Improved Third Order PID Sliding Mode Controller for Electrohydraulic Actuator Tracking Control

An electrohydraulic actuator (EHA) system is a combination of hydraulic systems and electrical systems which can produce a rapid response, high power-to-weight ratio, and large stiffness. Nevertheless, the EHA system has nonlinear behaviors and modeling uncertainties such as frictions, internal and external leakages, and parametric uncertainties, which lead to significant challenges in controller design for trajectory tracking. Therefore, this paper presents the design of an intelligent adaptive sliding mode proportional integral and derivative (SMCPID) controller, which is the main contribution toward the development of effective control on a third-order model of a double-acting EHA system for trajectory tracking, which significantly reduces chattering under noise disturbance. The sliding mode controller (SMC) is created by utilizing the exponential rule and the Lyapunov theorem to ensure closed-loop stability. The chattering in the SMC controller has been significantly decreased by substituting the modified sigmoid function for the signum function. Particle swarm optimization (PSO) was used to lower the total of absolute errors to adjust the controller. In order to demonstrate the efficacy of the SMCPID controller, the results for trajectory tracking and noise disturbance rejection were compared to those obtained using the proportional integral and derivative (PID), the proportional and derivative (PD), and the sliding mode proportional and derivative (SMCPD) controllers, respectively. In conclusion, the results of the extensive research given have indicated that the SMCPID controller outperforms the PD, PID, and SMCPD controllers in terms of overall performance.

Third-order robust fuzzy sliding mode tracking control of a double-acting electrohydraulic actuator

In the industrial sector, an electrohydraulic actuator (EHA) system is a common technology. This system is often used in applications that demand high force, such as the steel, automotive, and aerospace industries. Furthermore, since most mechanical actuators' performance changes with time, it is considerably more difficult to assure its robustness over time. Therefore, this paper proposed a robust fuzzy sliding mode proportional derivative (FSMCPD) controller. The sliding mode controller (SMC) is accomplished by utilizing the exponential law and the Lyapunov theorem to ensure closed loop stability. By replacing the fuzzy logic control (FLC) function over the signum function, the chattering in the SMC controller has been considerably reduced. By using the sum of absolute errors as the objective function, particle swarm optimization (PSO) was used to optimize the controller parameter gain. The experiment results for trajectory tracking and the robustness test were compared with the sliding mode proportional derivative (SMCPD) controller to demonstrate the performance of the FSMCPD controller. According to the findings of the thorough study, the FSMCPD controller outperforms the SMCPD controller in terms of mean square error (MSE) and robustness index (RI)

Modeling and control of a modular iron bird

This paper describes the control architecture and the control laws of a new concept of Modular Iron Bird aimed at reproducing flight loads to test mobile aerodynamic control surface actuators for small and medium size aircraft and Unmanned Aerial Vehicles. The iron bird control system must guarantee the actuation of counteracting forces. On one side, a hydraulic actuator simulates the hinge moments acting on the mobile surface due to aerodynamic and inertial effects during flight; on the other side, the actuator to be tested applies an active hinge moment to control the angular position of the same surface. Reference aerodynamic and inertial loads are generated by a flight simulation module to reproduce more realistic conditions arising during operations. The design of the control action is based on a dynamic model of the hydraulic plant used to generate loads. This system is controlled using a Proportional Integral Derivative control algorithm tuned with an optimization algorithm taking into account the closed loop dynamics of the actuator under testing, uncertainties and disturbances in the controlled plant. Numerical simulations are presented to show the effectiveness of the proposed architecture and control laws

Review On Controller Design In Pneumatic Actuator Drive System

A pneumatic actuator is a device that converts compressed air into mechanical energy to perform varieties of work. It exhibits high nonlinearities due to high friction forces, compressibility of air and dead band of the spool movement which is difficult to manage and requires an appropriate controller for better performance. The purpose of this study is to review the controller design of pneumatic actuator recommended by previous researchers from the past years. Initially, the basic views of the pneumatic will be presented in terms of introduction to the pneumatic actuator and its applications in industries. At the end of this review, discussions on the design of the controllers will be concluded and further research will be proposed along with the improvement of control strategies in the pneumatic actuator systems

Model-Based Control Design of an EHA Position Control Based on Multicriteria Optimization

For the control of dynamic systems such as an Electro-Hydraulic Actuator (EHA), there is a need to optimize the control based on simulations, since a prototype or a physical system is usually not available during system design. In consequence, no system identification can be performed. Therefore, it is unclear how well a simulation model of an EHA can be used for multicriteria optimization of the position control due to the uncertain model quality. To evaluate the suitability for control optimization, the EHA is modeled and parameterized as a grey-box model using existing parameters independent of test bench experiments. A method for multi-objective optimization of a controller is used to optimize the position control of the EHA. Finally, the step responses are compared with the test bench. The evaluation of the step responses for different loads and control parameters shows similar behavior between the simulation model and the physical system on the test bench, although the essential phenomena could not be reproduced. This means that the model quality achieved by modeling is suitable as an indication for the optimization of the control by simulation without a physical system

Parameter tuning of sliding mode controller using multi-objective particle swarm optimization in electro-hydraulic actuator system

Electro-Hydraulic Actuator (EHA) system is very popular and widely applied in the modern industry applications. This is because of its advantages on the high force to weight ratio, accurate positioning with fast motion and capability in generating large torque. Due to its increasing trends in modern applications, the research to control the EHA system has attract the attentions of many researchers around the world. However, the nonlinear characteristics in the dynamics of the EHA system such as internal leakage have make it difficult to control and hard to produce an accurate output such as position, force, and speed that are required in different applications. Internal leakage existed in the servo valve can degrade the overall performance of the EHA system. Commonly, a control system either open-loop or closed-loop is the key to overcome the aforementioned issue, where researchers had proposed many types of control strategies across the years ranging from classical to advanced controller to control the nonlinear EHA system so that it can suit into different industry applications. In this research, Sliding Mode Controller (SMC) is designed and proposed for the positioning control of the established EHA system. To obtain the optimum performance of the EHA system, Multi-Objective Particle Swarm Optimization (MOPSO) is implemented to the SMC to achieve the highest position output performance with least overshoot and steady-state error. In order to verify the effectiveness of the proposed SMC with MOPSO strategy, comparison study has been implemented to Proportional Integral Derivative (PID) and SMC controllers with conventional Particle Swarm Optimization (PSO) technique. The simulation results show that the proposed control strategy is able to improve the overshoot percentage of the EHA system by 99.78% and 99.64% as compared to the PSO-PID controller and PSO-SMC respectively. Robustness tests show the proposed control strategy achieved least overshoot percentage in all simulation case studies including the mass, pressure and internal leakage variations