Abstract: The Purdue University Research Reactor-1 (PUR-1) is a 10 kWth pool-type research reactor located at Purdue University’s West Lafayette campus. The reactor was recently upgraded to use entirely digital instrumentation and control systems. However, currently, there is no automated control system to regulate the power in the reactor. We propose a fuzzy logic controller as a form of digital twin to complement the existing digital instrumentation system to monitor and stabilize power control using existing experimental data. This work assesses the feasibility of a power controller based on a Fuzzy Rule-Based System (FRBS) by modelling and simulation with a MATLAB algorithm. The controller uses power error and reactor period as inputs and generates reactivity insertion as output. The reactivity insertion is then converted to control rod height using a logistic function based on information from the recorded experimental reactor control rod data. To test the capability of the proposed fuzzy controller, a point-kinetic reactor model is utilized based on the actual PUR-1 operation conditions and a Monte Carlo N-Particle simulation result of the core to numerically compute the neutronics parameters of reactor behavior. The Point Kinetic Equation (PKE) was employed to model dynamic characteristics of the research reactor since it explains the interactions between the spatial and time varying input and output variables efficiently. The controller is demonstrated computationally using various cases: startup, power maneuver, and shutdown. From the test results, it can be proved that the implemented fuzzy controller can satisfactorily regulate the reactor power to follow demand power without compromising nuclear safety measures.
Abstract: In this article, Optimal Control for Coordinated Control (COC) of Series Vectorial Compensator (SVeC) and Power System Stabilizer (PSS) in order to damp Low Frequency Oscillations (LFO) is proposed. SVeC is a series Flexible Alternating Current Transmission System (FACTS) device. The Optimal Control strategy based on state feedback control for coordination of PSS and SVeC controllers under different loading conditions has not been developed. So, the Optimal State Feedback Controller (OSFC) for incorporating of PSS and SVeC controllers in COC manner has been developed in this paper. The performance of the proposed controller is checked through eigenvalue analysis and nonlinear time domain simulation results. The proposed Optimal Controller design for the COC of SVeC and PSS results will be analyzed without controller. The comparative results show that Optimal Controller for COC of SVeC and PSSs improve greatly the system damping LFO than without controller.
Abstract: In this paper, the problem of robust model predictive control (MPC) for discrete-time linear systems in linear fractional transformation form with structured uncertainty and norm-bounded disturbance is investigated. The problem of minimization of the cost function for MPC design is converted to minimization of the worst case of the cost function. Then, this problem is reduced to minimization of an upper bound of the cost function subject to a terminal inequality satisfying the l2-norm of the closed loop system. The characteristic of the linear fractional transformation system is taken into account, and by using some mathematical tools, the robust predictive controller design problem is turned into a linear matrix inequality minimization problem. Afterwards, a formulation which includes an integrator to improve the performance of the proposed robust model predictive controller in steady state condition is studied. The validity of the approaches is illustrated through a robust control benchmark problem.
Abstract: Boiler is one of the critical unit in a petrochemical plant. Steam produced by the boiler is used for various processes in the plant such as urea and ammonia plant. An alternative method to optimize the boiler combustion system is presented in this paper. Adaptive Neuro-Fuzzy Inference System (ANFIS) approach is applied to model the boiler using real-time operational data collected from a boiler unit of the petrochemical plant. Nonlinear equation obtained is then used to optimize the air to fuel ratio using Genetic Algorithm, resulting an optimal ratio of 15.85. This optimal ratio is then maintained constant by ratio controller designed using inverse dynamics based on ANFIS. As a result, constant value of oxygen content in the flue gas is obtained which indicates more efficient combustion process.
Abstract: In this paper, a sampled-data model predictive tracking
control method is presented for mobile robots which is modeled as
constrained continuous-time linear parameter varying (LPV) systems.
The presented sampled-data predictive controller is designed by linear
matrix inequality approach. Based on the input delay approach, a
controller design condition is derived by constructing a new Lyapunov
function. Finally, a numerical example is given to demonstrate the
effectiveness of the presented method.
Abstract: The switched reluctance machine (SRM) has undeniable qualities in terms of low cost and mechanical robustness. However, its highly nonlinear character and its uncertain parameters justify the development of complicated controls. In this paper, authors present the design of a robust H-infinity current controller for an 8/6 SRM with taking into account the nonlinearity of the SRM and with rejection of disturbances. The electromagnetic torque is indirectly regulated through the current controller. To show the performances of this control, a robustness analysis is performed by comparing the H-infinity and PI controller simulation results. This comparison demonstrates better performances for the presented controller. The effectiveness and robustness of the presented controller are also demonstrated by experimental tests.
