31,218 research outputs found
Stability of Constrained Adaptive Model Predictive Control Algorithms
Recently, suboptimality estimates for model predictive controllers (MPC) have
been derived for the case without additional stabilizing endpoint constraints
or a Lyapunov function type endpoint weight. The proposed methods yield a
posteriori and a priori estimates of the degree of suboptimality with respect
to the infinite horizon optimal control and can be evaluated at runtime of the
MPC algorithm. Our aim is to design automatic adaptation strategies of the
optimization horizon in order to guarantee stability and a predefined degree of
suboptimality for the closed loop solution. Here, we present a stability proof
for an arbitrary adaptation scheme and state a simple shortening and
prolongation strategy which can be used for adapting the optimization horizon.Comment: 6 pages, 2 figure
Extremum Seeking-based Iterative Learning Linear MPC
In this work we study the problem of adaptive MPC for linear time-invariant
uncertain models. We assume linear models with parametric uncertainties, and
propose an iterative multi-variable extremum seeking (MES)-based learning MPC
algorithm to learn on-line the uncertain parameters and update the MPC model.
We show the effectiveness of this algorithm on a DC servo motor control
example.Comment: To appear at the IEEE MSC 201
Data-driven adaptive model-based predictive control with application in wastewater systems
This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based optimisation strategy to formulate the control algorithm and incorporate the input constraints. Both direct adaptive model-based predictive controller (DAMBPC) and indirect adaptive model-based predictive controller (IAMBPC) are considered. In DAMBPC, the direct identification of controller parameters is desired to reduce the design effort and computational load while the IAMBPC involves a two-stage process of model identification and controller design. The former method only requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process input/output data for the identification. A suboptimal SVD-based optimisation technique is proposed to incorporate the input constraints. The proposed techniques are implemented and tested on a fourth order non-linear model of a wastewater system. Simulation results are presented to compare the direct and indirect adaptive methods and to demonstrate the performance of the proposed algorithms
Gaussian process based model predictive control : a thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering, School of Engineering and Advanced Technology, Massey University, New Zealand
The performance of using Model Predictive Control (MPC) techniques is highly dependent
on a model that is able to accurately represent the dynamical system. The datadriven
modelling techniques are usually used as an alternative approach to obtain such
a model when first principle techniques are not applicable. However, it is not easy to
assess the quality of learnt models when using the traditional data-driven models, such
as Artificial Neural Network (ANN) and Fuzzy Model (FM). This issue is addressed in
this thesis by using probabilistic Gaussian Process (GP) models.
One key issue of using the GP models is accurately learning the hyperparameters.
The Conjugate Gradient (CG) algorithms are conventionally used in the problem of
maximizing the Log-Likelihood (LL) function to obtain these hyperparameters. In this
thesis, we proposed a hybrid Particle Swarm Optimization (PSO) algorithm to cope with
the problem of learning hyperparameters. In addition, we also explored using the Mean
Squared Error (MSE) of outputs as the fitness function in the optimization problem.
This will provide us a quality indication of intermediate solutions.
The GP based MPC approaches for unknown systems have been studied in the past
decade. However, most of them are not generally formulated. In addition, the optimization
solutions in existing GP based MPC algorithms are not clearly given or are
computationally demanding. In this thesis, we first study the use of GP based MPC approaches
in the unconstrained problems. Compared to the existing works, the proposed
approach is generally formulated and the corresponding optimization problem is eff-
ciently solved by using the analytical gradients of GP models w.r.t. outputs and control
inputs. The GPMPC1 and GPMPC2 algorithms are subsequently proposed to handle
the general constrained problems. In addition, through using the proposed basic and
extended GP based local dynamical models, the constrained MPC problem is effectively
solved in the GPMPC1 and GPMPC2 algorithms. The proposed algorithms are verified
in the trajectory tracking problem of the quadrotor.
The issue of closed-loop stability in the proposed GPMPC algorithm is addressed
by means of the terminal cost and constraint technique in this thesis. The stability
guaranteed GPMPC algorithm is subsequently proposed for the constrained problem. By
using the extended GP based local dynamical model, the corresponding MPC problem
is effectively solved
Gaussian process model based predictive control
Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark
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
Model predictive control techniques for hybrid systems
This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de EduaciĂłn y Ciencia DPI2007-66718-C04-01Ministerio de EduaciĂłn y Ciencia DPI2008-0581
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