Model Predictive Static Programming for Optimal Command Tracking: A Fast Model Predictive Control Paradigm

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Prem Kumar ◽  
B. Bhavya Anoohya ◽  
Radhakant Padhi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Time Delays ◽  
State Constraints ◽  
Input Parameter ◽  
Simulation Studies ◽  
Control Paradigm ◽  
Real Hardware ◽  
Low Dimensional ◽  
And Control

Inspired by fast model predictive control (MPC), a new nonlinear optimal command tracking technique is presented in this paper, which is named as “Tracking-oriented Model Predictive Static Programming (T-MPSP).” Like MPC, a model-based prediction-correction approach is adopted. However, the entire problem is converted to a very low-dimensional “static programming” problem from which the control history update is computed in closed-form. Moreover, the necessary sensitivity matrices (which are the backbone of the algorithm) are computed recursively. These two salient features make the computational process highly efficient, thereby making it suitable for implementation in real time. A trajectory tracking problem of a two-wheel differential drive mobile robot is presented to validate and demonstrate the proposed philosophy. The simulation studies are very close to realistic scenario by incorporating disturbance input, parameter uncertainty, feedback sensor noise, time delays, state constraints, and control constraints. The algorithm has been implemented on a real hardware and the experimental validation corroborates the simulation results.

Simulation Studies Comparing Feedback Predictive Control to Model Predictive Control for Unmeasured Disturbances in the Artificial Pancreas Application

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Yong Mei ◽  
Trinh Huynh ◽  
Rachel Khor ◽  
Derrick K. Rollins
Keyword(s):  
Standard Deviation ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Artificial Pancreas ◽  
Simulation Studies ◽  
Modeling And Control ◽  
Carbohydrate Consumption ◽  
And Control ◽  
Measured Input

The artificial pancreas (AP) is an electro-mechanical device to control glucose (G) levels in the blood for people with diabetes using mathematical modeling and control system technology. There are many variables not measured and modeled by these devices that affect G levels. This work evaluates the effectiveness of two control systems for the case where critical inputs are unmeasured. This work compares and evaluates two predictive feedback control (FBC) algorithms in two unmeasured input studies. In the first study, the process is a dynamic transfer function model with one measured input variable and one unmeasured input variable. The process for the second study is a diabetes simulator with insulin feed rate (IFR) measured and carbohydrate consumption (CC) unmeasured. The feedback predictive control (FBPC) approach achieved much better control performance than model predictive control (MPC) in both studies. In the first study, MPC was shown to get worse as the process lag increases but FBPC was unaffected by process lag. In the diabetes simulation study, for five surrogate type 1 diabetes subjects, the standard deviation of G about its mean (standard deviation) (i.e., the set point) was 133% larger for MPC relative to FBPC. For FBPC, its standard deviation was less than 10% larger for unmeasured CC versus measured CC. Thus, FBPC appears to be a more effective AP control algorithm than MPC for unmeasured disturbances and may not perform much worse in practice when CC is measured versus when it is unmeasured since CC can be very inaccurate in real situations.

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%.

scholarly journals A Simplified Model Predictive Control for T-Type Inverter with Output LC Filter

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 31 ◽  
Author(s):  
Van-Quang-Binh Ngo ◽  
Minh-Khai Nguyen ◽  
Tan-Tai Tran ◽  
Young-Cheol Lim ◽  
Joon-Ho Choi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Performance Simulation ◽  
Control Scheme ◽  
Finite Control ◽  
Lc Filter ◽  
The Cost ◽  
And Control ◽  
Measurement And Control ◽  
Selection Of

In this paper, a model predictive control scheme for the T-type inverter with an output LC filter is presented. A simplified dynamics model is proposed to reduce the number of the measurement and control variables, resulting in a decrease in the cost and complexity of the system. Furthermore, the main contribution of the paper is the approach to evaluate the cost function. By employing the selection of sector information distribution in the reference inverter voltage and capacitor voltage balancing, the execution time of the proposed algorithm is significantly reduced by 36% compared with conventional model predictive control without too much impact on control performance. Simulation and experimental results are studied and compared with conventional finite control set model predictive control to validate the effectiveness of the proposed method.

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