scholarly journals Simulation of Performance Response of State Variables of a Chemical Boiler Process

2018 ◽  
pp. 1-5
Author(s):  
Donatus O. Njoku ◽  
Asagba P. O ◽  
Chilaka U. Longinus ◽  
Amaefule I. A. ◽  
Igwe S. Onyema
Keyword(s):  
Transfer Function ◽  
Closed Loop ◽  
Control Process ◽  
Open Loop ◽  
The State ◽  
Feedback Gain ◽  
State Variables ◽  
Simulink Model ◽  
Performance Response ◽  
Optimal Regulator

This paper has presented simulation of performance response of state variables of a chemical boiler process. The transfer function of a boiler flow control process of a chemical plant was obtained. The transfer function was transformed into state space form to study the state variables of the system. An optimal regulator was designed using MATLAB programme. The developed optimal regulator was added to the loop of the system to form a closed loop system. A Simulink model was developed and used to study performance response of the system. Simulation was carried out for two conditions, open loop and closed loop. The simulation results indicated that the performance responses of the state variables were improved and better stability achieved with the inclusion of the designed feedback gain matrix of the optimal regulator.

Robust attitude control of the three-dimensional unknown chaotic satellite system

2021 ◽  
pp. 014233122110561
Author(s):  
Muhammad Shafiq ◽  
Israr Ahmad ◽  
O Abdullah Almatroud ◽  
M Mossa Al-Sawalha
Keyword(s):  
Attitude Control ◽  
Closed Loop ◽  
Three Dimensional ◽  
Adaptive Controller ◽  
The State ◽  
Feedback Gain ◽  
State Variables ◽  
Nonlinear Controller ◽  
Model Uncertainties ◽  
Control Effort

This paper proposes a novel continuous-time robust direct adaptive controller for the attitude control of the three-dimensional unknown chaotic spacecraft system. It considers that the plant’s nonlinear terms, exogenous disturbances, and model uncertainties are unknown and bounded; the controller design is independent of the system’s nonlinear terms. These controller attributes flourish the robust performance of the closed-loop and establish smooth state vector convergence to zero. The proposed controller consists of three parts: (1) a linear controller establishes the stability of the closed-loop at the origin, (2) a nonlinear controller component that autonomously adjusts the feedback gain, and (3) a nonlinear adaptive controller compensates for the model uncertainties and external disturbances using the online estimates of bounds and model uncertainties. The output of this part remains within a given upper and lower bound. The feedback controller gain is large when the state variables are away from the origin and become small in the origin’s vicinity. This feature is novel and contributes to the synthesis of smooth control effort that establishes robust fast and oscillation-free convergence of the state variables to zero. The Lyapunov direct stability analysis assures the global asymptotic robust stability of the closed-loop. Computer simulations and comparative analysis are included to verify the theoretical findings.

scholarly journals PI- Controlled PV Fed Fly Back Converter with Enhanced Dynamic Response

2019 ◽  
Vol 8 (2S11) ◽  
pp. 4031-4034
Keyword(s):  
Steady State ◽  
Dynamic Response ◽  
Rise Time ◽  
Closed Loop ◽  
Input Voltage ◽  
Pi Controller ◽  
Open Loop ◽  
Load Variations ◽  
Simulink Model ◽  
Step Down

Fly back converter is the most popular converter because of its simplicity, low part counts and isolation. It occupies less volume and it saves cost. Fly back converter steps up and step down the voltage with the same polarity. Open loop operation remains insensitive to the input voltage and load variations. Matlab Simulink model for Fly back converter is established using PI controller. Open loop Fly back converter system and closed loop fly back converter systems are simulated and their outcomes are compared. Comparison is done in terms of Rise time ,Settling time and steady state error

Modeling and Simulation of Closed Loop Controlled Parallel Cascaded Buck Boost Converter Inverter Based Solar System

2015 ◽  
Vol 6 (3) ◽  
pp. 648 ◽  
Author(s):  
T. Sundar ◽  
S. Sankar
Keyword(s):  
Solar System ◽  
Modeling And Simulation ◽  
Single Phase ◽  
Closed Loop ◽  
Boost Converter ◽  
Open Loop ◽  
Boost Converters ◽  
Closed Loop Systems ◽  
Simulink Model ◽  
Simulation Results

