PID Tuning of Servo Motor Using Bat Algorithm

AbstractThe Proportional-Integral-Derivative (PID) controller uses three parameters to produce the desired output of a system. The desired system performances are in terms of overshoot, rise time, settling time and steady state error. This has brought about various methods to tune the controller to the desired response. Therefore, the presence of the bat algorithm as part of the system will reduce the time and cost of tuning these parameters and improve the overall system performance

Kalman Filter to Improve Performance of PID Control Systems on DC Motors

A proportional–integral–derivative (PID) controller is a type of control system that is most widely applied in industrial world. Various tuning models have been developed to obtain optimal performance in PID control. However, the methods are designed under ideal circumstances. This means that the control system which has been built will not work optimally when noise exists. Noise can come from electrical vibrations, inference of electronic components, or other noise sources. Thus, it is necessary to design PID control system that can work optimally without being disturbed by noise. In this research, Kalman filter was used to improve the performance of PID controllers. The application of Kalman filter was used to reduce the noise of the input signal so that it could generate output signal which is in accordance with the expected output. Simulation result showed that the PID performance with Kalman filter was more optimal than the ordinary one to minimize the existing noise. The resulting speed of DC motor with Kalman filter had a lower overshoot than PID control without Kalman filter. This method resulted lower integral of absolute error (IAE) than ordinary PID controls. The IAE value for the PID controller with the Kalman filter was 25.4, the PID controller with the observer was 31.0, while the IAE value in the ordinary controller was only 60.9

Hybrid pitch angle controller approaches for stable wind turbine power under variable wind speed

The production of maximum wind energy requires controlling various parts of medium to large-scale wind turbines (WTs). This paper presents a robust pitch angle control system for the rated wind turbine power at a wide range of simulated wind speeds by means of a proportional–integral–derivative (PID) controller. In addition, ant colony optimization (ACO), particle swarm optimization (PSO), and classical Ziegler–Nichols (Z-N) algorithms have been used for tuning the PID controller parameters to obtain within rated stable output power of WTs from fluctuating wind speeds. The proposed system is simulated under fast wind speed variation, and its results are compared with those of the PID-ZN controller and PID-PSO to verify its effeteness. The proposed approach contains several benefits including simple implementation, as well as tolerance of turbine parameters and several nonparametric uncertainties. Robust control of the generator output power with wind-speed variations can also be considered a significant advantage of this strategy. Theoretical analyses, as well as simulation results, indicate that the proposed controller can perform better in a wide range of wind speed compared with the PID-ZN and PID-PSO controllers. The WT model and hybrid controllers (PID-ACO and PID-PSO) have been developed in MATLAB/Simulink with validated controller models. The hybrid PID-ACO controller was found to be the most suitable in comparison to the PID-PSO and conventional PID. The root mean square (RMS) error calculated between the desired power and the WT’s output power with PID-ACO is found to be 0.00036, which is the smallest result among the studied controllers

Comparison of LFC Optimization on Micro-hydro using PID, CES, and SMES based Firefly Algorithm

Micro-hydro gets potential energy from water flow that has a certain height difference. Potential energy is strongly influenced by high water fall. Potential energy through pipes, incoming turbines converted into kinetic energy. The kinetic energy of the turbine coupled with the generator is converted into electrical energy. Some components used for micro-hydro power generation, among others; intake, settling basin, headrace, penstock, turbine, draft tube, generator, and control panel. Water flows through the pipe into the turbine house so it can rotate the turbine blades. Turbine rotation is used to rotate a generator at the micro hydro generator. The most common problem with micro-hydro generating systems is inconsistent generator rotation caused by changes in connected loads. Load changes can cause system frequency fluctuations and may cause damage to electrical equipment. Artificial Intelligence (AI) is used to obtain the right constants to obtain the best optimization. In this study compare the control method, namely; Proportional Integral Derivatives (PID), Capacitive Energy Storage (CES), and Superconducting Magnetic Energy Storage (SMES). This study also compared the method of artificial intelligence between Particle Swarm Optimization (PSO) method has been studied with the method of Firefly Algorithm (FA). Overall this study compares 11 methods, namely methods; uncontrolled, PID-PSO method, PID-FA method, CES-PSO method, CES-FA method, SMES-PSO method, SMES-FA method, PID-CES-PSO method, PID-CES-FA method, PID-SMES - PSO, and PID-SMES-FA method. The results of the simulation showed that from the 11 methods studied, it was found that the PID-CES-FA method has the smallest undershot value, ie -7.774e-03 pu, the smallest overshoot value, which is 4.482e-05 pu, and the fastest completion time is 7.11 s. These results indicate that the smallest frequency fluctuations are found in the PID-CES-FA controller. Thus it is stated that the PID-CES-FA method is the best method used in the previous method. This research will use other methods to get the best controller

An application of modified adaptive bats sonar algorithm (MABSA) on fuzzy logic controller for dc motor accuracy

