CONTROLLER PARAMETER OPTIMIZATION FOR AN ELECTRO-HYDRAULIC ACTUATOR SYSTEM BASED ON PARTICLE SWARM OPTIMIZATION TECHNIQUE

In this paper, a fault detection, identification and estimation approach has been developed for the condition monitoring of the electro-hydraulic actuator (EHA) system using the multiple-model (MM) estimation algorithm. The MM estimation algorithm makes use of the extended Kalman filter (EKF) technique to generate estimates of states and key physical parameters, which are related to faults in the EHA system. The proposed fault detection and identification (FDI) is formulated as a hybrid interacting multiple-model estimation scheme. The interaction scheme between multiple models is introduced into the MM estimation algorithm to yield more robust detection and estimation. Estimates of the key physical parameters in the EHA system are assessed against baseline values and fused with the FDI results for higher level monitoring purposes. Two parameters of interests, namely torque motor equivalent resistance and the effective bulk modulus are investigated for the EHA system condition monitoring purpose. The simulation results highlight the considerable potential of the proposed technique for achieving improved condition monitoring of the EHA system.

Exoskeleton robots have been developed in various fields and are divided into electric and hydraulic exoskeletons according to the actuator type. In the case of hydraulic robots, because a unidirectional pump is applied, there are limitations to the wearer’s walking. In addition, robot systems are complicated, because a directional control valve is required to change the direction of the actuator. To solve these problems, we designed the electro-hydraulic actuator (EHA) system which has both the hydraulic and electric advantages. The EHA system consists of a hydraulic bidirectional pump, a motor, a hydraulic cylinder, and various valves. For the development of the piston pump, we analyzed the gait cycle and considered the flow rate and pulsation rate. In order to reduce the size and weight of the EHA system, the valves were made from one manifold, and the hydraulic circuit was simplified. We verified that the developed EHA system is applicable to robots through position and force control experiments. Because the hydraulic system is nonlinear, we designed a sliding mode control (SMC) and compared it with the proportional integral derivative (PID) controller.

Precise control of electro-hydraulic actuator (EHA) system has been an interesting subject due to its nonlinearities and uncertainties characteristics. Good control can be designed when precise model of the system is available. ANFIS (Adaptive Neuro-Fuzzy Inference System) modeling technique has proven to be able to model various nonlinear systems. The objective of this paper is to obtain an ANFIS model from EHA system stimulus-response data. The prediction ability of ANFIS is also tested using limited data set for model training. Results show that ANFIS model is able to estimate nonlinear EHA system with high precision, even when training data provided is limited.

This paper studies the design of a robust discrete-time sliding mode control (DT-SMC) for a high precision electro-hydraulic actuator (EHA) system with nonlinear actuator friction. Nonlinear friction in the hydraulic actuator can greatly influence the performance and accuracy of the hydraulic actuators; however, it is difficult to accurately model nonlinear friction characteristics. In this paper, it is proposed to characterize frictions as an uncertainty in the system matrices. Indeed, the effects of variations of the nonlinear friction coefficients are considered as norm bounded uncertainties that span a bounded region to cover a wide range of real actuator friction. For such a discrete-time dynamic model for the EHA system with system matrices uncertainties and a nonlinear term, a sufficient condition for the existence of stable sliding surfaces is proposed by using the linear matrix inequality (LMI) approach. Based on this existence condition, a discrete-time sliding mode controller is developed such that the reaching motion satisfies the discrete-time sliding mode reaching condition for uncertain systems. Simulation and comparison studies on the EHA system model illustrate the effectiveness of the proposed method. The study is simulation based only as it is important to establish the feasibility and stability of the controller before attempting to apply the controller to a physical system.

The steam turbine control system is strongly non-linear in all operating conditions. Proportional-Integral-Derivative (PID) controller that is currently used in control systems of many types of equipment is not considered highly precision for turbine speed control system. A fine tuning of the PID controller by some optimization technique is a desired objective to maintain the precise speed of the turbine in a wide range of operating conditions. This Paper evaluates the feasibility of the use of Particle Swarm Optimization (PSO) method for determining the optimal Proportional-Integral-Derivative (PID) controller parameters for steam turbine control. The turbine speed control is modelled in SimulinkTM with PID controller and the PSO algorithm is implemented in MATLAB to optimize the PID function. The PSO optimization technique is also compared with Genetic Algorithm (GA) and it is validated that PSO based controller is more efficient in reducing the steady-states error; settling time, rise time, and overshoot limit in speed control of the steam turbine control.

Electro-hydraulic servo actuation system is a mechanical, electrical and hydraulic mixing complex system. If it can't be repaired for a long time, it is necessary to consider the possibility of occurrence of multiple faults. Considering this possibility, this paper presents an extended Kalman filter (EKF) based method for multiple faults diagnosis. Through analysing the failure modes and mechanism of the electro-hydraulic servo actuation system and modelling selected typical failure modes, the relationship between the key parameters of the system and the faults is obtained. The extended Kalman filter which is a commonly used algorithm for estimating parameters is used to on-line fault diagnosis. Then use the extended Kalman filter to diagnose potential faults. The simulation results show that the multi-fault diagnosis method based on extended Kalman filter is effective for multi-fault diagnosis of electro-hydraulic servo actuation system.

