weight based MPC framework, including the need of searching a suitable terminal weight and possible poor performance caused by an inappropriate terminal weight. This work is further extended to MPC with a terminal weight for the completeness. Numerical examples are presented to demonstrate the effectiveness of the proposed tool, whereas the existing stability analysis tools are either not applicable or lead to quite conservative results. It shows that the proposed tools offer a number of mechanisms to achieve stability: adjusting state and/or control weights, extending the length of horizon, and adding a simple extra constraint on the first or second state in the optimisation