Top PDF LQR based improved discrete PID controller design via optimum selection of weighting matrices using fractional order integral performance index

LQR based improved discrete PID controller design via optimum selection of weighting matrices using fractional order integral performance index

LQR based improved discrete PID controller design via optimum selection of weighting matrices using fractional order integral performance index

K = ξ = ω = with a badly tuned PID controller setting K p = 2, K i = 2, K d = 1 (as a guess solution in the real coded GA based optimization process and the time evolution of ITSE has been shown in Fig. 8. Fig. 8 also shows that the ITSE which is based on a first order integration approaches towards a steady value monotonically as time increases whereas with a low order of fractional integration ( Λ ) the integration no longer remains monotonic function which justifies the fact that the integrand in (31) is changing its shape over time [29], [32]. A few interesting observations can be inferred from Fig. 8. The t e ⋅ 2 ( ) t curve is always positive but has an oscillatory behavior and it tends towards zero with increase in time. This implies that the process settles down to a steady state value after a transitory period. Now from our concept of integer order integration, which interprets it simply as an area under a curve, we know that it is monotonically increasing if the area is positive as also shown in Fig. 8 for Λ = 1 . But due to the complicated characteristics of the fractional order integration, it can be seen that the integrand curves are not monotonically increasing and shows some kind of oscillatory behavior which is not possible with integer order integrals. Also the final value of the fractional order integrands vary with time and none of them reach a steady state value like the integer order integral. Thus taking the final value of the integral in a finite time horizon for the design of the controller or comparing two designs based on the precept of final values may not be appropriate.
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Multi-objective LQR with Optimum Weight Selection to Design FOPID Controllers for Delayed Fractional Order Processes

Multi-objective LQR with Optimum Weight Selection to Design FOPID Controllers for Delayed Fractional Order Processes

paper with optimal selection of weighting matrices for handling FO process with time delay, in a compact NIOPTD template. The optimal choice of the weighting matrices along with the FO differ- integrals of the PI λ D µ controller have been obtained through multi-objective NSGA-II algorithm, based on simultaneous minimization of two conflicting time domain integral performance indices – ITSE and ISDCO. Thus, the proposed method preserves the state optimality of LQR and at the same time gives a low error index in the closed loop time response while also ensuring stability and efficiently handling the time delay terms of FO process. These improvements enable the control designer to obtain satisfactory closed loop response while also enjoying the benefits of LQR in the optimal PI λ D µ controller tuning. The MOO results in a range of controller parameters lying on the Pareto front as opposed to a single controller obtained by commonly adopting single-objective optimization framework, by satisfying different conflicting time domain objectives. It is shown that there exists a trade-off between the two time domain objectives and an improvement in one performance index would invariably result in a deterioration of the other. Thus the designer can choose a controller according to the specific requirements of his control problem. Our simulation results show that the proposed techniques works well even for a highly oscillatory and a highly sluggish FO system with time delay yielding a range of solutions on the Pareto front. For delay dominant plants our simulation shows He’s method and for balanced lag and delay plants Cai’s method perform better, whereas for lag-dominant systems the solutions are comparable. Tuning rules for the five optimal LQR-FOPID knobs have been provided as a function of process parameter – delay to lag ratio (L/T) and fractional exponent of the process (α). Future scope of work may include multi-objective LQR based FOPID controller tuning for unstable and integrating fractional order systems with time delay and extending the concept of FO-LQR to noisy processes using FO Kalman filter and Linear Quadratic Gaussian (LQG) technique.
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Optimum Weight Selection Based LQR Formulation for the Design of Fractional Order PIλDμ Controllers to Handle a Class of Fractional Order Systems

Optimum Weight Selection Based LQR Formulation for the Design of Fractional Order PIλDμ Controllers to Handle a Class of Fractional Order Systems

