1,721,649 research outputs found
Output-feedback predictive control of constrained linear systems with disturbances via set-membership state estimation
No full textThis paper combines model predictive control (MPC) and set-membership (SM) state estimation techniques for controlling systems subject to hard input and state constraints. Linear systems with unknown but bounded disturbances and partial state information are considered. The adopted approach guarantees that the constraints are satisfied for all the states which are compatible with the available information and for all the disturbances within given bounds. Properties of the proposed MPC-SM algorithm and simulation studies are reported
Combined design of disturbance model and observer for offset-free model predictive control
No full textThis note presents a method for the combined design of an integrating disturbance model and of the observer (for the augmented system) to be used in offset-free model predictive controllers. A dynamic observer is designed for the original (nonaugmented) system by solving an Hprop control problem aimed at minimizing the effect of unmeasured disturbances and plant/model mismatch on the output prediction error. It is shown that, when offset-free control is sought, the dynamic observer is equivalent to choosing an integrating disturbance model and an observer for the augmented system. An example of a chemical reactor shows the main features and benefits of the proposed method
A master-slave algorithm for the optimal control of continuous-time switched affine systems
No full textFor continuous-time switched affine systems, this paper proposes an approach for solving infinite-horizon optimal control problems where the decision variables are the switching instants and the sequence of operating modes. The procedure iterates between a "master" procedure that finds an optimal switching sequence of modes, and a "slave" procedure that finds the optimal switching instants
Direct Data-Driven Control of Constrained Systems
Full text linkIn model-based control design, one often has to describe the plant by a linear model. Deriving such a model poses issues of parameterization, estimation, and validation of the model before designing the controller. In this paper, a direct data-driven control method is proposed for designing controllers that can handle constraints without deriving a model of the plant and directly from data. A hierarchical control architecture is used, in which an inner linear time-invariant or linear parameter-varying controller is first designed to match a simple and a priori specified closed-loop model. Then, an outer model predictive controller is synthesized to handle input/output constraints and to enhance the performance of the inner loop. The effectiveness of the approach is illustrated by means of a simulation and an experimental example. Practical implementation issues are also discussed
Suboptimal Explicit Receding Horizon Control via Approximate Multiparametric Quadratic Programming
No full textAlgorithms for solving multiparametric quadratic programming (MPQP) were recently proposed in Refs. 1–2 for computing explicit receding horizon control (RHC) laws for linear systems subject to linear constraints on input and state variables. The reason for this interest is that the solution to MPQP is a piecewise affine function of the state vector and thus it is easily implementable online. The main drawback of solving MPQP exactly is that, whenever the number of linear constraints involved in the optimization problem increases, the number of polyhedral cells in the piecewise affine partition of the parameter space may increase exponentially. In this paper, we address the problem of finding approximate solutions to MPQP, where the degree of approximation is arbitrary and allows to tradeoff between optimality and a smaller number of cells in the piecewise affine solution. We provide analytic formulas for bounding the errors on the optimal value and the optimizer, and for guaranteeing that the resulting suboptimal RHC law provides closed-loop stability and constraint fulfillment
Jump model learning and filtering for energy end-use disaggregation
No full textEnergy disaggregation aims at reconstructing the power consumed by each electric appliance available in a household from the aggregate power readings collected by a single-point smart meter. With the ultimate goal of fully automatizing this procedure, we first estimate a set of jump models, each of them describing the consumption behaviour of each electric appliance. By representing the total power consumed at the household level as the sum of the outputs of the estimated jump models, a filtering algorithm, based on dynamic programming, is then employed to reconstruct, in an iterative way, the power consumption at an individual appliance level
Piecewise affine regression via recursive multiple least squares and multicategory discrimination
No full textIn nonlinear regression choosing an adequate model structure is often a challenging problem. While simple models (such as linear functions) may not be able to capture the underlying relationship among the variables, over-parametrized models described by a large set of nonlinear basis functions tend to overfit the training data, leading to poor generalization on unseen data. Piecewise-affine (PWA) models can describe nonlinear and possible discontinuous relationships while maintaining simple local affine regressor-to-output mappings, with extreme flexibility when the polyhedral partitioning of the regressor space is learned from data rather than fixed a priori. In this paper, we propose a novel and numerically very efficient two-stage approach for PWA regression based on a combined use of (i) recursive multi-model least-squares techniques for clustering and fitting linear functions to data, and (ii) linear multi-category discrimination, either offline (batch) via a Newton-like algorithm for computing a solution of unconstrained optimization problems with objective functions having a piecewise smooth gradient, or online (recursive) via averaged stochastic gradient descent
Kalman filtering for energy disaggregation
No full textProviding the users information on the energy consumed in the household at the appliance level is of major importance for increasing their awareness of their consumption behavior. In this paper, we propose a technique based on Kalman filters to estimate the devices’ consumption patterns from aggregate readings, i.e., to solve the so called disaggregation problem. The method is suited for on-line disaggregation and the proposed results show that it is robust against modelling errors and unmodelled appliances
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