scholarly journals Disturbance modeling and state estimation for offset-free predictive control with state-space process models

2014 ◽  
Vol 24 (2) ◽  
pp. 313-323 ◽  
Author(s):  
Piotr Tatjewski
Keyword(s):  
State Estimation ◽  
State Space ◽  
Predictive Control ◽  
Controller Design ◽  
State Space Model ◽  
Process Models ◽  
State Observers ◽  
Constant State ◽  
Linear State ◽  
Augmented State

Abstract Disturbance modeling and design of state estimators for offset-free Model Predictive Control (MPC) with linear state-space process models is considered in the paper for deterministic constant-type external and internal disturbances (modeling errors). The application and importance of constant state disturbance prediction in the state-space MPC controller design is presented. In the case with a measured state, this leads to the control structure without disturbance state observers. In the case with an unmeasured state, a new, simpler MPC controller-observer structure is proposed, with observation of a pure process state only. The structure is not only simpler, but also with less restrictive applicability conditions than the conventional approach with extended process-and-disturbances state estimation. Theoretical analysis of the proposed structure is provided. The design approach is also applied to the case with an augmented state-space model in complete velocity form. The results are illustrated on a 2×2 example process problem.

scholarly journals Offset-free nonlinear Model Predictive Control with state-space process models

2017 ◽  
Vol 27 (4) ◽  
pp. 595-615 ◽  
Author(s):  
Piotr Tatjewski
Keyword(s):  
Model Predictive Control ◽  
State Estimation ◽  
State Space ◽  
Predictive Control ◽  
Conventional Approach ◽  
Process Models ◽  
External Disturbances ◽  
Disturbance Estimation ◽  
Constant State ◽  
Linear State

AbstractOffset-free model predictive control (MPC) algorithms for nonlinear state-space process models, with modeling errors and under asymptotically constant external disturbances, is the subject of the paper. The main result of the paper is the presentation of a novel technique based on constant state disturbance prediction. It was introduced originally by the author for linear state-space models and is generalized to the nonlinear case in the paper. First the case with measured state is considered, in this case the technique allows to avoid disturbance estimation at all. For the cases with process outputs measured only and thus the necessity of state estimation, the technique allows the process state estimation only - as opposed to conventional approach of extended process-and-disturbance state estimation. This leads to simpler design with state observer/filter of lower order and, moreover, without the need of a decision of disturbance placement in the model (under certain restrictions), as in the conventional approach. A theoretical analysis of the proposed algorithm is provided, under applicability conditions which are weaker than in the conventional approach. The presented theory is illustrated by simulation results of nonlinear processes, showing competitiveness of the proposed algorithms.

Stewart Platform Model with Uncertain Parameters

2010 ◽  
Vol 164 ◽  
pp. 177-182 ◽  
Author(s):  
Lukas Březina ◽  
Tomáš Březina
Keyword(s):  
State Space ◽  
Controller Design ◽  
State Space Model ◽  
Stewart Platform ◽  
Dynamics Model ◽  
H Infinity ◽  
Uncertain Dynamics ◽  
Six Dof ◽  
Linear State ◽  
Nominal Position

The paper deals with development of uncertain dynamics model of a six DOF parallel mechanism (Stewart platform) suitable for H-infinity controller design. The model is based on linear state space models of the machine obtained by linearization of the SimMechanics model. The linearization is performed for two positions of the machine in its workspace. It is the nominal position and the position where each link of the machine reaches its maximal length. The uncertainties are then represented as differences between parameters of corresponding state-space matrices. The uncertain state space model is then obtained using upper linear fractional transformation. There are also mentioned several notes regarding H-infinity controller designed according to the obtained model.

scholarly journals Nonlinear State–Space Predictive Control with On–Line Linearisation and State Estimation

2015 ◽  
Vol 25 (4) ◽  
pp. 833-847 ◽  
Author(s):  
Maciej Ławryńczuk
Keyword(s):  
State Estimation ◽  
State Space ◽  
Predictive Control ◽  
Control Policy ◽  
Computationally Efficient ◽  
State Observers ◽  
Constant Type ◽  
The Future ◽  
On Line ◽  
Sampling Instant

Abstract This paper describes computationally efficient model predictive control (MPC) algorithms for nonlinear dynamic systems represented by discrete-time state-space models. Two approaches are detailed: in the first one the model is successively linearised on-line and used for prediction, while in the second one a linear approximation of the future process trajectory is directly found on-line. In both the cases, as a result of linearisation, the future control policy is calculated by means of quadratic optimisation. For state estimation, the extended Kalman filter is used. The discussed MPC algorithms, although disturbance state observers are not used, are able to compensate for deterministic constant-type external and internal disturbances. In order to illustrate implementation steps and compare the efficiency of the algorithms, a polymerisation reactor benchmark system is considered. In particular, the described MPC algorithms with on-line linearisation are compared with a truly nonlinear MPC approach with nonlinear optimisation repeated at each sampling instant.

