scholarly journals LMI-Based Model Predictive Control for Underactuated Surface Vessels with Input Constraints

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Lutao Liu ◽  
Zhilin Liu ◽  
Jun Zhang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Matrix ◽  
Control Input ◽  
Input Constraints ◽  
State Dependent ◽  
Operating Region ◽  
Surface Vessels ◽  
Underactuated Surface Vessels ◽  
The Given

A nonlinear model predictive control (MPC) is proposed for underactuated surface vessel (USV) with constrained inputs. Aimed at the special structure of USV, a state-dependent coefficient (SDC) under the given USV is constructed in terms of diffeomorphism and state-dependent Riccati equation (SDRE) theory. Based on linear matrix inequalities (LMIs), the states of the USV are steered into an operating region around zero. When the states reach the region, the control law is switched to stabilize the system. And the constrained control input of the considered system is solved by convex optimization based on MPC involving LMIs. The simulation results verified the effectiveness of the proposed method. It is shown that, based on LMIs, it is easy to get the MPC for the USV with input constraints.

Tube-model predictive control based on sum of squares for hypersonic vehicle with state-dependent input constraints

2021 ◽  
pp. 014233122110465
Author(s):  
Xiaohe Yang ◽  
Weijie Lv ◽  
Chaofang Hu ◽  
Yongtai Hu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Feedback Linearization ◽  
Convex Combination ◽  
Sum Of Squares ◽  
Linear Matrix ◽  
Control Law ◽  
Tube Model ◽  
Input Constraints ◽  
State Dependent

In this paper, tube-model predictive control based on the sum of squares technique is developed for hypersonic vehicles with state-dependent input constraints. Firstly, the longitudinal non-linear model in the presence of uncertain parameters is transformed into the polytopic linear parameter varying model with bounded disturbance by feedback linearization. Then the actual input constraints are converted to the virtual state-dependent input constraints in linear multivariable polynomial. A composite feedback control law based on tube-model predictive control is designed into a convex combination of unconstrained and constrained control. The real control law can be obtained by inversion. The sum of squares technique is used to transform the polynomial constraints into the convex matrix sum of squares condition via linear matrix inequality. Finally, simulation results verify the effectiveness of the proposed controller.

scholarly journals Model Predictive Control of Piecewise Affine System with Constrained Input and Time Delay

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Zhilin Liu ◽  
Lutao Liu ◽  
Jun Zhang ◽  
Xin Yuan
Keyword(s):  
Time Delay ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Infinite Horizon ◽  
Linear Matrix ◽  
Control Input ◽  
Piecewise Affine ◽  
Operating Region ◽  
Current Operating ◽  
Constrained Input

A model predictive control (MPC) is proposed for the piecewise affine (PWA) systems with constrained input and time delay. The corresponding operating region of the considered systems in state space is described as ellipsoid which can be characterized by a set of vector inequalities. And the constrained control input of the considered systems is solved in terms of linear matrix inequalities (LMIs). An MPC controller is designed that will move the PWA system with time delay from the current operating point to the desired one. Multiple objective functions are used to relax the monotonically decreasing condition of the Lyapunov function when the control algorithm switches from a quasi-infinite horizon to an infinite horizon strategy. The simulation results verify the effectiveness of the proposed method. It is shown that, based on LMI constraints, it is easy to get the MPC for the PWA systems with time delay. Moreover, it is suitable for practical application.

scholarly journals A Robust Fault-Tolerant Predictive Control for Discrete-Time Linear Systems Subject to Sensor and Actuator Faults

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2307
Author(s):  
Sofiane Bououden ◽  
Ilyes Boulkaibet ◽  
Mohammed Chadli ◽  
Abdelaziz Abboudi
Keyword(s):  
Model Predictive Control ◽  
Linear Systems ◽  
Predictive Control ◽  
Robust Stability ◽  
Discrete Time ◽  
Fault Tolerant ◽  
Linear Matrix ◽  
Input Constraints ◽  
Actuator Faults ◽  
Time Linear

In this paper, a robust fault-tolerant model predictive control (RFTPC) approach is proposed for discrete-time linear systems subject to sensor and actuator faults, disturbances, and input constraints. In this approach, a virtual observer is first considered to improve the observation accuracy as well as reduce fault effects on the system. Then, a real observer is established based on the proposed virtual observer, since the performance of virtual observers is limited due to the presence of unmeasurable information in the system. Based on the estimated information obtained by the observers, a robust fault-tolerant model predictive control is synthesized and used to control discrete-time systems subject to sensor and actuator faults, disturbances, and input constraints. Additionally, an optimized cost function is employed in the RFTPC design to guarantee robust stability as well as the rejection of bounded disturbances for the discrete-time system with sensor and actuator faults. Furthermore, a linear matrix inequality (LMI) approach is used to propose sufficient stability conditions that ensure and guarantee the robust stability of the whole closed-loop system composed of the states and the estimation error of the system dynamics. As a result, the entire control problem is formulated as an LMI problem, and the gains of both observer and robust fault-tolerant model predictive controller are obtained by solving the linear matrix inequalities (LMIs). Finally, the efficiency of the proposed RFTPC controller is tested by simulating a numerical example where the simulation results demonstrate the applicability of the proposed method in dealing with linear systems subject to faults in both actuators and sensors.

