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.

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.

scholarly journals Stochastic Time-Varying Model Predictive Control for Trajectory Tracking of a Wheeled Mobile Robot

2021 ◽  
Vol 9 ◽  
Author(s):  
Weijiang Zheng ◽  
Bing Zhu
Keyword(s):  
Model Predictive Control ◽  
Mobile Robot ◽  
Predictive Control ◽  
Linear Time ◽  
Tracking Error ◽  
Wheeled Mobile Robot ◽  
Reference Trajectory ◽  
Time Varying ◽  
Probability Constraints ◽  
And Control

In this paper, a stochastic model predictive control (MPC) is proposed for the wheeled mobile robot to track a reference trajectory within a finite task horizon. The wheeled mobile robot is supposed to subject to additive stochastic disturbance with known probability distribution. It is also supposed that the mobile robot is subject to soft probability constraints on states and control inputs. The nonlinear mobile robot model is linearized and discretized into a discrete linear time-varying model, such that the linear time-varying MPC can be applied to forecast and control its future behavior. In the proposed stochastic MPC, the cost function is designed to penalize its tracking error and energy consumption. Based on quantile techniques, a learning-based approach is applied to transform the probability constraints to deterministic constraints, and to calculate the terminal constraint to guarantee recursive feasibility. It is proved that, with the proposed stochastic MPC, the tracking error of the closed-loop system is asymptotically average bounded. A simulation example is provided to support the theoretical result.

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.

ON MODEL PREDICTIVE CONTROL OF QUASI-RESONANT CONVERTERS

2009 ◽  
Vol 18 (07) ◽  
pp. 1167-1183 ◽  
Author(s):  
FARZAD TAHAMI ◽  
MEHDI EBAD
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Models ◽  
Control Strategies ◽  
Sampling Interval ◽  
Control Synthesis ◽  
Resonant Converters ◽  
Control Input ◽  
Model Approximation ◽  
Dynamic Matrix

In this paper, different model predictive control synthesis frameworks are examined for DC–DC quasi-resonant converters in order to achieve stability and desired performance. The performances of model predictive control strategies which make use of different forms of linearized models are compared. These linear models are ranging from a simple fixed model, linearized about a reference steady state to a weighted sum of different local models called multi model predictive control. A more complicated choice is represented by the extended dynamic matrix control in which the control input is determined based on the local linear model approximation of the system that is updated during each sampling interval, by making use of a nonlinear model. In this paper, by using and comparing these methods, a new control scheme for quasi-resonant converters is described. The proposed control strategy is applied to a typical half-wave zero-current switching QRC. Simulation results show an excellent transient response and a good tracking for a wide operating range and uncertainties in modeling.

Settling Control of the Triple-Stage Hard Disk Drives Using Robust Output Feedback Model Predictive Control

2016 ◽  
Author(s):  
Huy Nguyen ◽  
Omid Bagherieh ◽  
Roberto Horowitz
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Output Feedback ◽  
Frequency Control ◽  
Hard Disk Drives ◽  
Low Frequency ◽  
Hard Disk ◽  
Control Input ◽  
Feedback Model ◽  
Actuator Constraints

Track settling control for a hard disk drive with three actuators has been considered. The objective is to settle the read/write head on a specific track by following the minimum jerk trajectory. Robust output feedback model predictive control methodology has been utilized for the control design which can satisfy actuator constraints in the presence of noises and disturbances in the system. The controller is designed based on a low order model of the system and has been applied to a higher order plant in order to consider the model mismatch at high frequencies. Since the settling control generally requires a relatively low frequency control input, simulation result shows that the head can be settled on the desired track with 10 percent of track pitch accuracy while satisfying actuator constraints.

scholarly journals Plant and Controller Optimization for Power and Energy Systems With Model Predictive Control

2021 ◽  
Vol 143 (8) ◽  
Author(s):  
Donald J. Docimo ◽  
Ziliang Kang ◽  
Kai A. James ◽  
Andrew G. Alleyne
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Hybrid Electric Vehicle ◽  
Performance Metrics ◽  
Control Strategies ◽  
Transportation Systems ◽  
Thermal Dynamics ◽  
Plant Optimization ◽  
Power And Energy Systems ◽  
And Control

Abstract This article explores the optimization of plant characteristics and controller parameters for electrified mobility. Electrification of mobile transportation systems, such as automobiles and aircraft, presents the ability to improve key performance metrics such as efficiency and cost. However, the strong bidirectional coupling between electrical and thermal dynamics within new components creates integration challenges, increasing component degradation, and reducing performance. Diminishing these issues requires novel plant designs and control strategies. The electrified mobility literature provides prior studies on plant and controller optimization, known as control co-design (CCD). A void within these studies is the lack of model predictive control (MPC), recognized to manage multi-domain dynamics for electrified systems, within CCD frameworks. This article addresses this through three contributions. First, a thermo-electromechanical hybrid electric vehicle (HEV) powertrain model is developed that is suitable for both plant optimization and MPC. Second, simultaneous plant and controller optimization is performed for this multi-domain system. Third, MPC is integrated within a CCD framework using the candidate HEV powertrain model. Results indicate that optimizing both the plant and MPC parameters simultaneously can reduce physical component sizes by over 60% and key performance metric errors by over 50%.

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