Subspace-Based Indirect Adaptive State-Space Model Predictive Control

2012 ◽  
Vol 246-247 ◽  
pp. 311-316
Author(s):  
Xiao Suo Luo
Keyword(s):  
Model Predictive Control ◽  
State Space ◽  
Predictive Control ◽  
Control Method ◽  
State Space Model ◽  
System Model ◽  
Subspace Identification ◽  
Space Model ◽  
Predictive Controller ◽  
Linear State

In order to deal with nonlinear, time-varying and disturbance-involved characteristics in the practical industrial processes, an indirect adaptive state-space MPC (model predictive control) method based on subspace identification is proposed. The state-space model, obtained through the POMOESP (Past Output MOESP, MOESP is one form of the subspace identification methods) algorithm, is regarded as the system model. Then, this model is used to design the model predictive controller that involves the solution of a quadratic programming problem to constraints. This controller is applied to the process control simulation on a 2-CSTR. Through comparisons of performance with a linear state-space MPC scheme, the superiority of the proposed control method is illustrated.

A non-linear estimation and model predictive control algorithm based on ant colony optimization

2019 ◽  
Vol 41 (4) ◽  
pp. 1123-1138 ◽  
Author(s):  
Hadi Nobahari ◽  
Saeed Nasrollahi
Keyword(s):  
Model Predictive Control ◽  
State Estimation ◽  
State Space ◽  
Predictive Control ◽  
The State ◽  
Ant Colony ◽  
Ant Colony System ◽  
Non Linear ◽  
Augmented State ◽  
And Control

A new heuristic controller, called the continuous ant colony controller, is proposed for non-linear stochastic Gaussian/non-Gaussian systems. The new controller formulates the state estimation and the model predictive control problems as a single stochastic dynamic optimization problem, and utilizes a colony of virtual ants to find and track the best estimated state and the best control signal. For this purpose, an augmented state space is defined. An integrated cost function is also defined to evaluate the points of the augmented state space, explored by the ants. This function minimizes simultaneously the state estimation error, tracking error, control effort and control smoothness. Ants search the augmented state space dynamically in a similar scheme to the optimization algorithm, known as the continuous ant colony system. Performance of the new model predictive controller is evaluated for three non-linear problems. The problems are a non-linear continuous stirred tank reactor, a non-linear cart and spring system, and the attitude control of a non-linear quadrotor. The results verify successful performance of the proposed algorithm from both estimation and control points of view.

Optimal State-Space Control of a Gas Turbine Engine

1992 ◽  
Vol 114 (4) ◽  
pp. 763-767 ◽  
Author(s):  
J. W. Watts ◽  
T. E. Dwan ◽  
C. G. Brockus
Keyword(s):  
State Space ◽  
Gas Turbine ◽  
State Space Model ◽  
Gas Turbine Engine ◽  
The State ◽  
Operating Conditions ◽  
Turbine Engine ◽  
Analog Control ◽  
Linear State ◽  
High Level

An analog fuel control for a gas turbine engine was compared with several state-space derived fuel controls. A single-spool, simple cycle gas turbine engine was modeled using ACSL (high level simulation language based on FORTRAN). The model included an analog fuel control representative of existing commercial fuel controls. The ACSL model was stripped of nonessential states to produce an eight-state linear state-space model of the engine. The A, B, and C matrices, derived from rated operating conditions, were used to obtain feedback control gains by the following methods: (1) state feedback; (2) LQR theory; (3) Bellman method; and (4) polygonal search. An off-load transient followed by an on-load transient was run for each of these fuel controls. The transient curves obtained were used to compare the state-space fuel controls with the analog fuel control. The state-space fuel controls did better than the analog control.

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