A draw wire sensor is considered as a contact measurement method. It is normally used to measure the speed and position of a system. A draw wire sensor is convenient especially when a low cost solution and a small sensor dimension are desired. The objective of this paper is to describe the application of draw wire sensor in control tracking of electro hydraulic actuator system. This research started with the modelling of electro hydraulic actuator system by using system identification approach. During the data taking process, an experiment is conducted using electro hydraulic actuator test bed. A draw wire sensor is attached to the load of electro hydraulic actuator system to measure the output displacement when the system is injected with the desired input signal. Draw wire sensor is measuring the output displacement in millimeters and then the signal is converted to voltage reading regarding to the given input signal. The input and output signal is collected and is used in system identification technique to obtain the best mathematical model that can represent the electro hydraulic actuator system. Once a model is obtained, a Self Tuning Controller (STC) with Generalize Minimum Variance Control (GMVC) strategy is designed to control the tracking performance of the electro hydraulic actuator system. The designed controller is tested in simulation and experiment mode. Then, the output result from both modes is compared. The results show that the output performance from both modes are almost similar. Thus, this research had shown that a draw wire sensor has a significant role in capturing an accurate output data from electro hydraulic actuator system even with or without the controller.

Genetic algorithms (GA) and particle swarm optimization (PSO) are the most famous optimization techniques among various modern heuristic optimization techniques. These two approaches identify the solution to a given objective function, but they employ different strategies and computational effort; therefore, a comparison of their performance is needed. This paper presents the application and performance comparison of the PSO and GA optimization techniques for a static synchronous series compensator-based controller design. The design objective is to enhance power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem, and both PSO and GA optimization techniques are employed to search for the optimal controller parameters.

This paper presents the position tracking performance of an electro-hydraulic hydraulic actuator (EHA) system by using a discrete sliding mode control (DSMC). A point-to-point trajectory is used in the experimental works to evaluate the performance of the DSMC which is design in two-degree-of-freedom (2-DOF) control structure. The design consists of DSMC and a feed forward controller. The control law is derived from the reaching law concept and its parameters are determined methodically based on the identified model. Experimental results reveal that the DMSC with 2-DOF control structure is highly robust and capable to deal with the uncertainties and disturbances occur during the position tracking control for different point of trajectories. It is also shows that the proposed controller can achieve better tracking performance as compared to conventional LQR and PID controller.

This paper presents a modified sliding mode controller (MSMC) for tracking purpose of electro-hydraulic actuator system 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 particle swarm optimization (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.

Firefly algorithm (FFA) optimization technique is a popular continuous optimization technique and also widely applied to tune controller gain values of proportional–integral–derivative (PID) controller. In the chaos-based firefly algorithm, the chaotic maps are included to improve the randomness when generating new solutions and thereby to increase the diversity of the population. It increases global search mobility for robust global optimization. The investigated power system incorporates two thermal power systems, and two areas are interconnected via tie-line. Also, this work system is equipped with a secondary (PID) controller. The gain value of the controller is optimized by implementing chaos-based firefly algorithm (CFFA) by applying step load in area 1. The effectiveness and supremacy of proposed optimization technique tuned controller performance are verified by comparing genetic algorithm (GA), particle swarm optimization (PSO) technique and firefly algorithm tuned PID controller performance in the same interconnected power system. The proposed optimization technique-based controller response is evaluated by considering time domain specifications parameters of the proposed optimization technique-based controller response. Further, effectiveness of recommended optimization technique is verified by adding nonlinearity’s [generation rate constraint (GRC) and governor dead band (GCB)] in the investigated power system with connecting unified power flow controller (UPFC) is applied, and it is a flexible alternating current transmission system (FACTS) controller family device connected parallel to the tie-line. Further, hydrogen aqua electrolyzer (HAE) energy storage unit with a fuel cell is connected in area 2 for improving the performance of the system during sudden load demand. Finally, simulation result proved that the proposed CFFA technique gives better controlled performance compared with GA, PSO and FFA technique tuned controller performance in terms of fast settled response with a minimum peak over and undershoot, damping oscillations during unexpected load demand conditions.

A reduced and comprehensible polynomial neural network for classification

State-observer-based asymptotic tracking control for electro-hydraulic actuator systems with uncertainties and unmeasurable velocity

The use of speed sensors in the speed controller of three-phase induction motors affects the reliability of the induction motors. In addition, the drive engine that is often used in industry is a three-phase induction motor. So, speed sensorless control is needed for induction motors to achieve the best performance. This study uses a discrete disturbance observer (D0) as feedback on the speed sensorless control. The controller used in this method is a discrete PI with the Cohen-Coon (CC) and Ziegler-Nichols (ZN) tuning method. The purpose of this study is to obtain a comparative analysis of the CC and ziegler nichols tuning method using a discrete PI on the speed sensorless control scheme with torque load variation. This study was carried out experimentally using an Alliance AY3A-90L4 induction motor. The results show that the CC tuning method is better under parameter efficiency and robustness against disturbance and ZN is better under parameter reliability.

In recent years, genetic algorithm (GA), particle swarm optimization (PSO) and hybrid genetic algorithm particle swarm optimization (HGAPSO) have attracted considerable attention among various modern heuristic optimization techniques. In this study the HGAPSO, PSO and GA optimization techniques are used for to search the optimal placement and sizing of static VAR compensator (SVC) in power system. The objective function is defined for reducing power loss, voltage deviation and investment costs of SVC. The effectiveness of the proposed hybrid based approach is applied and demonstrated on IEEE 30 Bus network. The results show that the proposed hybrid HGAPSO compared with PSO and GA optimization for performs and giving better solution.