251 research outputs found
Receding Horizon Temporal Logic Control for Finite Deterministic Systems
This paper considers receding horizon control of finite deterministic
systems, which must satisfy a high level, rich specification expressed as a
linear temporal logic formula. Under the assumption that time-varying rewards
are associated with states of the system and they can be observed in real-time,
the control objective is to maximize the collected reward while satisfying the
high level task specification. In order to properly react to the changing
rewards, a controller synthesis framework inspired by model predictive control
is proposed, where the rewards are locally optimized at each time-step over a
finite horizon, and the immediate optimal control is applied. By enforcing
appropriate constraints, the infinite trajectory produced by the controller is
guaranteed to satisfy the desired temporal logic formula. Simulation results
demonstrate the effectiveness of the approach.Comment: Technical report accompanying a paper to be presented at ACC 201
Compositional stability criteria based on cyclically neutral supply conditions
In this paper we consider stability of large scale interconnected nonlinear systems that satisfy a strict dissipativity property in terms of local storage and supply functions. Existing compositional stability criteria certify global stability by constructing a global Lyapunov function as the (weighted) sum of local storage functions. We generalize these results by unifying spatial composition, i.e., (weighted) sum of local supply functions is neutral, with temporal composition, i.e., (weighted) sum of supply functions over a time cycle is neutral. Two benchmark examples illustrate the benefits of the developed compositional stability criteria in terms of reducing conservatism and constrained distributed stabilization.</p
A Method to Guarantee Local Convergence for Sequential Quadratic Programming with Poor Hessian Approximation
Sequential Quadratic Programming (SQP) is a powerful class of algorithms for
solving nonlinear optimization problems. Local convergence of SQP algorithms is
guaranteed when the Hessian approximation used in each Quadratic Programming
subproblem is close to the true Hessian. However, a good Hessian approximation
can be expensive to compute. Low cost Hessian approximations only guarantee
local convergence under some assumptions, which are not always satisfied in
practice. To address this problem, this paper proposes a simple method to
guarantee local convergence for SQP with poor Hessian approximation. The
effectiveness of the proposed algorithm is demonstrated in a numerical example
sNMPC:A Matlab Toolbox for Computing Stabilizing Terminal Costs and Sets
This paper presents a Matlab toolbox that implements methods for computing stabilizing terminal costs and sets for nonlinear model predictive control (NMPC). Given a discrete-time nonlinear model provided by the user, the toolbox computes quadratic/ellipsoidal terminal costs/sets and local control laws for the following options: (i) cyclically time-varying or standard terminal ingredients; (ii) first or quasi-second order Taylor approximation of the dynamics; (iii) linear or nonlinear local control laws. The YALMIP toolbox and the MOSEK solver are used for solving linear matrix inequalities and the IPOPT solver (with global search) is used for nonlinear programming. Simulation of the resulting stabilizing NMPC algorithms is provided using the CasADi toolbox.</p
Distributed predictive control of the 7-Machine CIGRÉ power system
Stable operation of the future electrical power system will require efficient techniques for supply-demand balancing, i.e., load-frequency control, due to liberalization of electrical energy production. Currently, there is a growing interest for asymptotically stabilizing the grid frequency via model predictive control (MPC). However, the centralized implementation of standard MPC is hampered by the scale and complexity of power networks. In this paper we therefore evaluate the suitability of a scalable, distributed Lyapunovbased MPC algorithm as an alternative to conventional balancing
techniques. The approach is particularly suited for largescale power networks, as it employs only local information and limited communication between directly-coupled generator buses to provide a stabilizing control action. The effectiveness of the distributed control scheme is assessed by simulating it in closed-loop with the 7-machine CIGRE benchmark system
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