Level Control of Quadruple Tank Process using Laguerre Functions based Model Predictive Control Algorithm

This paper aims to present a model predictive controller based on discrete state-space modeling, where the future control trajectory is approximated by a set of discrete-time Laguerre functions instead of shift forward operators. The benefit of using these orthonormal Laguerre functions is that they have fewer parameters to adjust in the optimization problem and the computation load is significantly lower than the standard predictive control. The effectiveness of this controller is illustrated through the quadruple tank process, which is a highly interacted, multivariable and constrained system.

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A Simplified Predictive Control of Constrained Markov Jump System with Mixed Uncertainties

Abstract and Applied Analysis ◽

10.1155/2014/475808 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 7

Author(s):

Yanyan Yin ◽

Yanqing Liu ◽

Hamid R. Karimi

Keyword(s):

Predictive Control ◽

Transition Probability ◽

Dynamic Performance ◽

Markov Jump ◽

Markov Jump System ◽

Predictive Controller ◽

Model Predictive Control Algorithm ◽

Simplified Algorithm ◽

Jump Systems ◽

Mixed Uncertainties

A simplified model predictive control algorithm is designed for discrete-time Markov jump systems with mixed uncertainties. The mixed uncertainties include model polytope uncertainty and partly unknown transition probability. The simplified algorithm involves finite steps. Firstly, in the previous steps, a simplified mode-dependent predictive controller is presented to drive the state to the neighbor area around the origin. Then the trajectory of states is driven as expected to the origin by the final-step mode-independent predictive controller. The computational burden is dramatically cut down and thus it costs less time but has the acceptable dynamic performance. Furthermore, the polyhedron invariant set is utilized to enlarge the initial feasible area. The numerical example is provided to illustrate the efficiency of the developed results.

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Trajectory Tracking and Stabilization of a Quadrotor Using Model Predictive Control of Laguerre Functions

Abstract and Applied Analysis ◽

10.1155/2015/916864 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Mapopa Chipofya ◽

Deok Jin Lee ◽

Kil To Chong

Keyword(s):

State Space ◽

Predictive Control ◽

Trajectory Tracking ◽

Linear Quadratic Regulator ◽

Laguerre Functions ◽

Linear Quadratic ◽

Model Predictive Controller ◽

Predictive Controller ◽

Linear State ◽

Quadrotor System

This paper presents a solution to stability and trajectory tracking of a quadrotor system using a model predictive controller designed using a type of orthonormal functions called Laguerre functions. A linear model of the quadrotor is derived and used. To check the performance of the controller we compare it with a linear quadratic regulator and a more traditional linear state space MPC. Simulations for trajectory tracking and stability are performed in MATLAB and results provided in this paper.

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Improved Distributed Model Predictive Control with Control Planning Set

Journal of Control Science and Engineering ◽

10.1155/2016/8167931 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

Wei Chen

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

System Performance ◽

Control Algorithm ◽

Distributed Model ◽

Distributed Model Predictive Control ◽

Exchange Information ◽

Predictive Controller ◽

Model Predictive Control Algorithm ◽

The Stability

We focus on distributed model predictive control algorithm. Each distributed model predictive controller communicates with the others in order to compute the control sequence. But there are not enough communication resources to exchange information between the subsystems because of the limited communication network. This paper presents an improved distributed model predictive control scheme with control planning set. Control planning set algorithm approximates the future control sequences by designed planning set, which can reduce the exchange information among the controllers and can also decrease the distributed MPC controller calculation demand without degrading the whole system performance much. The stability and system performance analysis for distributed model predictive control are given. Simulations of the four-tank control problem and multirobot multitarget tracking problem are illustrated to verify the effectiveness of the proposed control algorithm.