Like, the conventional integer order case, optimal control theory has also been extended for fractional order systems with fractional state variables by Agrawal [23]. Shafieezadeh et al. [24] have investigated the effect of fractional powers of the state variables along with the conventional optimal state feedback law. Tricaud and Chen [25], Agrawal [26], Biswas and Sen [27] formulated the fractional optimal control problem with a quadratic performance index involving the states and control law. Li and Chen [28], Tangpong and Agrawal [29], Biswas and Sen [30] also proposed similar quadratic performance index in matrix form for fractional optimal control problems that has been adopted in the present work. Saha et al. [31] studied LQR equivalence of dominant pole placement problem with FOPID controllers. Das et al. [32] studied the proposed the optimum weight selection based digital PID design for fractional order integral performance indices. This paper improvises over the available techniques by coupling the LQR theory with GA based time domain optimal PI D λ µ
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Stability and Robust Performance Analysis of Fractional Order Controller over Conventional Controller Design

Stability and Robust Performance Analysis of Fractional Order Controller over Conventional Controller Design

In this paper, a new comparative approach was proposed for reliable controller design. Scientists and engineers are often confronted with the analysis, design, and synthesis of real-life problems. The first step in such studies is the development of a 'mathematical model' which can be considered as a substitute for the real problem. The mathematical model is used here as a plant. Fractional integrals and derivatives have found wide application in the control of dynamical systems when the controlled system and the controller are described by a set of fractional order differential equations. Here the stability and robustness of fractional order system is checked at the different level and it is found that the stability region is large in the complex plane. This large stability region provides the more flexibility for system implementation in the control engineering. Generally, an analytically or experimentally approaches are used for designing the controller. If a fractional order controller design approach used for a given plant then the controlled parameter gives the better result.
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Comparison of Fractional Order PID Controller and Sliding Mode Controller with Computational Tuning Algorithm

Comparison of Fractional Order PID Controller and Sliding Mode Controller with Computational Tuning Algorithm

Controllers are playing vital roles especially in the assistant of the engineering processes, for example shifting, shaping and lifting. Apart from being an assistant, the controller is especially useful in dealing with the system major uncertainties and disturbances. It is the fact that the practical systems are mostly intrinsically nonlinear. To overcome the existing drawback in the practical systems, high-performance control system is needed to reduce the actual required elements and achieved the desired response, for example, the voltage or power that generates torque to actuate the load or the application. Apart from reducing the actual effort, the high-performance controller can perform surprisingly in achieving the desired response even with the parameter changes along with the operation.
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PID vs LQR controller for tilt rotor airplane

PID vs LQR controller for tilt rotor airplane

In literature some parameters have to been verified and limited in acceptable ranges including overshoot, response time and control precision, the recommended acceptable range for the overshoot is set to not exceed 10% and the control precision to not exceed ±1% [19],the response time is depend on the size of the UAV and the quality of actuators used. Due to its simplicity a multiple PID’s were adopted for stabilizing the UAV in hover mode for indoor performing, the role of this controller is to minimize a cost function by adjusting the input value in order to reduce the error between the desired and the measured values [19].
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MIMO PID Controller Tuning Method for Quadrotor Based on LQR/LQG Theory

MIMO PID Controller Tuning Method for Quadrotor Based on LQR/LQG Theory

A method for pre-tuning a multivariable PID controller for a quadrotor attitude and altitude control has been proposed, which is based on LQR/LQG theory, using a modified structure of the LQR, where the state feedback algorithm employs the tracking error vector instead of the state vector. Realistic computer simulations have been carried out using a nonlinear mathematical model of the DJI-F450 quadrotor, considering parametric uncertainty and external disturbances acting at the plant input. Madgwick’s algorithm and a Kalman filter have been used for data fusion and estimating state variables using data from MARG, LIDAR and optical flow camera, which have been simulated using data sheets provided by manufactures. Simulation results show that the proposed tuning algorithm gives MIMO PID controllers which are robust in the face of uncertainties and external disturbances. Supplementary Materials: The following are available online at http://www.mdpi.com//xx/1/5/https: //youtu.be/rRv-dkYR6Ro, Video S1: MIMO PID Controller Tuning Method Based on LQR/LQG Theory:
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Fractional Order Modeling and GA Based Tuning for Analog Realization with Lossy Capacitors of a PID Controller

