scholarly journals Introducing Dynamic Programming and Persistently Exciting into Data-Driven Model Predictive Control

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hong Jianwang ◽  
Ricardo A. Ramirez-Mendoza ◽  
Ruben Morales-Menendez
Keyword(s):  
Dynamic Programming ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Minimum Principle ◽  
Short Note ◽  
Optimization Theory ◽  
Inequality Constraint ◽  
Data Driven ◽  
Control Input ◽  
Control Scheme

In this paper, one new data-driven model predictive control scheme is proposed to adjust the varying coupling conditions between different parts of the system; it means that each group of linked subsystems is grouped as data-driven scheme, and this group is independently controlled through a decentralized model predictive control scheme. After combing coalitional scheme and model predictive control, coalitional model predictive control is used to design each controller, respectively. As the dynamic programming is only used in optimization theory, to extend its advantage in control theory, the idea of dynamic programming is applied to analyze the minimum principle and stability for the data-driven model predictive control. Further, the goal of this short note is to bridge the dynamic programming with model predictive control. Through adding the inequality constraint to the constructed model predictive control, one persistently exciting data-driven model predictive control is obtained. The inequality constraint corresponds to the condition of persistent excitation, coming from the theory of system identification. According to the numerical optimization theory, the necessary optimality condition is applied to acquire the optimal control input. Finally, one simulation example is used to prove the efficiency of our proposed theory.

scholarly journals Dynamic Programming in Data Driven Model Predictive Control

2021 ◽  
Vol 20 ◽  
pp. 170-177
Author(s):  
Wang Jianhong
Keyword(s):  
Optimal Control ◽  
Dynamic Programming ◽  
Model Predictive Control ◽  
Cost Function ◽  
Predictive Control ◽  
Short Note ◽  
Data Driven ◽  
Programming System ◽  
Control Sequence ◽  
Actual Output

In this short note, one data driven model predictive control is studied to design the optimal control sequence. The idea of data driven means the actual output value in cost function for model predictive control is identi_ed through input-output observed data in case of unknown but bounded noise and martingale di_erence sequence. After substituting the identi_ed actual output in cost function, the total cost function in model predictive control is reformulated as the other standard form, so that dynamic programming can be applied directly. As dynamic programming is only used in optimization theory, so to extend its advantage in control theory, dynamic programming algorithm is proposed to construct the optimal control sequence. Furthermore, stability analysis for data drive model predictive control is also given based on dynamic programming strategy. Generally, the goal of this short note is to bridge the dynamic programming, system identi_cation and model predictive control. Finally, one simulation example is used to prove the e_ciency of our proposed theory

scholarly journals Shipboard Power Management for Failure Mode Using the Hybrid MPC Approach

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2915
Author(s):  
Xiuyan Peng ◽  
Bo Wang ◽  
Lanyong Zhang ◽  
Peng Su
Keyword(s):  
Model Predictive Control ◽  
Power Systems ◽  
Power Management ◽  
Failure Mode ◽  
Hybrid Model ◽  
Predictive Control ◽  
Minimum Principle ◽  
Management Control ◽  
Shipboard Power ◽  
Two Stages

Shipboard integrated power systems, the key technology of ship electrification, call for effective failure mode power management control strategy to achieve the safe and reliable operation in dynamic reconfiguration. Considering switch reconfiguration with system dynamics and power balance restoration after reconfiguration, in this paper, the optimization objective function of optimal management for ship failure mode is established as a hybrid model predictive control problem from the perspective of hybrid system. To meet the needs for fast computation, a hierarchical hybrid model predictive control algorithm is proposed, which divides the original optimization problem into two stages, and reduces the computation complexity by relaxing constraints and the minimum principle. By applying to a real-time simulator in two scenarios, the results verify the effectivity of the proposed method.

scholarly journals A nonlinear tracking model predictive control scheme for dynamic target signals

Automatica ◽  
2020 ◽  
Vol 118 ◽  
pp. 109030 ◽  
Author(s):  
Johannes Köhler ◽  
Matthias A. Müller ◽  
Frank Allgöwer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Scheme ◽  
Nonlinear Tracking ◽  
Tracking Model ◽  
Dynamic 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.

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