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Mobile Robot Motion Control Using Laguerre-Based Model Predictive Control

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.776.403 ◽

2015 ◽

Vol 776 ◽

pp. 403-410 ◽

Cited By ~ 1

Author(s):

Mapopa Chipofya ◽

Deok Jin Lee ◽

Kil To Chong

Keyword(s):

Mobile Robot ◽

Linear Model ◽

Motion Control ◽

Predictive Control ◽

Robot Motion ◽

Laguerre Functions ◽

Model Predictive Controller ◽

Cartesian System ◽

Predictive Controller ◽

Simulation Results

This paper presents a method of solving the problem of mobile robot motion control using a model predictive controller designed using Laguerre functions. A linear model of the two-wheeled nonholonomic robot is used. This linear model is obtained by converting the nonlinear model in the Cartesian system to a polar one. This change is preferred because it is easier to work with the linear model than its corresponding nonlinear one. Simulation results obtained from MATLAB showing that Laguerre-based MPC (LMPC) performs well are presented.

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Nonlinear model predictive control of spacecraft relative motion

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410018807613 ◽

2018 ◽

Vol 233 (11) ◽

pp. 3906-3919 ◽

Cited By ~ 1

Author(s):

Piotr A Felisiak ◽

Krzysztof S Sibilski ◽

Kaiyu Qin ◽

Gun Li ◽

Wiesław A Wróblewski

Keyword(s):

Model Predictive Control ◽

Motion Control ◽

Predictive Control ◽

Relative Motion ◽

Control Algorithm ◽

Optimization Problem ◽

Control Method ◽

Nonlinear Model Predictive Control ◽

Model Predictive Control Algorithm ◽

Control Trajectory

This investigation deals with the problem of spacecraft relative motion control, which is typically associated with the spacecraft rendezvous and proximity maneuvers. Relative position and linear velocity are considered. A distinguishing attribute of the presented approach is consideration of definitely larger relative distance between the satellites than it is commonly addressed in the literature. The presented control method is applicable in the case where the chief satellite moves in a known, highly elliptical orbit. A quasi-optimal control is found by a model predictive control algorithm, where the nonlinear optimization problem is reduced to quadratic optimization by preliminary estimation of the future control trajectory. Significance of the method has been verified using a computer simulation.

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Modified Model Predictive Control and Optimized Nearest Level Control on Balance of the Submodule Voltage Control for MMC-HVDC

2020 4th International Conference on HVDC (HVDC) ◽

10.1109/hvdc50696.2020.9292751 ◽

2020 ◽

Author(s):

Qing Dang ◽

Ziyang Hong ◽

Zhaoliang Guan ◽

Xinying Wang ◽

Jinkun Ke ◽

...

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Voltage Control ◽

Level Control ◽

Modified Model

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Generalized ESO and Predictive Control Based Robust Autopilot Design

Journal of Control Science and Engineering ◽

10.1155/2016/5741603 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Bhavnesh Panchal ◽

S. E. Talole

Keyword(s):

Predictive Control ◽

Closed Loop ◽

Specific Information ◽

Disturbance Compensation ◽

External Disturbances ◽

Output Tracking ◽

Control Gain ◽

Predictive Controller ◽

Mismatched Uncertainty ◽

Acceleration Tracking

A novel continuous time predictive control and generalized extended state observer (GESO) based acceleration tracking pitch autopilot design is proposed for a tail controlled, skid-to-turn tactical missile. As the dynamics of missile are significantly uncertain with mismatched uncertainty, GESO is employed to estimate the state and uncertainty in an integrated manner. The estimates are used to meet the requirement of state and to robustify the output tracking predictive controller designed for nominal system. Closed loop stability for the controller-observer structure is established. An important feature of the proposed design is that it does not require any specific information about the uncertainty. Also the predictive control design yields the feedback control gain and disturbance compensation gain simultaneously. Effectiveness of GESO in estimation of the states and uncertainties and in robustifying the predictive controller in the presence of parametric uncertainties, external disturbances, unmodeled dynamics, and measurement noise is illustrated by simulation.

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