Fractional Order Modeling and GA Based Tuning for Analog Realization with Lossy Capacitors of a PID Controller

Clearly, classical PID controller is a special case of PI λ D μ controller with λ = 1, μ = 1. Classical PI and PD controllers can also be recovered from (1) by proper choice of λ and μ values. It can be expected that the PI λ D μ controller may enhance the systems control performance because of its potential enhanced flexibility. Another advantage of the PI λ D μ controller is the possible better control of fractional order dynamical systems. As a matter of fact the PI λ D μ controllers are also more robust than their classical counterparts due primarily to the presence of two extra degrees of freedom [2]. It is pointed out in [3] that a finite dimensional approximation of the fractional order controller is possible and it should be carried out in a proper range of frequencies of practical interest.
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PID vs LQR controller for tilt rotor airplane

PID vs LQR controller for tilt rotor airplane

The main thematic of this paper is controlling the main manoeuvers of a tilt rotor UAV airplane in several modes such as vertical takeoff and landing, longitudinal translation and the most important phase which deal with the transition from the helicopter mode to the airplane mode and visversa based on a new actuators combination technique for specially the yaw motion with not referring to rotor speed control strategy which is used in controlling the attitude of a huge number of vehicles nowadays. This new actuator combination is inspired from that the transient response of a trirotor using tilting motion dynamics provides a faster response than using rotor speed dynamics. In the literature, a lot of control technics are used for stabilizing and guarantee the necessary manoeuvers for executing such task, a multiple Attitude and Altitude PID controllers were chosen for a simple linear model of our tilt rotor airplane in order to fulfill the desired trajectory, for reasons of complexity of our model the multiple PID controller doesnt take into consideration all the coupling that exists between the degrees of freedom in our model, so an LQR controller is adopted for more feasible solution of complex manoeuvering, the both controllers need linearization of the model for an easy implementation.
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Controller Design for Fractional-Order Systems

Controller Design for Fractional-Order Systems

In this thesis work, a fractional order system is represented by a higher integer order system, which is further approximated by second order plus time delay (SOPTD) model. The represented SOPTD model of fractional order system is verified both frequency and time domain gives the approximate representation of fractional-order system. Further, the optimal time domain tuning of fractional order PID and classical PID controller based on Genetic algorithm. Genetic algorithm is used to minimizing various integral performance indices. It is observed that the controller performance depends on the type of process to be controlled and also on the choice of integral performance indices. Considering the uncurtaining in system dynamics, a fuzzy fractional order PID and fuzzy PID is designed. Simulation results show that the fuzzy fractional order PID controller is able to outperform the classical PID, fuzzy PID and FOPID controllers.
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Frequency Domain Design of Fractional Order PID Controller for AVR System Using Chaotic Multi-objective Optimization

Frequency Domain Design of Fractional Order PID Controller for AVR System Using Chaotic Multi-objective Optimization

it becomes unstable. Uncertainties can arise not only due to load variations in the power system, but there can be significant uncertainty due to modelling approximations or other stochastic phenomena. Hence frequency domain designs are mostly preferred over time domain design from the implementation and operation point of view of a control system. In spite of the importance of AVR in power systems, very few literatures consider a multi-objective formalism. A co-ordinated tuning of AVR and Power System Stabiliser (PSS) has been done in Viveros et al. [10] using the Strength Pareto Evolutionary Algorithm (SPEA). However, the contradictory objectives considered are the integrated time domain response for the AVR and the closed loop eigenvalue damping ratio of the PSS. This is a coupled time-frequency domain approach and does not address the inherent contradictory objectives in the AVR itself. In Ma et al. [11] a multi objective problem has been formulated for finding out the optimal solution for coordinate voltage control. A hierarchical genetic algorithm has been proposed for multi objective optimisation and a Pareto trade-off is obtained. In Mendoza et al. [12] a micro genetic algorithm is used to solve the multi objective problem of finding the AVR location in a radial distribution network in order to reduce energy losses and improve the energy quality. In [13], a similar problem has been attempted using a multi objective fuzzy adaptive PSO algorithm. However none of these papers consider the inherent design trade-off in the AVR tuning itself, which is one of the main focuses of the present paper.
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Implementation of Fractional Order PID Controller for An AVR System Using GA Optimization Technique

