4,309 research outputs found
Non-linear predictive control for manufacturing and robotic applications
The paper discusses predictive control algorithms in the context of applications to robotics and manufacturing systems. Special features of such systems, as compared to traditional process control applications, require that the algorithms are capable of dealing with faster dynamics, more significant unstabilities and more significant contribution of non-linearities to the system performance. The paper presents the general framework for state-space design of predictive algorithms. Linear algorithms are introduced first, then, the attention moves to non-linear systems. Methods of predictive control are presented which are based on the state-dependent state space system description. Those are illustrated on examples of rather difficult mechanical systems
SlijeÄenje reference s ograniÄenjima zasnovano na homotetiÄnim skupovima
In this paper, we consider the problem of constrained tracking of piecewise constant references for nonlinear dynamical systems. In the considered problem we assume that an existing controller satisfies constraints in a corresponding positive-invariant set of the system. To solve the problem we propose the use of homothetic transformations of the positive-invariant set to modify the existing control law. The proposed approach can be implemented as a tracking model predictive control or as a reference governor. Simulation and experimental results are provided, showing the applicability of the proposed approach to a class of nonlinear systems.U radu se razmatra problem slijeÄenja reference s ograniÄenjima za nelinearne dinamiÄke sustave. Polazna je pretpostavka da postojeÄi zakon upravljanja zadovoljava ograniÄenja u pripadnom invarijantom skupu sustava. Uz takvu pretpostavku u radu se predlaĆŸe primjena homotetiÄne transformacije invarijantnih skupova kako bi se izmjenio postojeÄi zakon upravljanja. PredloĆŸeni pristup se moĆŸe primjeniti u sklopu modelskog prediktivnog upravljanja za slijeÄenje reference ili samostalno za oblikovanje reference. Dani su simulacijski i eksperimentalni rezultati koji pokazuju primjenjivost predloĆŸene metode za klasu nelinearnih sustava
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
Robust Adaptive Model Predictive Control: Performance and Parameter Estimation
For systems with uncertain linear models, bounded additive disturbances and
state and control constraints, a robust model predictive control algorithm
incorporating online model adaptation is proposed. Sets of model parameters are
identified online and employed in a robust tube MPC strategy with a nominal
cost. The algorithm is shown to be recursively feasible and input-to-state
stable. Computational tractability is ensured by using polytopic sets of fixed
complexity to bound parameter sets and predicted states. Convex conditions for
persistence of excitation are derived and are related to probabilistic rates of
convergence and asymptotic bounds on parameter set estimates. We discuss how to
balance conflicting requirements on control signals for achieving good tracking
performance and parameter set estimate accuracy. Conditions for convergence of
the estimated parameter set are discussed for the case of fixed complexity
parameter set estimates, inexact disturbance bounds and noisy measurements
Robust Optimal Control for Nonlinear Systems with Parametric Uncertainties via System Level Synthesis
This paper addresses the problem of optimally controlling nonlinear systems
with norm-bounded disturbances and parametric uncertainties while robustly
satisfying constraints. The proposed approach jointly optimizes a nominal
nonlinear trajectory and an error feedback, requiring minimal offline design
effort and offering low conservatism. This is achieved by decomposing the
affine-in-the-parameter uncertain nonlinear system into a nominal
system and an uncertain linear time-varying system. Using
this decomposition, we can apply established tools from system level synthesis
to over-bound all uncertainties in the nonlinear
optimization problem. Moreover, it enables tight joint optimization of the
linearization error bounds, parametric uncertainties bounds, nonlinear
trajectory, and error feedback. With this novel controller parameterization, we
can formulate a convex constraint to ensure robust performance guarantees for
the nonlinear system. The presented method is relevant for numerous
applications related to trajectory optimization, e.g., in robotics and
aerospace engineering. We demonstrate the performance of the approach and its
low conservatism through the simulation example of a post-capture satellite
stabilization.Comment: Accepted for CDC (Singapore, 13-15 December 2023). Code:
https://gitlab.ethz.ch/ics/nonlinear-parametric-SL
Dynamic dependence networks: Financial time series forecasting and portfolio decisions (with discussion)
We discuss Bayesian forecasting of increasingly high-dimensional time series,
a key area of application of stochastic dynamic models in the financial
industry and allied areas of business. Novel state-space models characterizing
sparse patterns of dependence among multiple time series extend existing
multivariate volatility models to enable scaling to higher numbers of
individual time series. The theory of these "dynamic dependence network" models
shows how the individual series can be "decoupled" for sequential analysis, and
then "recoupled" for applied forecasting and decision analysis. Decoupling
allows fast, efficient analysis of each of the series in individual univariate
models that are linked-- for later recoupling-- through a theoretical
multivariate volatility structure defined by a sparse underlying graphical
model. Computational advances are especially significant in connection with
model uncertainty about the sparsity patterns among series that define this
graphical model; Bayesian model averaging using discounting of historical
information builds substantially on this computational advance. An extensive,
detailed case study showcases the use of these models, and the improvements in
forecasting and financial portfolio investment decisions that are achievable.
