Positioncontrolsystem of an Electro-HydraulicActuatorSystem (EHAS) is investigated in this paper The EHAS is developed by taking into consideration the nonlinearities of the system: the friction and the internal leakage. A variable load that simulates a realistic load in robotic excavator is applied as the trajectory reference. A method of control strategy that is implemented by employing a FuzzyLogicController (FLC) whose parameters are optimizedusingParticleSwarmOptimization (PSO) is proposed. The scaling factors of the fuzzy inference system are tuned to obtain the optimal values which yield the best system performance. The simulation results show that the FLC is able to track the trajectory reference perfectly for orifice opening. Orifice opening more than introduces chattering, where the FLC alone is not sufficient to overcome this. The PSO optimized FLC reduces the chattering significantly. This result suggests the implementation of the proposed method in positioncontrol of EHAS.
Stabilization-tracking systems are multidisciplinary in nature and are required to maintain the orientation of optical sensors (payload ), so that they are pointed in application dependent direction and held steady in inertial space along selected orientation. Thus stabilization tracking system precisely control the position of sensor’s line of sight (LOS) and provides isolation from base foundation dynamics or host platform generated vibration. The movement of vehicle carrying the sensors can introduce errors in the attempts to maintain a track on a given target, thus the modulation transfer function of an electro-optical system mounted on a mobile platform decreases very rapidly with an increase in the disturbance on a LOS. The servo control loops determine the ultimate behavior of stabilization-tracking systems.
The new approach of controller, to overcome the uncertainties and nonlinear problem the fuzzycontroller were applied to EHA system. A few of research toward on fuzzylogiccontrol has been done and utilized such as fuzzy with PID and adaptive PID controlusingfuzzy . Jun et. al  presented fuzzylogic self – tuning PID controller to a nonlinear characteristic system for regulating brushless DC motor (BLDC) of EHA system. The nonlinear characteristic that occur such as saturation of the motor power and dead zone due to the statistic friction. The results show, when the Fuzzycontroller implemented in EHA system, it can achieve fast response ability without overshoot. In addition, representing, manipulating and implementing a human heuristic knowledge for formal methodology can be provided to control a system performance .
Abstract: Negative output elementary Luo converter performs the conversion from positive DC input voltage to negative DC output voltage. Since Luo converters are non-linear and time-variant systems, the design of high performance controllers for such converters is a challenging issue. The controller should ensure system stability in any operating condition and good static and dynamic performances in terms of rejection of supply disturbances and load changes. To ensure that the controllers work well in large signal conditions and to enhance their dynamic responses, soft computing techniques such as FuzzyLogiccontroller (FLC) and ParticleSwarmOptimization based FLC (PSO-FLC) are suggested. In recent years, Fuzzylogic has emerged as an important artificial intelligence tool to characterize and control a system, whose model is not known or ill defined. Fuzzylogic is expressed by means of if-then rules with the human language. In the design of a fuzzylogiccontroller, the mathematical model is not necessary. However, the rules and the membership functions of a fuzzylogiccontroller are based on expert experience or knowledge database. To ensure better performance of fuzzycontroller, membership functions, control rules, normalizing and de-normalizing parameters are optimizedusing PSO. The main strength of PSO is its fast convergence than the other global optimization algorithms. To exhibit the effectiveness of proposed algorithm, the performance of the PSO based fuzzylogiccontroller has been compared with FLC and the necessary results are presented to validate the PSO for control purposes. Comparative study emphasize that the optimized PSO based fuzzycontroller provide better performance and superior to the other control strategies because of fast transient response, zero steady state error and good disturbance rejection under variations of line and load and hence output voltage regulation is achieved. Simulation studies have been performed using Matlab-Simulink software.
The controller is an important component in the nonlinear controlsystem, especially for the s ys tem that needs a ccuracy i n position tra cking. El ectro-HydraulicActuator (EHA) s ys tem i s a popular nonlinear s ystem that is used by researchers. Proportional- Integral-Derivative (PID) controller is the most popular controller that is normally used i n the i ndustry. This i s mainly because of i ts simplicity i n the design process. However, there are three constants that need to be assigned in the PID controller, often we called thi s as the parameters s election process or the PID tuning process. In this paper, a compa rison s tudy for the s election process of the PID pa ra meters process will be conducted a mong Zi egler-Nichols tuni ng method, conventional Pa rti cle Swarm Opti mization (PSO) technique and Pri ority-based Fitness Pa rticle SwarmOptimization (PFPSO) technique. PFPSO is one of the i mproved versions of the conventional PSO technique. The s imulation study wi ll be conducted on a nonlinear Electro -Hydraulic Actua tor (EHA) system. A simple robustness test on the PID controller will be evaluated i n terms of actuator i nternal leakage. Results s howed that the PID performed better when its controller’s parameters are selected using PFPSO technique rather than the Zi egler-Nichols method and conventional PSO technique.
