An effective model of the forging process is crucial for the optimal operation and health management of a hydraulic press machine (HPM). Modeling this forging process is difficult, because multiple localized nonlinear solutions and modeling of unknown complex interactions between localized regions are required. In this paper, a novel least squares support vector machine (LS-SVM) method is developed for modeling the forging process. The proposed method integrates the advantages of local LS-SVM modeling and global regularization. Local LS-SVM modeling is performed to capture the local dynamics for each local working region. Global regularization is performed to minimize the global error and improve the global generalization of the local models. These features guarantee continuity and smoothness between the local LS-SVM models and avoid over-fitting of each local LS-SVM model. The algorithm developed here is simple and may easily be added into existing HPM systems. Experiment data from a practical HPM demonstrate the effectiveness of the proposed method.
Index Terms-Forging process, least squares support vector machine (LS-SVM), multiple working regions, regularization.Manuscript Xinjiang Lu (M'12) received the B.E. and M.E. degrees in process modeling and control, robust design, integration of design and control from the
The annelid, which consists of several identical segments, exploits its soft structures to move effectively in complex natural environments. Elongation and shortening of different segments produce a reverse peristaltic wave while retractable setae generate a variable friction, enabling bidirectional crawling locomotion. Although several designs have applied soft technologies towards the construction of annelid-like robots, these robots do not exhibit the homonymous segmentation, reverse peristaltic wave and variable friction. This paper reports the development of an annelid-like soft robot based on an improved dielectric elastomer (DE) minimum energy structure actuator to have these annelidan features. Each biomimetic segment of the robot is supported by a polyethylene terephthalate (PET) frame adhered to the DE actuator. The DE actuator induces segment elongation or shortening, which causes silica gel pads attached to the PET frame to contact or separate from the ground, producing a variable friction. The designed robot, whose identical segments conform to the homonymous segmentation, achieves forward or backward movement via the cooperative efforts of all the biomimetic segments. This cooperative movement, which produces the reverse peristaltic wave, strongly resembles that of natural annelidan locomotion. In addition, the kinematic analysis of the robot is investigated. Experimental results confirm that the designed robot is capable of bidirectional and rapid locomotion. The robot can achieve a maximum velocity of 11.5 mm s −1 and a maximum velocity/mass ratio of 86.25 mm (min −1 g −1 ). Compared to other existing annelid-like soft robots, this designed robot exhibits a superior average velocity, velocity/length ratio, body length/cycle, and velocity/mass ratio, and its performance affords the best approximation to that of the natural annelid.
Hydraulic
press machines (HPMs) are a complex nonlinear system that work across
a large operation region. In such a region, input/output samples do
not easily satisfy the requirements of data-driven modeling because
of many practical constraints involved. This renders HPMs difficult
to model accurately. In this paper, an operation-region-decomposition-based
SVD/NN modeling method is proposed for this type of system. It can
produce models that work across a large operation region without input
spectra with special properties. Using this method, this operation
region is first broken down into a group of local operation regions.
Every local region is excited by its corresponding input signal. Because
the complexity of the system at the local region is much lower than
the original system throughout the operation region, the required
input signal for modeling at a local region is easier to obtain than
the one suitable for the whole region. An SVD/NN modeling method is
then proposed to produce a low-order model from these experiments
at all local operation regions. Finally, a practical HPM experiment
was used to demonstrate the effectiveness of the proposed method.
A novel integration of design and control is proposed for the nonlinear process under uncertainty. The fuzzy modeling method is first employed to approximate the process, upon which fuzzy control rules are developed to achieve the stability, robustness and feasibility. Then, the steady-state economic design and the control system design are integrated into a unified objective function, which can guarantee the desirable economic and dynamic performances. Finally, the proposed method is compared with the traditional sequential method and an existing integration method on controlling the temperature profile of a nonlinear curing process. The comparison demonstrates that the proposed method will have the better performances than the other two methods.
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