scholarly journals Model-Predictive Control and Closed-Loop Stability Considerations for Nonlinear Plants Described by Local ARX-Type Models

2013 ◽  
Author(s):  
Michael E. Cholette ◽  
Dragan Djurdjanovic
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Optimization Problem ◽  
Closed Loop ◽  
Type Model ◽  
Control Input ◽  
Dual Purpose ◽  
Nonlinear Plants ◽  
And Control ◽  
Local Input

In this paper, a model-predictive control (MPC) method is detailed for the control of nonlinear systems with stability considerations. It will be assumed that the plant is described by a local input/output ARX-type model, with the control potentially included in the premise variables, which enables the control of systems that are nonlinear in both the state and control input. Additionally, for the case of set point regulation, a suboptimal controller is derived which has the dual purpose of ensuring stability and enabling finite-iteration termination of the iterative procedure used to solve the nonlinear optimization problem that is used to determine the control signal.

scholarly journals Modeling and Control of Crane Overload Protection During Marine Lifting Operation Based on Model Predictive Control

2017 ◽  
Author(s):  
Zhengru Ren ◽  
Roger Skjetne ◽  
Zhen Gao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Degrees Of Freedom ◽  
Multiple Shooting ◽  
Control Input ◽  
Lumped Mass ◽  
Modeling And Control ◽  
Overload Protection ◽  
And Control

This paper deals with a nonlinear model predictive control (NMPC) scheme for a winch servo motor to overcome the sudden peak tension in the lifting wire caused by a lumped-mass payload at the beginning of a lifting off or a lowering operation. The crane-wire-payload system is modeled in 3 degrees of freedom with the Newton-Euler approach. Direct multiple shooting and real-time iteration (RTI) scheme are employed to provide feedback control input to the winch servo. Simulations are implemented with MATLAB and CaSADi toolkit. By well tuning the weighting matrices, the NMPC controller can reduce the snatch loads in the lifting wire and the winch loads simultaneously. A comparative study with a PID controller is conducted to verify its performance.

Vibration Control of a Flexible Beam Using Model Predictive Control

2012 ◽  
Vol 457-458 ◽  
pp. 1299-1304
Author(s):  
Jun Feng Hu ◽  
Da Chang Zhu ◽  
Qiang Chen
Keyword(s):  
Model Predictive Control ◽  
State Space ◽  
Predictive Control ◽  
Optimization Problem ◽  
External Disturbance ◽  
The State ◽  
Measurement Noise ◽  
Flexible Beam ◽  
Control Input ◽  
State Variables

Model predictive control is applied to suppress the vibration of a flexible link with piezoelectric actuators and strain gage transducer. The state-space dynamic model of the system was derived by using finite element method and experimental modal test. On the basis of the model, model predictive controller is designed taking into account the uncertain disturbance and measurement noise. The discrete prediction model is derived from the state-space equation of the system, and the future output is obtained from the model. The uncertain external disturbance and measurement noise are white noise signal, the Kalman filter estimator is designed to estimate the state variables of the system. A standard quadratic programming optimization problem is formed where the performance index function minimizes a quadratic performance function that trades off controller performance and control effort. The constraints are the control input voltage and its change rate. Finally, the optimization problem is solved to obtain the optimal control output. A MIMO control system is built using dSPACE DS1103 platform, and experimental tests are performed. The performances of the controller are verified experimentally. The results of experiment show the effectiveness of the controller.

scholarly journals Model Predictive Control of Stochastic Linear Systems with Probability Constraints

2018 ◽  
Vol 13 (6) ◽  
pp. 927-937
Author(s):  
Constantin F. Caruntu ◽  
Cristian C. Velandia-Cardenas ◽  
Xinghua Liu ◽  
Alessandro Vargas
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Cumulative Probability ◽  
Control Input ◽  
System State ◽  
Efficient Manner ◽  
Computing Model ◽  
Probability Constraints ◽  
And Control ◽  
Stochastic Linear Systems

This paper presents a strategy for computing model predictive control of linear Gaussian noise systems with probability constraints. As usual, constraints are taken on the system state and control input. The novelty relies on setting bounds on the underlying cumulative probability distribution, and showing that the model predictive control can be computed in an efficient manner through these novel bounds— an application confirms this assertion. Indeed real-time experiments were carried out to control a direct current (DC) motor. The corresponding data show the effectiveness and usefulness of the approach.

Ellipsoidal Lyapunov-based hybrid model predictive control for mixed logical dynamical systems with a recursive feasibility guarantee

2018 ◽  
Vol 41 (9) ◽  
pp. 2475-2487
Author(s):  
Alireza Olama ◽  
Mokhtar Shasadeghi ◽  
Amin Ramezani ◽  
Mostafa Khorramizadeh ◽  
Paulo R C Mendes
Keyword(s):  
Dynamical Systems ◽  
Model Predictive Control ◽  
Hybrid Model ◽  
Predictive Control ◽  
Robust Stability ◽  
Optimization Problem ◽  
Closed Loop ◽  
Loop System ◽  
Closed Loop System ◽  
Mixed Logical Dynamical

This paper proposes an ellipsoidal hybrid model predictive control approach to solve the robust stability problem of uncertain hybrid dynamical systems modelled by the mixed logical dynamical framework. In this approach, the traditional terminal equality constraint is replaced by an ellipsoid that results in a maximal positive invariant set for the closed-loop system. Then, a Lyapunov decreasing condition along with the robustness criterion is introduced to the optimization problem to achieve the robust stability of the closed-loop system. As the main advantages, the ellipsoidal terminal set proposed in this paper attains a larger domain of attraction along with the recursive feasibility guarantee. Moreover, the stability and robustness constraints are achieved by a lower prediction horizon, which leads to a smaller dimension optimization problem. In addition, to reduce the computational complexity of the corresponding optimization problem, a suboptimal version of the proposed algorithm is introduced. Finally, numerical and car suspension system examples show the capabilities of the proposed method.

scholarly journals Missile IGC Based on Improved Model Predictive Control and Sliding Mode Observer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Li Ma ◽  
Jiayuan Shan ◽  
Junhui Liu ◽  
Yan Ding
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Sliding Mode ◽  
Lyapunov Stability Theory ◽  
Sliding Mode Observer ◽  
Control Input ◽  
Guidance And Control ◽  
Optimizing Control ◽  
Improved Model ◽  
And Control

Considering recurrent optimization process in model predictive control (MPC), the model uncertainties and disturbances terms in the missile’s guidance and control model can degrade recursive feasibility, and there are control mutation problems in common MPC algorithm. This paper presents a disturbance rejection model predictive control algorithm for missile integrated guidance and control (IGC). Firstly, a sliding mode observer (SMDO) is designed to estimate the unknown disturbances caused by target maneuvering. Secondly, the method of optimizing control increment is adopted in MPC to avoid the phenomenon of control mutation in the model calculation. By limiting the control increment in each cycle, it ensures the continuity of the control input. Thirdly, by combining the SMDO and MPC, an IGC algorithm is presented, and the stability of the algorithm is proved by using Lyapunov stability theory. Finally, the simulation results with different impact angles verify the effectiveness of the proposed algorithm for intercepting maneuver target.

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