Implementation of Fractional Order PID Controller for An AVR System Using GA Optimization Technique

Abstract: In recent years, the research and studies on fractional order (FO) modelling of dynamic systems and controllers are quite advancing. To improve the stability and dynamic response of automatic voltage regulation (AVR) system, we presented fractional Order PID (FOPID) controller by employing Genetic Algorithm (GA) technique. We used fractional order tool kit a masked fractional order PID block in MATLAB programming to implement the P ⋋ controller for any type of system. Comparisons are made with a PID controller from standpoints of transient response. It is shown that the proposed FOPID controller can improve the performance of the AVR in all the aspects.
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Enhancement of Investigation on Dynamic Response of Smart Grid System Using Fractional Order PID Controller

Enhancement of Investigation on Dynamic Response of Smart Grid System Using Fractional Order PID Controller

The closed loop SGS system with PI controller system is shown in Fig 8. Load voltage is sensed and it is compared with the reference voltage. The error is given to the PI controller. The output of PI compensator is given to the comparator which updates the width of the pulse applied to the source side converter. The output voltage of wind P

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Design, Implementation and Performance Analysis of Tabu Search Based PID Controller

Design, Implementation and Performance Analysis of Tabu Search Based PID Controller

Currently, it has grown to be a recognized optimization approach that is quickly increasing to numerous fields of engineering and sciences. Together with other heuristic search algorithms TS has been singled out as "extremely promising" for the future treatment of practical applications [8]. Usually, TS is categorized by its capability to avoid entrapment in local optimum result and prevent cycling by the use flexible memory of search history [9].

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Regression model for tuning the PID controller with fractional order time delay system

Regression model for tuning the PID controller with fractional order time delay system

Debbarma et al. [33] have suggested a new two-Degree-of- Freedom-Fractional Order PID (2-DOF-FOPID) controller ended up being suggested intended for automatic generation control (AGC) involving power systems. The controller ended up being screened intended for the first time using three unequal area thermal systems considering reheat turbines and appropriate generation rate constraints (GRCs). The simultaneous optimization of several parameters as well as speed regulation parameter (R) in the governors ended up being accomplished by the way of recently produced metaheu- ristic nature-inspired criteria known as Firefly Algorithm (FA). Study plainly reveals your fineness in the 2-DOF- FOPID controller regarding negotiating moment as well as lowered oscillations. Found function furthermore explores the effectiveness of your Firefly criteria primarily based mar- keting technique in locating the perfect guidelines in the con- troller as well as selection of R parameter. Moreover, the convergence attributes in the FA are generally justify when compared with its efficiency along with other more developed marketing technique such as PSO, BFO and ABC. Sensitivity analysis realizes your robustness in the 2-DOF-FOPID con- troller intended for distinct loading conditions as well as large improvements in inertia constant (H) parameter. Additionally, the functionality involving suggested controller will be screened next to better quantity perturbation as well as ran- domly load pattern.
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On fractional-order PID controllers

On fractional-order PID controllers

Set-point tracking or steady state error reduction is judged using integral absolute error. This is tabulated in Table 2. In terms of performance, the proposed method compares favorably with the optimum PI method as reflected in the tabulated IAE index. However, the proposed method is based on simple time domain and frequency response calculations and does not require any extensive optimisation routine. This reduces computational burden when compared with optimal methods like ORA-Optimum PI. In addition, it yields a more robust control system as shown by the ISV analysis in Fig. 3. 8. CONCLUSIONS
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Optimal frequency control in microgrid system using fractional order PID controller using Krill Herd algorithm

Optimal frequency control in microgrid system using fractional order PID controller using Krill Herd algorithm