Conducting polymers actuators (CPAs) are potential candidates for replacing conventional actuators in various fields, such as robotics and biomedical engineering, due to their advantageous properties, which includes their low cost, light weight, low actuation voltage and biocompatibility. As these actuators are very suitable for use in micro-nano manipulation and in injection devices in which the magnitude of the force applied to the target is of crucial importance, the force generated by CPAs needs to be accurately controlled. In this paper, a fuzzylogic (FL) controller with a Mamdani inference system is designed to control the blocking force of a trilayer CPA with polypyrrole electrodes, which operates in air. The particleswarmoptimization (PSO) method is employed to optimize the controller's membership function parameters and therefore enhance the performance of the FL controller. An adaptive neuro-fuzzy inference system model, which can capture the nonlinear dynamics of the actuator, is utilized in the optimization process. The optimized Mamdani FL controller is then implemented on the CPA experimentally, and its performance is compared with a non-optimizedfuzzycontroller as well as with those obtained from a conventional PID controller. The results presented indicate that the blocking force at the tip of the CPA can be effectively controlled by the optimized FL controller, which shows excellent transient and steady state characteristics but increases the control voltage compared to the non-optimizedfuzzy controllers.
Abstract—This paper presents a modified sliding mode controller (MSMC) for tracking purpose of electro-hydraulicactuatorsystem with mismatched disturbance. The main contribution of this study is in attempting to find the optimal tuning of sliding surface parameters in the MSMC using a hybrid algorithm of particleswarmoptimization (PSO) and gravitational search algorithms (GSA), in order to produce the best system performance and reduce the chattering effects. In this regard, Sum square error (SSE) has been used as the objective function of the hybrid algorithm. The performance was evaluated based on the tracking error identified between reference input and the system output. In addition, the efficiency of the designed controller was verified within a simulation environment under various values of external disturbances. Upon drawing a comparison of PSOGSA with PSO and GSA alone, it was learnt that the proposed controller MSMC, which had been integrated with PSOGSA was capable of performing more efficiently in trajectory control and was able to reduce the chattering effects of MSMC significantly compared to MSMC- PSO and MSMC-GSA, respectively when the highest external disturbance, 10500N being injected into the system’s actuator.
In that regards Poley (Poley, 2005) in his work developed a digital controller for an electro-hydraulicusing the digital signal processor (DSP) C2000 series which was new at that time and has a distinguishing feature that it has assisted structures for programming. Hence, the advantage of using digital systems was harnessed with relative ease, and it entailed improved performance and flexibility. In another contribution in control of the electro-hydraulic systems Bonchis (Bonchis, 2001) proposed a positioncontrollerusing variable structure algorithm for such systems with the aim of eradicating errors caused by frictional disturbances which are nonlinear in nature. In the research the load on the system and the disturbances were regarded as external perturbations, and results showed that the control scheme was effective. Zhong and He in 2008 (Zhong and He, 2008) proposed solution to arrest the time varying disturbances and nonlinearities associated with the electro- hydraulic systems by utilizing a combination of fuzzylogic and neural networks techniques. They also employed a method known as the hierarchical fuzzy error with the aim of improving the weight and convergence rate of the neuro-fuzzysystem. Results showed that the developed system was able to improve on accuracy and robustness of the system (Zhong and He, 2008). In the work of Troung and Ahn (Troung and Ahn , 2009) a sandwich of grey predictor and fuzzylogic was utilized to improve on the overshoot and settling time response of a hydraulicactuatorsystem. The proposed method also showed ability to reduce disturbances both internal and external. In another development by Guan and Pan (Guan and Pan, 2008) an adaptive sliding mode control scheme was developed for the electro-hydraulic systems to curb the effects of both linear and nonlinear unknown parameter variations in such systems. Results show that the proposed method was effective with good system stability.
