Comparative analysis of sliding mode and Volterra model predictive controllers applied to a benchmark CSTR non-minimum phase reactor

2007 ◽  
Author(s):  
W. Garcia-Gabin ◽  
F. Dorado ◽  
C. Bordons
Keyword(s):  
Comparative Analysis ◽  
Sliding Mode ◽  
Volterra Model ◽  
Minimum Phase ◽  
Model Predictive Controllers ◽  
Predictive Controllers

Robust Nonlinear Model Predictive Control with Model Predictive Sliding Mode for Continuous-Time Systems

2021 ◽  
Author(s):  
Elyse Hill ◽  
S. Andrew Gadsden ◽  
Mohammad Biglarbegian
Keyword(s):  
Nonlinear Model ◽  
Data Model ◽  
Continuous Time ◽  
Sliding Mode ◽  
Sampled Data ◽  
Control Effort ◽  
Continuous Time Systems ◽  
Model Predictive Controllers ◽  
Time Systems ◽  
Predictive Controllers

Abstract This paper presents a robust, tube-based nonlinear model predictive controller for continuous-time systems with additive disturbances which cascades two sampled-data model predictive controllers: the first creates a desired path using nominal dynamics and the second maintains the true state close to the nominal state by regulating a sliding variable designed on the error between the true and nominal states. The sampled-data model predictive approach permits easy incorporation of continuous-time sliding mode dynamics, allowing a dynamic boundary layer and tube design to be included. In this way, the control applied to the system capitalizes on the robustness properties of traditional sliding mode control while incorporating system constraints. Stability analysis is presented in the context of input-to-state stability for continuous-time systems. The proposed controller is implemented on two case studies, is compared to benchmark tube-based model predictive controllers, and is evaluated using average root mean square values on the state and input variables, in addition to average integral square and integral absolute error values on the position states. Results reveal the proposed technique responds to higher levels of disturbance with significant increases in control effort; eliminates constraint violation by using of constrained SMC as the secondary controller; and maintains similar tracking performance to benchmark controllers at lower levels of control effort.

An efficient sliding mode observer-based model predictive control: experimental implementation on a DC/DC power converter

2018 ◽  
Vol 40 (8) ◽  
pp. 2488-2497 ◽  
Author(s):  
Bedri Bahtiyar ◽  
Meriç Çetin ◽  
Selami Beyhan ◽  
Serdar İplikçi
Keyword(s):  
Sliding Mode ◽  
Power Converter ◽  
The State ◽  
Sliding Mode Observer ◽  
Convergence Properties ◽  
Dc Power ◽  
Experimental Implementation ◽  
Model Predictive Controllers ◽  
The Stability ◽  
Predictive Controllers

In this paper, a discretization-based sliding mode observer (DBSMO) is proposed for the state and parameter estimation of nonlinear systems. In the DBSMO structure, an accurate discretized dynamics are derived for the state and parameter update of the sliding mode observer (SMO) instead of integration-based state update. In this way, faster converging observer dynamics are obtained. The stability properties of the conventional SMO remain the same for DBSMO. In the application presented here, first a real-time DC/DC power converter is designed with a computer interface. Then, to show the enhancement of convergence properties, conventional SMO and DBSMO-based model predictive controllers are designed and applied to control of the DC/DC power converter. Through simulation and experimental results, it is shown that the estimation performance of SMO is greatly improved so that the tracking performance is also increased, which are the main contributions of the paper.

scholarly journals Hardware Synthesis of Explicit Model Predictive Controllers

2007 ◽  
Vol 15 (1) ◽  
pp. 191-197 ◽  
Author(s):  
Tor A. Johansen ◽  
Warren Jackson ◽  
Robert Schreiber ◽  
Petter Tondel
Keyword(s):  
Explicit Model ◽  
Hardware Synthesis ◽  
Model Predictive Controllers ◽  
Predictive Controllers
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