Анотація. У статті досліджено використання регуляторів пропорційного, інтегрального та похідного дробового порядку (FOPID) для регулювання частоти та потужності в електромережі. Запропонована мікромережева система складається з поновлюваних джерел енергії, таких як сонячні та вітрогенератори, дизельних генераторів як вторинного джерела для підтримки основних генераторів, а також з різних пристроїв для накопичування енергії, таких як паливна батарея, акумулятор і маховик. Через переривчасту природу інтегрованої відновлювальної енергії, наприклад, вітрогенераторів та фотоелектричних генераторів, які залежать від погодних умов та зміни клімату, це впливає на стабільність мікромережі, враховуючи коливання частоти та відхилення потужності, які можна поліпшити за допомогою вибраного контролера. Контролер дробового порядку має п’ять параметрів порівняно з класичним PID-контролером, що робить його більш гнучким та надійним щодо збурень мікромережі. Параметри PID-контролера дробового порядку оптимізовані за допомогою нової методики оптимізації під назвою «зграя криля», яка обрана як підходящий метод оптимізації порівняно з іншими методами, такими як оптимізація методом рою частинок. Результати показують кращі показники роботи цієї системи за допомогою алгоритму «зграя криля», заснованого на PID-контролері дробового порядку, виключаючи коливання частоти та відхилення потужності порівняно з класичним PID-контролером. Отримані результати порівнюються з PID-контролером дробового порядку, оптимізованим за допомогою оптимізації методом рою частинок. Запропонована система моделюється в номінальному режимі роботи та використовує відключення накопичувальних пристроїв, таких як акумулятор та маховик, щоб перевірити надійність запропонованих методів та порівняти отримані результати. Бібл. 18, рис. 8.
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ALL DIGITAL DESIGN AND IMPLEMENTAION OF PROPORTIONAL- INTEGRAL-DERIVATIVE (PID) CONTROLLER

ALL DIGITAL DESIGN AND IMPLEMENTAION OF PROPORTIONAL- INTEGRAL-DERIVATIVE (PID) CONTROLLER

Due to the prevalence of pulse encoders for system state information, an all- digital proportional-integral-derivative (ADPID) is proposed as an alternative to traditional analog and digital PID controllers. The basic concept of an ADPID stems from the use of pulse-width-modulation (PWM) control signals for continuous-time dynamical systems, in that the controller’s proportional, integral and derivative actions are converted into pulses by means of standard up-down digital counters and other digital logic devices. An ADPID eliminates the need for analog-digital and digital-analog conversion, which can be costly and may introduce error and delay into the system. In the proposed ADPID, the unaltered output from a pulse encoder attached to the system’s output can be interpreted directly. After defining a pulse train to represent the desired output of the encoder, an error signal is formed then processed by the ADPID. The resulting ADPID output or control signal is in PWM format, and can be fed directly into the target system without digital-to-analog conversion. In addition to proposing an architecture for the ADPID, rules are presented to enable control engineers to design ADPIDs for a variety of applications.
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PID Controller Design for Nonlinear Systems Using Discrete-Time Local Model Networks

PID Controller Design for Nonlinear Systems Using Discrete-Time Local Model Networks

Controller Design Workflow Testbed Maps Signals DoE [n, q, u] LMN SS-Model Local PIDs DoE Optimisation y Simulation Parameter Controller Maps Performance Stability Identif ication Dynami[r]

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Design of Fixed Order Robust Discrete Controller Using Frequency-Domain Robust Controller Design Toolbox

Design of Fixed Order Robust Discrete Controller Using Frequency-Domain Robust Controller Design Toolbox

The most efficient and established algorithms for robust control analysis and design are gathered in Robust Control Toolbox of Matlab together with additional commands for closed-loop and controller structure definition, weighting filter design for performance and uncertainty and controller reduction commands[2][4]. The main robust control synthesis commands are used for H∞ loop shaping, optimal H∞ control, μ-synthesis [1] and H∞ fixed-structure controller tuning. In this paper, the frequency domain robust controller toobox is used for designing the robust fixed order controller. The Frequency-Domain Robust Controller Design Toolbox [16] is a tool for finding the robust linearized parametric controllers in Nyquist plot. It can be used to design linearly parametric controllers of any order for parametric as well as for non parametric models.
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