This manuscript introduces the application of Model Predictive Control (MPC) for high force control precision in a real industrial electro-hydraulic servo system (EHSS). Moreover, it presents a fractional order control (FOC) and conventional controllers (CC) based on genetic algorithm (GA). The GA technique has been used to tune the parameters of FOC and CC approach. In order to verify the ability of the proposed controller applied to the hydraulic press machines emulator using EHSS, a hardware implementation of a test press system is also suggested and setup to be used in this research. As a result, the study has been investigated using a simulation model then verified via the experimental implementation. In fact, the EHSS plays an important role in many industrial applications, especially in flight simulators, aircraft landing gear system, material testing machine and hydraulic press machines for which the high accuracy and fast response of the force or pressure control are exceedingly necessary. Real-time experiments on the EHSS are carried out to evaluate the proposed control approach in a large system parameters variation of working environments. Considerable improvement in the performance generated by the designed MPC controller is compared with the traditional and fractional order controllers. Moreover, the results show that the performance criteria in terms of settling, rise times, system overshoots, system parameters variation and applying different test signals are good values in case of applying MPC over using FOC and CC in this study. As a general conclusion, one can conclude that the MPC has the priority of applying it in the field of the industrial EHSS. The obtained results are promising in the field of mechatronic.
This article discusses a system identification based on a black-box state-space model for an experimental electro-hydraulic servo system. Furthermore, it presents force-tracking control for the electro-hydraulic servo system based on model predictive control. The parameters of model predictive controls have been tuned by cuckoo search algorithm as well as genetic algorithm. The realization of model predictive controls depends on using a data acquisition card (NI-6014) and Simulink/MATLAB as the core of the electro-hydraulic servo system control system. In this research, the combination of model predictive control tuned by cuckoo search algorithm and genetic algorithm has been introduced in the form of switching model predictive controls. This combination collects the advantages of two model predictive controls in one model predictive control by switching model predictive controls. The simulation and experimental results display that the suggested switching of model predictive controls introduces a good tracking performance in terms of settling time, rise time, and system overshoots as compared to the two separated model predictive controls. In addition, the experimental evaluation has shown that the proposed switching model predictive controls achieved a stable and robust control system even facing to a different reference command signals (step, multistep, and sinusoidal signals). Moreover, its behavior is more robust for system parameters perturbation and small or large perturbation of disturbances in the working environment. It also achieves the necessitated physical limits of the actuator. As a general conclusion and a deep study of electro-hydraulic servo system, one can conclude that the switching strategy between model predictive control tuned by cuckoo search algorithm and by genetic algorithm has the priority of applying it on the field of electro-hydraulic servo system. The proposed new strategy (switching of model predictive control) is promising in experimental applications.
This paper presents the development and experimental validation of a modeling approach that was proposed to predict the surface generation process during ultraprecision turning. In particular, in addition to the kinematic paramters, the proposed model takes into consideration the effects of the minimum chip thickness and elastic recovery along side their associated uncertainity attributable to the blend nature of the multi-phase materials. The model amis to eliminate the contribution of the uncertainty errors due to the stochastic behavior of the phases presents within the material microstructe. Thus, it allows predicting the achievable surface roughness more preciously under different cutting conditions. The developed model was experimentally validated by machining dual-phase material, Brass 6040, under a range of processing parameters. The roughness of the generated surface was measured and compared with those estimated by the model under similar conditions. Prelimenrary implementation of the model indicated that the model predictions relatively agreed with the experimental results. After conducting a calibration procedure, lower error was obtained 20.45%. However, by excluding the results at very low feed rates to duduct its erratic influence, the average error substantially reduced to 11.18% using cutting tools with nose radius of 200 µm.
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