scholarly journals Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2885
Author(s):  
Mahmoud Elsisi ◽  
Minh-Quang Tran ◽  
Hany M. Hasanien ◽  
Rania A. Turky ◽  
Fahad Albalawi ◽  
...  
Keyword(s):  
Voltage Regulators ◽  
System Response ◽  
Trial And Error ◽  
Control Effort ◽  
Robust Model Predictive Control ◽  
Robust Model ◽  
Control Paradigm ◽  
Parameters Uncertainty ◽  
Predictive Controller ◽  
The Stability

This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques.

Enlarging the region of stability in robust model predictive controller based on dual-mode control

2021 ◽  
pp. 014233122110123
Author(s):  
Fatemeh Khani ◽  
Mohammad Haeri
Keyword(s):  
Stability Region ◽  
Linear Matrix ◽  
Input State ◽  
Dual Mode ◽  
Model Predictive Controller ◽  
Mode Control ◽  
Robust Model ◽  
Output Constraints ◽  
Predictive Controller ◽  
The Stability

Industrial processes are inherently nonlinear with input, state, and output constraints. A proper control system should handle these challenging control problems over a large operating region. The robust model predictive controller (RMPC) could be an linear matrix inequality (LMI)-based method that estimates stability region of the closed-loop system as an ellipsoid. This presentation, however, restricts confident application of the controller on systems with large operating regions. In this paper, a dual-mode control strategy is employed to enlarge the stability region in first place and then, trajectory reversing method (TRM) is employed to approximate the stability region more accurately. Finally, the effectiveness of the proposed scheme is illustrated on a continuous stirred tank reactor (CSTR) process.

Path tracking framework synthesizing robust model predictive control and stability control for autonomous vehicle

2020 ◽  
Vol 234 (9) ◽  
pp. 2330-2341
Author(s):  
Jiaxing Yu ◽  
Xiaofei Pei ◽  
Xuexun Guo ◽  
JianGuo Lin ◽  
Maolin Zhu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Stability Control ◽  
Robust Model Predictive Control ◽  
Direct Yaw Moment Control ◽  
Robust Model ◽  
Yaw Moment Control ◽  
Moment Control ◽  
The Stability ◽  
Yaw Moment

This paper proposes a framework for path tracking under additive disturbance when a vehicle travels at high speed or on low-friction road. A decoupling control strategy is adopted, which is made up of robust model predictive control and the stability control combining preview G-vectoring control and direct yaw moment control. A vehicle-road model is adopted for robust model predictive control, and a robust positively invariant set calculated online ensures state constraints in the presence of disturbances. Preview G-vectoring control in stability control generates deceleration and acceleration based on lateral jerk, later acceleration, and curvature at preview point when a vehicle travels through a cornering. Direct yaw moment control with additional activating conditions provides an external yaw moment to stabilize lateral motion and enhances tracking performance. A comparative analysis of stability performance of stability control is presented in simulations, and furthermore, many disturbances are considered, such as varying wind, road friction, and bounded state disturbances from motion planning and decision making. Simulation results show that the stability control combining preview G-vectoring control and direct yaw moment control with additional activating conditions not only guarantees lateral stability but also improves tracking performance, and robust model predictive control endows the overall control system with robustness.

scholarly journals New Results on Robust Model Predictive Control for Time-Delay Systems with Input Constraints

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Qing Lu ◽  
Yiyong Sun ◽  
Qi Zhou ◽  
Zhiguang Feng
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Upper And Lower Bounds ◽  
Delay Systems ◽  
Input Constraints ◽  
New Model ◽  
Robust Model Predictive Control ◽  
Time Varying Delay ◽  
Robust Model ◽  
Predictive Controller

This paper investigates the problem of model predictive control for a class of nonlinear systems subject to state delays and input constraints. The time-varying delay is considered with both upper and lower bounds. A new model is proposed to approximate the delay. And the uncertainty is polytopic type. For the state-feedback MPC design objective, we formulate an optimization problem. Under model transformation, a new model predictive controller is designed such that the robust asymptotical stability of the closed-loop system can be guaranteed. Finally, the applicability of the presented results are demonstrated by a practical example.

scholarly journals Trajectory Planning in Time-Varying Adverse Weather for Fixed-Wing Aircraft Using Robust Model Predictive Control

Aerospace ◽  
2019 ◽  
Vol 6 (6) ◽  
pp. 68 ◽  
Author(s):  
Federico Mothes
Keyword(s):  
Time Varying ◽  
Robust Model Predictive Control ◽  
Moving Obstacles ◽  
Robust Model ◽  
Single Target ◽  
Adverse Weather ◽  
Relevant Task ◽  
Time Optimal ◽  
Predictive Controller ◽  
Nonholonomic Planning

The avoidance of adverse weather is an inevitable safety-relevant task in aviation. Automated avoidance can help to improve safety and reduce costs in manned and unmanned aviation. For this purpose, a straightforward trajectory planner for a single-source-single-target problem amidst moving obstacles is presented. The functional principle is explained and tested in several scenarios with time-varying polygonal obstacles based on thunderstorm nowcast. It is furthermore applicable to all kinds of nonholonomic planning problems amidst nonlinear moving obstacles, whose motion cannot be described analytically. The presented resolution-complete combinatorial planner uses deterministic state sampling to continuously provide globally near-time-optimal trajectories for the expected case. Inherent uncertainty in the prediction of dynamic environments is implicitly taken into account by a closed feedback loop of a model predictive controller and explicitly by bounded margins. Obstacles are anticipatory avoided while flying inside a mission area. The computed trajectories are time-monotone and meet the nonholonomic turning-flight constraint of fixed-wing aircraft and therefore do not require postprocessing. Furthermore, the planner is capable of considering a time-varying goal and automatically plan holding patterns.

Stability of Ring-Type MEMS Gyroscopes Subjected to Stochastic Angular Speed Fluctuation

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Samuel F. Asokanthan ◽  
Soroush Arghavan ◽  
Mohamed Bognash
Keyword(s):  
Angular Velocity ◽  
Degrees Of Freedom ◽  
Microelectromechanical Systems ◽  
Damping Ratio ◽  
Operating Conditions ◽  
System Stability ◽  
System Response ◽  
Ring Type ◽  
Mems Gyroscopes ◽  
The Stability

Effect of stochastic fluctuations in angular velocity on the stability of two degrees-of-freedom ring-type microelectromechanical systems (MEMS) gyroscopes is investigated. The governing stochastic differential equations (SDEs) are discretized using the higher-order Milstein scheme in order to numerically predict the system response assuming the fluctuations to be white noise. Simulations via Euler scheme as well as a measure of largest Lyapunov exponents (LLEs) are employed for validation purposes due to lack of similar analytical or experimental data. The response of the gyroscope under different noise fluctuation magnitudes has been computed to ascertain the stability behavior of the system. External noise that affect the gyroscope dynamic behavior typically results from environment factors and the nature of the system operation can be exerted on the system at any frequency range depending on the source. Hence, a parametric study is performed to assess the noise intensity stability threshold for a number of damping ratio values. The stability investigation predicts the form of threshold fluctuation intensity dependence on damping ratio. Under typical gyroscope operating conditions, nominal input angular velocity magnitude and mass mismatch appear to have minimal influence on system stability.

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