scholarly journals Model Based Robust Predictive Control of Ship Roll/Yaw Motions with Input Constraints

2020 ◽  
Vol 10 (10) ◽  
pp. 3377
Author(s):  
Zhongjia Jin ◽  
Sheng Liu ◽  
Lincheng Jin ◽  
Wei Chen ◽  
Weilin Yang
Keyword(s):  
Predictive Control ◽  
State Feedback ◽  
State Feedback Control ◽  
Linear Matrix ◽  
Control Input ◽  
Input Constraints ◽  
Control Performance ◽  
Parameter Uncertainties ◽  
Yaw Control ◽  
Control Gain

A robust H∞-type state feedback model predictive control (H∞-SFMPC) with input constraints is proposed to optimize the control performance during the ship sailing. Specifically, the approach employed in this paper is able to optimize the closed-loop performance with respect to an H∞-type cost function which predicts the system performance based on the actual model instead of the ideal model. As a result, the effect caused by disturbances is attenuated. The state feedback control gain for the control input of the rudder-fin joint roll/yaw control system is obtained by solving a constrained convex optimization problem in terms of linear matrix inequalities. Simulations are carried out, which reveal that the proposed approach has outstanding control performance. Furthermore, it is found that the approach also has significant robustness with respect to parameter uncertainties.

scholarly journals Synchronization Control of Dynamic Positioning Ships Using Model Predictive Control

2021 ◽  
Vol 9 (11) ◽  
pp. 1239
Author(s):  
Cheng Liu ◽  
Ting Sun ◽  
Qizhi Hu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Lyapunov Equation ◽  
Synchronization Control ◽  
Dynamic Positioning ◽  
Control Input ◽  
Input Constraints ◽  
Position And Orientation ◽  
Simulation Time

Underway replenishment is essential for ships performing long-term missions at sea, which can be formulated into the problem of leader-tracking configuration. Not only the position and orientation but also the velocities are required to be controlled for ensuring the synchronization; additionally, the control inputs are constrained. On this basis, in this paper, a novel synchronization controller on account of model predictive control (MPC) for dynamic positioning (DP) ships is devised to achieve underway replenishment. Firstly, a novel synchronization controller based on MPC is devised to ensure the synchronization of not only the position and orientation but the velocities; furthermore, it is a beneficial solution for its advantages in handling the control input constraints ignored in most studies of underway replenishment. Secondly, a neurodynamic optimization system is applied to the implementation of MPC, which can improve the computational efficiency and shorten the simulation time. Thirdly, stability, frequently neglected by traditional MPC, is ensured by the means of adding a terminal cost function exported from the Lyapunov equation into the objective function. Finally, the effectiveness and advantages of the proposed control design are illustrated by extensive simulations.

scholarly journals Model Predictive Control of Uninterruptible Power Supply with Robust Disturbance Observer

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2871 ◽  
Author(s):  
Yahya Danayiyen ◽  
Kyungsuk Lee ◽  
Minho Choi ◽  
Young Il Lee
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Disturbance Observer ◽  
Controller Design ◽  
Linear Matrix ◽  
Control Input ◽  
Continuous Control ◽  
Model Uncertainties ◽  
Three Phase ◽  
Uninterruptible Power

This paper presents a robust continuous control set model predictive control (CCS-MPC) method to control the output voltage of a three-phase inverter in uninterruptible power supplies (UPS). A robust disturbance observer (DOB) is proposed to estimate the load current of the three-phase UPS without a steady-state error, taking the effect of model uncertainties into account. A CCS-MPC is designed using the DOB for reference voltage tracking purpose, and input constraints are considered in the controller design to calculate the optimal control input. Model uncertainties are defined using polytopic uncertainty class, and a linear matrix inequality (LMI) optimization method is used to compute the optimal observer gain matrix. Another robust controller (RC) is designed based on the DOB and compared with CCS-MPC. The effectiveness of the proposed method (the DOB based CCS-MPC) is evaluated for resistive, inductive, and nonlinear loads then compared with other control methods using a three-phase 5-KVA laboratory experiment UPS system.

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