Alhamdulillah. Many grateful and thanks to Allah SWT for His continuous blessing and giving me the consent to complete this Final Year Project. This Final Year Project was organized by Faculty of Electrical Engineering (FKE), Universiti Teknikal Malaysia Melaka (UTeM) for student in final year to complete the undergraduate program in Bachelor of Electrical Engineering Major in Control, Instrumentation and Automation with Honors.
Production systems in industrial processes are so complex and dynamic, so that the process often have undesirable conditions due to a lack of ability on the production process controlsystem, such as the production process at the plant sap sugar. Lack of evaporation control performance or decreasing in water content in evaporator system resulting in product that is less than the maximum. This study aims to improve the performance of Proportional + Integral + Differential (PID) control to dampen the speed of the steam flow rate and do a search optimal points in the evaporation process and modeling the evaporator in mathematical. Optimization of PID control tuning parameters usingParticleSwarmOptimization (PSO) by adding a weighting factor of inertia is expected to handle nonlinear systems with evaporator undershoot response characteristics that are difficult to treat and improve the system response with overshoot, rise time is quite long and large.This study is based on previous studies that have been tested are: the PSO tuning PID control can handle the dynamics of a system that have similar to the process that is optimal tuning of PID controllerusing adaptive hybrid particleswarmoptimization algorithm , algorithm PSO to solve the problem of
The PSO technique is an evolutionary computation technique, but it differs from other well-known evolutionary computation algorithms such as the genetic algorithms. Although a population is used for searching the search space, there are no operators inspired by the human DNA procedures applied on the population. Instead, in PSO, the population dynamics simulates a ‘bird flock’s’ behavior, where social sharing of information takes place and individuals can profit from the discoveries and previous experience of all the other companions during the search for food. Thus, each companion, called particle , in the population, which is called swarm , is assumed to ‘fly’ over the search space in order to find promising regions of the landscape. For example, in the minimization case, such regions possess lower function values than other, visited previously. In this context, each particle is treated as a point in a d-dimensional space, which adjusts its own ‘flying’ according to its flying experience as well as the flying experience of other particles (companions). In PSO, a particle is defined as a moving point in hyperspace. For each particle, at the current time step, a record is kept of the position, velocity, and the best position found in the search space so far. The assumption is a basic concept of PSO . In the PSO algorithm, instead of using evolutionary operators such as mutation and crossover, to manipulate algorithms, for a d- variable optimization problem, a flock of particles are put into the d-dimensional search space
In this proposed work, 25 contaminated and 25 uncontaminated chili pepper x-ray images were taken for analysis. This research work focus on the comparison of filtering methods such as 4-connected Median filter, Weighted 4-connected Median filter along with proposed Optimized connected Median usingParticleSwarmOptimization. To determine the result on the above comparison some performance measures were calculated, by Peak signal to noise ratio (PSNR), Signal to noise ratio (SNR), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). These performance measures are calculated by above equations starting from 6 to 9. The calculations are done between the original chili x-ray image and the result of filtered image. From the above, Optimized connected Median usingParticleSwarmOptimization provides good results than the existing proposed methods. The MAE error value obtained was lower than the 4- connected median filter and the weighted 4-connected median filter. From the table 2, it infers that the PSNR value obtained was greater than all the other values and it is proved that fitness function has attained the optimization. The earlier studies proved that greater value of PSNR gives very good image quality and suitable for the next level processing. From the above analysis it is concluded that the Optimized connected Median usingParticleSwarmOptimization is considered as a best method to remove the noise in the chili pepper x-ray images.
ABSTRACT: In a photovoltaic system,the maximum power point varies with insolation and cell temperature . Maximum power point tracking (MPPT) is implemented to identify the maximum power operating point and subsequently system is operated at that particular operating voltage for maximum power gaining.Thisalgorithm is implemented in charge controllers for extracting maximum available power from PV module. Maximum power point tracking in photovoltaic systems using artificial intelligence methods are very popular. Fuzzy systems are very effective than simple conventional MPPT tracking. In the simulation part, a buck-boost converter feeding a permanent magnet dc load is achieved. The accuracy of the overall system depends on the fuzzy rule base and membership functions defined. The performance curves for comparison was obtained using MATLAB/Simulink platform.Simulation results show that fuzzy based tracking has better performance where it can facilitate the solar panel to produce a more stable power. KEYWORDS: GUI-Graphical User Interface; MPPT-Maximum power point tracker; NN- Neural Network; PV- Photovoltaic; P&O- Perturb and Observe; RCC- Ripple correlation control
5. 3. Discussion According to Figures 7 and 8, it is observed that the UPSO Algorithm leads to fast time response of the system states by minimizing the fitness function. Whereas, if the values of the parameters were chosen by trial and error, the time response of the system states converged to zero in a much longer time. In fact, the intelligent backstepping controller causes the system states to become stable in a shorter time and as a consequence, the system chaos is controlled in much shorter time. In addition, according to Figure 9, it is observed that the proposed controller produces a limited control signal for the chaos control of Duffing system. And it is because the control effort is applied in the proposed objective function. In fact, the UPSO Algorithm via the minimization of the objective function, causes the system states to become stable in a shorter time, i.e. the system chaos is controlled in a very short time and also, more limited control signal is needed to control chaos. Fast control of chaos in a very short time and having more limited control signal for this purpose, are the great advantages of the proposed controller.