Abstract: Solvability of the model matching problem for
input/output switched asynchronous sequential machines is discussed
in this paper. The control objective is to determine the existence
condition and design algorithm for a corrective controller that can
match the stable-state behavior of the closed-loop system to that of
a reference model. Switching operations and correction procedures
are incorporated using output feedback so that the controlled
switched machine can show the desired input/output behavior. A
matrix expression is presented to address reachability of switched
asynchronous sequential machines with output equivalence with
respect to a model. The presented reachability condition for the
controller design is validated in a simple example.
Abstract: In this paper, the concept of a non-dominated sorting multi-objective particle swarm optimization with local search (NSPSO-LS) is presented for the optimal design of multimachine power system stabilizers (PSSs). The controller design is formulated as an optimization problem in order to shift the system electromechanical modes in a pre-specified region in the s-plan. A composite set of objective functions comprising the damping factor and the damping ratio of the undamped and lightly damped electromechanical modes is considered. The performance of the proposed optimization algorithm is verified for the 3-machine 9-bus system. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation show the potential and superiority of the NSPSO-LS algorithm in tuning PSSs over a wide range of loading conditions and large disturbance compared to the classic PSO technique and genetic algorithms.
Abstract: The wing is one of the most important parts of an airplane because it ensures stability, sustenance and maneuverability of the airplane. Because of its shape, the airplane wing can be simplified to a smart beam. Active vibration suppression is realized using piezoelectric actuators that are mounted on the surface of the beam. This work presents a tuning procedure of fractional order controllers based on a graphical approach of the frequency domain representation. The efficacy of the method is proven by practically testing the controller on a laboratory scale experimental stand.
Abstract: The paper describes the use of subspace based
identification methods for auto tuning of a state space control system.
The plant is an unstable but self balancing transport robot. Because
of the unstable character of the process it has to be identified
from closed loop input-output data. Based on the identified model
a state space controller combined with an observer is calculated. The
subspace identification algorithm and the controller design procedure
is combined to a auto tuning method. The capability of the approach
was verified in a simulation experiments under different process
conditions.
Abstract: Liquid level control of conical tank system is known to be a great challenge in many industries such as food processing, hydrometallurgical industries and wastewater treatment plant due to its highly nonlinear characteristics. In this research, an adaptive fuzzy PID control scheme is applied to the problem of liquid level control in a nonlinear tank process. A conical tank process is first modeled and primarily simulated. A PID controller is then applied to the plant model as a suitable benchmark for comparison and the dynamic responses of the control system to different step inputs were investigated. It is found that the conventional PID controller is not able to fulfill the controller design criteria such as desired time constant due to highly nonlinear characteristics of the plant model. Consequently, a nonlinear control strategy based on gain-scheduling adaptive control incorporating a fuzzy logic observer is proposed to accurately control the nonlinear tank system. The simulation results clearly demonstrated the superiority of the proposed adaptive fuzzy control method over the conventional PID controller.
Abstract: The purpose of this paper is to present a modeling and
control of a quarter-car active suspension system with unknown
mass, unknown time-delay and road disturbance. The objective of
designing the controller is to derive a control law to achieve stability
of the system and convergence that can considerably improve ride
comfort and road disturbance handling. This is accomplished by
using Routh-Hurwitz criterion based on defined parameters.
Mathematical proof is given to show the ability of the designed
controller to ensure the target of design, implementation with the
active suspension system and enhancement dispersion oscillation of
the system despite these problems. Simulations were also performed
to control quarter car suspension, where the results obtained from
these simulations verify the validity of the proposed design.
Abstract: This paper provides a comparative study on the
performances of standard PID and adaptive PID controllers tested on
travel angle of a 3-Degree-of-Freedom (3-DOF) Quanser bench-top
helicopter. Quanser, a well-known manufacturer of educational
bench-top helicopter has developed Proportional Integration
Derivative (PID) controller with Linear Quadratic Regulator (LQR)
for all travel, pitch and yaw angle of the bench-top helicopter. The
performance of the PID controller is relatively good; however, its
performance could also be improved if the controller is combined
with adaptive element. The objective of this research is to design
adaptive PID controller and then compare the performances of the
adaptive PID with the standard PID. The controller design and test is
focused on travel angle control only. Adaptive method used in this
project is self-tuning controller, which controller’s parameters are
updated online. Two adaptive algorithms those are pole-placement
and deadbeat have been chosen as the method to achieve optimal
controller’s parameters. Performance comparisons have shown that
the adaptive (deadbeat) PID controller has produced more desirable
performance compared to standard PID and adaptive (poleplacement).