Using a long series of daily international currency, stock indices and
commodity prices, the case study includes evaluations of multi-day forecasts
and Bayesian portfolio analysis with a variety of practical utility functions,
as well as comparisons against commodity trading advisor benchmarks.Comment: 31 pages, 9 figures, 3 table
Online learning constrained model predictive control based on double prediction
A data-based predictive controller is proposed, offering both robust stability
guarantees and online learning capabilities. To merge these two properties in
a single controller, a double-prediction approach is taken. On the one hand,
a safe prediction is computed using Lipschitz interpolation on the basis of an
offline identification dataset, which guarantees safety of the controlled system.
On the other hand, the controller also benefits from the use of a second online
learning-based prediction as measurements incrementally become available
over time. Sufficient conditions for robust stability and constraint satisfaction
are given. Illustrations of the approach are provided in a simulated case studyFeder (UE) DPI2016â76493âC3â1âRUniversidad de Sevilla VIâPPITMinisterio de EconomĂa y Competitividad (MINECO). España DPI2016â76493âC3â1â
Heterogeneous Impacts Of Cooperatives On Smallholdersâ Commercialization Behavior: Evidence From Ethiopia.
This paper examines the impact of marketing cooperatives on smallholder commercialization of cereals using detailed household data in rural Ethiopia. We use the strong government role in promoting the establishment of cooperatives to justify the use of propensity score matching in order to compare households that are cooperative members to similar households in comparable areas without cooperatives. The analysis reveals that while cooperatives obtain higher prices for their members, they are not associated with a significant increase in the overall share of cereal production sold commercially by their members. However, these average results hide considerable heterogeneity in the impact across households. In particular, we find smaller farmers tend reduce their marketed output as a result of higher prices, while the opposite is true for larger farmers. JEL Classification: Q13, O12Agricultural and Food Policy, Community/Rural/Urban Development, Demand and Price Analysis, Farm Management, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, Institutional and Behavioral Economics, Labor and Human Capital, Marketing, Productivity Analysis, Research and Development/Tech Change/Emerging Technologies,
Combining Prior Knowledge and Data for Robust Controller Design
We present a framework for systematically combining data of an unknown linear
time-invariant system with prior knowledge on the system matrices or on the
uncertainty for robust controller design. Our approach leads to linear matrix
inequality (LMI) based feasibility criteria which guarantee stability and
performance robustly for all closed-loop systems consistent with the prior
knowledge and the available data. The design procedures rely on a combination
of multipliers inferred via prior knowledge and learnt from measured data,
where for the latter a novel and unifying disturbance description is employed.
While large parts of the paper focus on linear systems and input-state
measurements, we also provide extensions to robust output-feedback design based
on noisy input-output data and against nonlinear uncertainties. We illustrate
through numerical examples that our approach provides a flexible framework for
simultaneously leveraging prior knowledge and data, thereby reducing
conservatism and improving performance significantly if compared to black-box
approaches to data-driven control
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