where α is the reduction factor, and the initial reduced velocity ˘ v (i) (0) is set to the velocity v (i) (n+1) that caused the search space violation. Equation (3.12a) is applied starting with the first reducing iteration ˇ n = 1, until the reduced position ˘ x (i) is in the search space or the limit on the total number of reducing iterations ˘ N is reached. In the unlikely case that the final reduced position ˘ x (i) ( ˘ N ) is not in the search space, it is reinitialized at the components for which it violates the search space. By using this technique instead of simply setting the particle to the bound for which it violated the search space, we decrease the low chance of having all positions on a bound and stopping the optimization for that dimension. In particular, we use this technique and the values chosen for the reduction factor α and for the total number of reducing iterations ˘ N based on rudimentary tests. Therefore, it might be possible to find better settings for the reduction factor α and for the total number of reducing iterations ˘ N using additional tests. Better settings for the reduction factor α and for the total number of reducing iterations ˘ N would likely increase the performance of all eight implemented PSO variants because all variants use the same search space violation handling settings.
In the modern industry, electro-hydraulic actuators (EHAs) have been applied to various applications for precise position pressure/force control tasks. However, operating EHAs under sensor faults is one of the critical challenges for the control engineers. For its enormous nonlinear characteristics, sensor fault could lead the catastrophic failure to the overall system or even put human life in danger. Thus in this paper, a study on mathematical modeling and fault tolerant control (FTC) of a typical EHA for tracking control under sensor-fault conditions has been carried out. In the proposed FTC system, the extended Kalman-Bucy unknown input observer (EKBUIO) -based robust sensor fault detection and identification (FDI) module estimates the system states and the time domain fault information. Once a fault is detected, the controller feedback is switched from the faulty sensor to the estimated output from the EKBUIO owing to mask the sensor fault swiftly and retains the system stability. Additionally, considering the tracking accuracy of the EHA system, an efficient brain emotional learning based intelligent controller (BELBIC) is suggested as the main control unit. Effectiveness of the proposed FTC architecture has been investigated by experimenting on a test bed using an EHA in sensor failure conditions.
The distractive effect of fault increases by decreasing distance between event locations to sensitive load. To simulation of more critical conditions, two faults are simulated. The first fault is occurred just after series injection transformer and the second one is occurred in near of non-sensitive load. The first short circuit fault occurs in phase B and the other short circuit fault is between phases A and B. Fault properties are shown in Table 2. At beginning, the single objective optimization problem has been solved for minimizing voltage sag by PSO. The simulation results have been obtained with 200 runs while considering 150 particles. The results indicate improvement in the voltage SAG, but in terms of harmonic index (THD) the expectations were not satisfied.
Other control techniques in active suspension designs include nonlinearity nature of the system. Back stepping control techniques has been considered by .  proposed a work on fuzzycontrol design technique using full vehicle model for nonlinear active suspension system with hydraulicactuator.  proposed a designed controllerusing sliding mode control method; also adaptive sliding mode control was looked into by . All the results found in the literatures bring about some improvement into the system.
only a few rules to describe systems that may require several lines of conventional software codes, which reduce the design complexity [1,3,6]. Although PID control is a proficient technique for handling non-linear systems but modeling these systems is often troublesome and sometimes impossible using the laws of physics. Therefore, the use of a classical controller is not suitable for nonlinear control application. Alternatively, FuzzyLogicControl well suited for processes that are too complex for analysis by conventional techniques or when the available sources of information are interpreted qualitatively, inexactly, or uncertainly.