Controllers are mostly used to improve the control system performance. The works related to controllers attract researchers since the controller can be applied to solve many industrial problems involving speed and position. Fuzzy logic controller (FLC) gains popularity since it is widely used in industrial application. However, the FLC structure is still lacking in terms of the accuracy and time response. Although there are optimization technique used to obtain both accuracy and time response, it is still lacking. Therefore, this research presents works on the FLC system which is the fuzzy inference system that will be optimized by the modified adaptive bats sonar algorithm (MABSA) for the DC servo motor position control. The MABSA will be optimized with the range of the membership input in the FLC. The research aims are to achieve accuracy while minimizing the time response of the DC servo motor. This is done by designing the FLC using the Matlab toolbox. After the FLC is designed completely, the Simulink block diagram for the DC servo motor and FLC are built to see the performance of the controller. The range of the membership function for inputs and outputs will be optimized by the MABSA to get the best positional values. The performance of the developed FLC with the optimized MABSA is verified through the simulation and robustness tests with the system that did not use the FLC and also the system without MABSA. It was demonstrated from the study that the proposed FLC with optimization of MABSA algorithm was able to yield an improvement of 3.8% with respect to the rise time in comparison to other control schemes evaluated. When compared with PSO algorithm, proposed FLC optimized by MABSA showed improvement by 12.5% in rise time and 10% in settling time. PSO-FLC also give 0.6% steady state error compared to the MABSA-FLC. In conclusion, the results validate the better performance in terms of rise time and settling time of the developed FLC that has been optimized by the MABSA

Optimal Robust LQI Controller Design for Z-Source Inverters

This paper investigates the linear quadratic integral (LQI)-based control of Z-source inverters in the presence of uncertainties such as parameter perturbation, unmodeled dynamics, and load disturbances. These uncertainties, which are naturally available in any power system, have a profound impact on the performance of power inverters and may lead to a performance degradation or even an instability of the system. A novel robust LQI-based design procedure is presented to preserve the performance of the inverter against uncertainties while a proper level of disturbance rejection is satisfied. The stability robustness of the system is also studied on the basis of the maximum sensitivity specification. Moreover, the bat algorithm is adopted to optimize the weighting matrices. Simulation results confirm the effectiveness of the proposed controller in terms of performance and robustness

A novel technique for load frequency control of multi-area power systems

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability

Graphical User Interface (GUI) for Position and Trajectory Tracking Control of the Ball and Plate System Using H-Infinity Controller

In this paper, a graphical user interface (GUI) for position and trajectory tracking of the ball and plate system (BPS) control scheme using the double feedback loop structure i.e. a loop within a loop is proposed. The inner and the outer loop was designed using linear algebraic method by solving a set of Diophantine equations and  sensitivity function. The results were simulated in MATLAB 2018a, and the trajectory tracking was displayed on a GUI, which showed that the plate was able to be stabilized at a time of 0.3546 seconds, and also the ball settled at 1.7087 seconds, when a sinusoidal circular reference trajectory of radius 0.4m with an angular frequency of 1.57rad/sec was applied to the BPS, the trajectory tracking error was 0.0095m.  This shows that the controllers possess the following properties for the BPS, which are; good adaptability, strong robustness and a high control performance.   

Design of PID Controller for Magnetic Levitation System using Harris Hawks Optimization

In most real-time industrial systems, optimal controller implementation is very essential to maintain the output based on the reference input. The controller design problem becomes a complex task when the real-time system model becomes greatly non-linear and unstable. The proposed research aims to design the finest PID controller for the unstable Magnetic Levitation System (MLS) using the Harris Hawks Optimization (HHO) algorithm. The MLS is a highly unstable electro-mechanical system and hence the design of the controller is a complex task. The proposed work implements one Degree of Freedom (1DOF) and 2DOF PID for the system. In this work, the essential controller is designed with a two-step process; (i) Initial optimization search to find the P-controller (Kp) gain to stabilize the system and (ii) Tuning the integral (Ki) and derivative (Kd) gains to reduce the deviation between the reference input and MLS output. The performance of the proposed controller is validated with the servo and regulatory operations and the result of this study confirms that the proposed method helps to get better error value and time domain specifications compared to other available methods

: Hibridni samopodešavajući fuzzy PID regulator za upravljanje brzinom bezkolektorskog istosmjernog motora

The objective of the proposed work is to investigate the performance of hybrid self tuned fuzzy proportional integral derivative (STFPID) controller for brushless DC (BLDC) motor drive. The proposed hybrid STFPID controller includes a proportional integral derivative (PID) controller at steady state, a PID type self tuned fuzzy logic (FL) controller (STFLC) at transient state thereby combining the merits of both the controllers. The switching function incorporated in the controller ensures desired control response at various operating conditions by appropriately switching between PID and STFPID based on speed error. A detailed simulation study and performance comparison with other control approaches is performed to highlight the merits of the proposed work. The simulation results indicate that the proposed controller is robust with fast tracking capability and less steady state error. The experimental results are provided to validate the simulation study.Cilj ovog rada je istražiti performance hibridnog samopodešavajućeg regulatora za bezkolektorski istosmjerni motor. Predloženi hibridni samopodešavajući fuzzy regulator uključuje PID regulator u stacionarnom stanju i samopodešavajući fuzzy PID regulator (STFLC) za vrijeme trajanja prijelazne pojave kombinirajući prednosti oba regulatora. Funkcija prekapčanja regulatora omogućava upravljanje u različitim uvjetima odgovarajućim odabirom između PID i samopodešavajućeg fuzzy PID regulatora na temelju brzine pogreške. Provedena je detaljna simulacijska analiza i usporedba performanci s ostalim metodama upravljanja kako bi se istaknule prednosti predloženog rada. Iz simulacijskih rezultata je vidljivo je robusno svojstvo predloženenog regulatora te smanjena pogreška u stacionarnom stanju. Sustav pravljanja testiran je i eksperimentalno kao potvrda simulacijskih rezultata