The adaptive (deadbeat) PID controller attained very fast
settling time (5 seconds) and very small percentage of overshoot (5%
to 7.5%) for 10° to 30° step change of travel angle.
Abstract: Several of the practical industrial control processes are
multivariable processes. Due to the relation amid the variables
(interaction), delay in the loops, it is very intricate to design a
controller directly for these processes. So first, the interaction of the
variables is analyzed using Relative Normalized Gain Array
(RNGA), which considers the time constant, static gain and delay
time of the processes. Based on the effect of RNGA, relative gain
array (RGA) and NI, the pair (control configuration) of variables to
be controlled by decentralized control is selected. The equivalent
transfer function (ETF) of the process model is estimated as first
order process with delay using the corresponding elements in the
Relative gain array and Relative average residence time array
(RARTA) of the processes. Secondly, a decentralized Proportional-
Integral (PI) controller is designed for each ETF simply using
frequency response specifications. Finally, the performance and
robustness of the algorithm is comparing with existing related
approaches to validate the effectiveness of the projected algorithm.
Abstract: Frequency stability of microgrids under islanded
operation attracts particular attention recently. A new cooperative
frequency control strategy based on centralized multi-agent system
(CMAS) is proposed in this study. Based on this strategy, agents sent
data and furthermore each component has its own to center operating
decisions (MGCC).After deciding on the information, they are
returned. Frequency control strategies include primary and secondary
frequency control and disposal of multi-stage load in which this study
will also provide a method and algorithm for load shedding. This
could also be a big problem for the performance of micro-grid in
times of disaster. The simulation results show the promising
performance of the proposed structure of the controller based on
multi agent systems.
Abstract: Diabetes is a growing health problem in worldwide.
Especially, the patients with Type 1 diabetes need strict glycemic
control because they have deficiency of insulin production. This
paper attempts to control blood glucose based on body mathematical
body model. The Bergman minimal mathematical model is used to
develop the nonlinear controller. A novel back-stepping based sliding
mode control (B-SMC) strategy is proposed as a solution that
guarantees practical tracking of a desired glucose concentration. In
order to show the performance of the proposed design, it is compared
with conventional linear and fuzzy controllers which have been done
in previous researches. The numerical simulation result shows the
advantages of sliding mode back stepping controller design to linear
and fuzzy controllers.
Abstract: In this paper, fractional order feedback control of a ball
beam model is investigated. The ball beam model is a particular
example of the double Integrator system having strongly nonlinear
characteristics and unstable dynamics which make the control of
such system a challenging task. Most of the work in fractional order
control systems are in theoretical nature and controller design and its
implementation in practice is very small. In this work, a successful
attempt has been made to design a fractional order PIλDμcontroller
for a benchmark laboratory ball and beam model. Better performance
can be achieved using a fractional order PID controller and it is
demonstrated through simulations results with a comparison to the
classic PID controller.
Abstract: In recent years, many researchers are involved in the
field of fuzzy theory. However, there are still a lot of issues to be
resolved. Especially on topics related to controller design such as the
field of robot, artificial intelligence, and nonlinear systems etc.
Besides fuzzy theory, algorithms in swarm intelligence are also a
popular field for the researchers. In this paper, a concept of utilizing
one of the swarm intelligence method, which is called Bacterial-GA
Foraging, to find the stabilized common P matrix for the fuzzy
controller system is proposed. An example is given in in the paper, as
well.
Abstract: In this paper, we present a neural-network (NN) based
approach to represent a nonlinear Tagagi-Sugeno (T-S) system. A
linear differential inclusion (LDI) state-space representation is utilized
to deal with the NN models. Taking advantage of the LDI
representation, the stability conditions and controller design are
derived for a class of nonlinear structural systems. Moreover, the
concept of utilizing the Parallel Particle Swarm Optimization (PPSO)
algorithm to solve the common P matrix under the stability criteria is
given in this paper.
Abstract: This paper presents the design and implementation of a sliding-mod controller for tracking servo of optical disk drives. The tracking servo is majorly subject to two disturbance sources: radial run-out and shock. The lateral run-out disturbance is mostly repeatable, and a model of such disturbance is incorporated into the controller design to effectively compensate for it. Meanwhile, as a shock disturbance is usually non-repeatable and unpredictable, the sliding-mode controller is employed for its robustness to abrupt perturbations. As a result, a sliding-mode controller design based on the internal model principle is tailored for tracking servo of optical disk drives in order to deal with these two major disturbances. Experimental comparative studies are conducted to investigate the effectiveness of the specially designed controller.