Feedback linearization-based MIMO model predictive control with defined pseudo-reference for hydrogen regulation of automotive fuel cells

2021 ◽  
Vol 293 ◽  
pp. 116919
Author(s):  
Shengwei Quan ◽  
Ya-Xiong Wang ◽  
Xuelian Xiao ◽  
Hongwen He ◽  
Fengchun Sun
2000 ◽  
Vol 33 (25) ◽  
pp. 191-196 ◽  
Author(s):  
Sandra Piñón ◽  
Miguel Peña ◽  
Carlos Soria ◽  
Benjamín Kuchen

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2182 ◽  
Author(s):  
Alessandro Rosini ◽  
Alessandro Palmieri ◽  
Damiano Lanzarotto ◽  
Renato Procopio ◽  
Andrea Bonfiglio

The new electric power generation scenario, characterized by growing variability due to the greater presence of renewable energy sources (RES), requires more restrictive dynamic requirements for conventional power generators. Among traditional power generators, gas turbines (GTs) can regulate the output electric power faster than any other type of plant; therefore, they are of considerable interest in this context. In particular, the dynamic performance of a GT, being a highly nonlinear and complex system, strongly depends on the applied control system. Proportional–integral–derivative (PID) controllers are the current standard for GT control. However, since such controllers have limitations for various reasons, a model predictive control (MPC) was designed in this study to enhance GT performance in terms of dynamic behavior and robustness to model uncertainties. A comparison with traditional PID-based controllers and alternative model-based control approaches (feedback linearization control) found in the literature demonstrated the effectiveness of the proposed approach.


Author(s):  
A. T. Hafez ◽  
M. Iskandarani ◽  
S. N. Givigi ◽  
S. Yousefi ◽  
Camille Alain Rabbath ◽  
...  

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Xiaobing Kong ◽  
Xiangjie Liu ◽  
Xiuming Yao

Constituting reliable optimal solution is a key issue for the nonlinear constrained model predictive control. Input-output feedback linearization is a popular method in nonlinear control. By using an input-output feedback linearizing controller, the original linear input constraints will change to nonlinear constraints and sometimes the constraints are state dependent. This paper presents an iterative quadratic program (IQP) routine on the continuous-time system. To guarantee its convergence, another iterative approach is incorporated. The proposed algorithm can reach a feasible solution over the entire prediction horizon. Simulation results on both a numerical example and the continuous stirred tank reactors (CSTR) demonstrate the effectiveness of the proposed method.


Author(s):  
Xiaofei Wang ◽  
Zaojian Zou ◽  
Tieshan Li ◽  
Weilin Luo

The control problem of underactuated surface ships and underwater vehicles has attracted more and more attentions during the last years. Path following control aims at forcing the vehicles to converge and follow a desired path. Path following control of underactuated surface ships or underwater vehicles is an important issue to study nonlinear systems control, and it is also important in the practical implementation such as the guidance and control of marine vehicles. This paper proposes two nonlinear model predictive control algorithms to force an underactuated ship to follow a predefined path. One algorithm is based on state space model, the other is based on analytic model predictive control. In the first algorithm, the state space GPC (Generalized Predictive Control) method is used to design the path-following controller of underactuated ships. The nonlinear path following system of underactuated ships is discretized and re-arranged into state space model. Then states are augmented to get the new state space model with control increment as input. Thus the problem is becoming a typical state space GPC problem. Some characters of GPC such as cost function, receding optimization, prediction horizon and control horizon occur in the design procedure of path-following controller. The control law is derived in the form of control increment. In the second algorithm, an analytic model predictive control algorithm is used to study the path following problem of underactuated ships. In this path-following algorithm, the output-redefinition combined heading angle and cross-track error is introduced. As a result, the original single-input multiple-output (SIMO) system is transformed into an equivalent single-input single-output (SISO) system. For the transformed system, we use the analytic model predictive control method to get path-following control law in the analytical form. The analytic model predictive controller can be regarded as special feedback linearization method optimized by predictive control method. It provides a systematic method to compute control parameters rather than by try-and-error method which is often used in the exact feedback linearization control. Relative to GPC, the analytic model predictive control method provides an analytic optimal solution and decreases the computational burden, and the stability of closed-loop system is guaranteed. The path-following system of underactuated ships is guaranteed to follow and stabilize onto the desired path. Numerical simulations demonstrate the validity of the proposed control laws.


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