<p>This Work deals with design, modeling and simulation of parallel cascaded buck boost converter inverter based closed loop controlled solar system. Two buck boost converters are cascaded in parallel to reduce the ripple in DC output. The DC from the solar cell is stepped up using boost converter. The output of the boost converter is converted to 50Hz AC using single phase full bridge inverter. The simulation results of open loop and closed loop systems are compared. This paper has presented a simulink model for closed loop controlled solar system.  Parallel cascaded buck boost converter is proposed for solar system.</p>

scholarly journals Zeroing of state variables in fractional descriptor electrical circuits by state-feedbacks

2014 ◽  
Vol 63 (3) ◽  
pp. 321-333
Author(s):  
Tadeusz Kaczorek
Keyword(s):  
Closed Loop ◽  
Sufficient Conditions ◽  
The State ◽  
Necessary And Sufficient Conditions ◽  
State Variables ◽  
Electrical Circuits ◽  
Closed Loop Systems ◽  
Necessary And Sufficient

Abstract The problem of zeroing of the state variables in fractional descriptor electrical circuits by state-feedbacks is formulated and solved. Necessary and sufficient conditions for the existence of gain matrices such that the state variables of closed-loop systems are zero for time greater zero are established. The procedure of choice of the gain matrices is demonstrated on simple descriptor electrical circuits with regular pencils

scholarly journals Iterative LQG Controller Design Through Closed-Loop Identification

1996 ◽  
Vol 118 (2) ◽  
pp. 366-372 ◽  
Author(s):  
Min-Hung Hsiao ◽  
Jen-Kuang Huang ◽  
David E. Cox
Keyword(s):  
Kalman Filter ◽  
State Feedback ◽  
Closed Loop ◽  
Controller Design ◽  
Open Loop ◽  
The State ◽  
Magnetic Suspension ◽  
Loop Model ◽  
Closed Loop Identification ◽  
Noise Statistics

This paper presents an iterative LQG controller design approach for a linear stochastic system with an uncertain openloop model and unknown noise statistics. This approach consists of closed-loop identification and controller redesign cycles. In each cycle, the closed-loop identification method is used to identify an open-loop model and a steady-state Kalman filter gain from closed-loop input/output test data obtained by using a feedback LQG controller designed from the previous cycle. Then the identified open-loop model is used to redesign the state feedback. The state feedback and the identified Kalman filter gain are used to form an updated LQG controller for the next cycle. This iterative process continues until the updated controller converges. The proposed controller design is demonstrated by numerical simulations and experiments on a highly unstable large-gap magnetic suspension system.

scholarly journals Optimal load frequency control through combined state and control gain estimation for noisy measurements

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Anju G. Pillai ◽  
Elizabeth Rita Samuel ◽  
A. Unnikrishnan
Keyword(s):  
Frequency Control ◽  
The State ◽  
Feedback Stabilization ◽  
Load Frequency Control ◽  
Feedback Gain ◽  
State Variables ◽  
Linear Quadratic ◽  
System Parameters ◽  
Load Frequency ◽  
Optimal Load

AbstractCombined estimation of state and feed-back gain for optimal load frequency control is proposed. Load frequency control (LFC) addresses the problem of controlling system frequency in response to disturbance, and is one of main research areas in power system operation. A well acknowledged solution to this problem is feedback stabilization, where the Linear Quadratic Regulator (LQR) based controller computes the feedback gain K from the known system parameters and implements the control, assuming the availability of all the state variables. However, this approach restricts control to cases where the state variables are readily available and the system parameters are steady. Alternatively, by estimating the states continuously from available measurements of some of the states, it can accommodate dynamic changes in the system parameters. The paper proposes the technique of augmenting the state variables with controller gains. This introduces a non-linearity to the augmented system and thereby the estimation is performed using an Extended Kalman Filter. This results in producing controller gains that are capable of controlling the system in response to changes in load demand, system parameter variation and measurement noise.

scholarly journals Hamilton’s rule, gradual evolution, and the optimal (feedback) control of phenotypically plastic traits

2020 ◽  
Author(s):  
Piret Avila ◽  
Tadeas Priklopil ◽  
Laurent Lehmann
Keyword(s):  
Closed Loop ◽  
Expression Profiles ◽  
Open Loop ◽  
Order Conditions ◽  
Optimal Feedback Control ◽  
State Variables ◽  
First Order ◽  
Hamilton’S Rule ◽  
Gradual Evolution ◽  
Hamilton's Rule

AbstractMost traits expressed by organisms, such as gene expression profiles, developmental trajectories, behavioural sequences and reaction norms are function-valued traits (colloquially “phenotypically plastic traits”), since they vary across an individual’s age and in response to various internal and/or external factors (state variables). Furthermore, most organisms live in populations subject to limited genetic mixing and are thus likely to interact with their relatives. We here formalise selection on genetically determined function-valued traits of individuals interacting in a group-structured population, by deriving the marginal version of Hamilton’s rule for function-valued traits. This rule simultaneously gives a condition for the invasion of an initially rare mutant function-valued trait and its ultimate fixation in the population (invasion thus implies substitution). Hamilton’s rule thus underlies the gradual evolution of function-valued traits and gives rise to necessary first-order conditions for their uninvadability (evolutionary stability). We develop a novel analysis using optimal control theory and differential game theory, to simultaneously characterise and compare the first-order conditions of (i) open-loop traits - functions of time (or age) only, and (ii) closed-loop (state-feedback) traits - functions of both time and state variables. We show that closed-loop traits can be represented as the simpler open-loop traits when individuals do no interact or when they interact with clonal relatives. Our analysis delineates the role of state-dependence and interdependence between individuals for trait evolution, which has implications to both life-history theory and social evolution.

Plant-Input-Mapping Discretization Method for a Feedback System in the State-Space Form

2021 ◽  
Author(s):  
Keisuke Yagi ◽  
Hiroaki Muto ◽  
Yoshikazu Mori
Keyword(s):  
Transfer Function ◽  
State Space ◽  
State Feedback ◽  
Closed Loop ◽  
Space Form ◽  
Feedback System ◽  
The State ◽  
Loop System ◽  
Closed Loop System ◽  
Digital Redesign

Abstract The paper proposes the digital redesign technique called plant-input-mapping (PIM) method for a feedback system described in the state-space form. The PIM method, which was originally presented in the transfer function form, focuses on the plant input signal via the plant input transfer function and discretizes it so as to satisfy the control zero principle in the resulting discrete-time closed-loop system, which leads to guaranteeing the closed-loop stability for any non-pathological sampling interval. In accordance with this approach, the proposed PIM method focuses on the control zeros included in the plant input signal. The paper proves that the matched-pole-zero discrete-time model of the plant input state-equation satisfies the control zero principle with the step-invariant model of the plant. Then, when the matched-pole-zero model is set as the target of model matching, the parameters of the state-space PIM controller employing the observer-based dynamic state-feedback can systematically be determined from the underlying continuous-time closed-loop system with guaranteed stability. This discretization process can immediately be applied to a state-feedback system and a class of multi-input multi-output systems without any modification, which cannot be discretized by the conventional PIM methods. The discretization performance of the proposed PIM method is evaluated through illustrative examples with comparable digital redesign methods, which reveal that the proposed method performs a good reproduction of the characteristics of the underlying closed-loop system.

Observer Based Adaptive Estimation/Cancellation of Unmatched Sinusoidal Disturbances in Known LTI Systems by State Derivative Measurement

2015 ◽  
Author(s):  
Halil Ibrahim Basturk
Keyword(s):  
Closed Loop ◽  
Linear Time ◽  
Adaptive Estimation ◽  
Original System ◽  
Feedback System ◽  
Open Loop ◽  
The State ◽  
Adaptive Observer ◽  
Order System ◽  
Linear Time Invariant

In this paper, an adaptive observer and backstepping controller are designed to cancel and estimate sinusoidal disturbances forcing a linear time-invariant by using only the measurements of the state-derivatives. The parametrization of the sinusoidal disturbance as the output of a known feedback system with an unknown output vector that depends on unknown disturbance parameters with the necessary filter designs enables to approach the problem as an adaptive control problem. An observer is designed for the unmeasured virtual input to apply a backstepping procedure which handles the unmatched disturbance and input condition. Firstly, it is shown that the disturbance and the unmeasured actuator state are observed perfectly in the open loop case. Secondly, the closed loop case is considered and it is proven that the equilibrium of the closed-loop adaptive system is stable and the state of the considered original system converge to zero as t → ∞ with perfect disturbance estimation. The effectiveness of the controller and the observers are illustrated with a simulation example of a third order system.

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