This paper proposes an enhancement of the Bouc-Wen hysteresis model to capture the frequency-dependent hysteretic behavior of a thin bimorph-type piezoelectric actuator which also exhibits odd harmonic oscillation (OHO) at specific input frequencies. The odd harmonic repetitive controller has recently been proposed to compensate for the hysteresis, and attenuates the OHO of the piezoelectric actuator for which the hysteresis nonlinearity is regarded as a disturbance. This paper proposes an alternate treatment of the hysteresis compensation with the attenuation of the OHO observed at some input frequencies. It will be shown that the proposed compensator fully utilizes the mathematical structure of the enhanced Bouc-Wen model proposed in this paper to compensate the hysteresis and to attenuate the OHO. The results of the hysteresis compensation experiment illustrate the excellent performance of the proposed control system, especially at the frequencies where OHO is conspicuous.Actuators 2018, 7, 37 2 of 16 in both the modeling and compensation of various hysteresis-related phenomena. Rakotondrabe [8] proposed a control system to compensate hysteresis nonlinearity using the Bouc-Wen model. His work can be classified as feedforward control in the control engineering context. His excellent contribution owes its theoretical basis to the structure of the Bouc-Wen model, and there is no need to synthesize the inverse hysteresis model to cancel the hysteresis. The authors of the current paper recently proposed an extension of the Bouc-Wen model [9] to capture the behavior of a thin bimorph-type piezoelectric actuator which exhibits frequency-dependent hysteresis, and synthesized a compensator based on the idea proposed by Rakotondrabe. Hadineza et al. [10] formulated the multi-variable generalized Bouc-Wen model and used it in the control of their experimental plant, in which multiple piezoelectric actuators are installed.Recently, Li et al. [11] reported the existence of a special form of frequency-dependent hysteresis nonlinearity in their piezo-driven nanopositioning stage which is referred to as the odd harmonic oscillation (OHO). We have also observed the odd harmonic oscillation with our bimorph piezoelectric actuator (e.g., the response to a 23 Hz pure sinusoidal input shown in Figure 18 [9]). Li clearly stated that the odd harmonic oscillation is caused by the hysteresis nonlinearity of the piezoelectric actuator, but they treated it as a disturbance and synthesized an odd harmonic repetitive controller to attenuate the odd harmonic oscillation. We are highly motivated by the work of Li et al., as we believe that attenuation of the odd harmonic oscillation can be treated in the course of model-based hysteresis compensation. The present paper accordingly addresses the results of our effort on modeling the frequency-dependent hysteresis of a thin bimorph piezoelectric actuator which also exhibits OHO. We will hereafter refer to the model proposed in this paper as the enhanced Bouc-Wen model.We will also propose a co...
This study aimed to develop a six degrees-of-freedom (6DoF) robotic moving phantom for evaluating the dosimetric impact of intrafraction rotation during respiratory-gated radiotherapy with real-time tumor monitoring in the lung. Materials and Methods: Fifteen patients who had undergone respiratory-gated stereotactic body radiotherapy (SBRT) with the SyncTraX system for lung tumors were enrolled in this study. A waterequivalent phantom (WEP) was set at the tip of the robotic arm. A log file that recorded the three-dimensional positions of three fiducial markers implanted near the lung tumor was used as the input to the 6DoF robotic moving phantom. Respiratory-gated radiotherapy was performed for the WEP, which was driven using translational and rotational motions of the lung tumor. The accuracy of the 6DoF robotic moving phantom was calculated as the difference between the actual and the measured positions. To evaluate the dosimetric impact of intrafraction rotation, the absolute dose distributions under conditions involving gating and movement were compared with those under static conditions. Results: For the sinusoidal patterns, the mean AE standard deviation (SD) of the root mean square errors (RMSEs) of the translation and rotation positional errors was <0.40 mm and 0.30°, respectively, for all directions. For the respiratory motion patterns of 15 patients, the mean AE SD of the RMSEs of the translation and rotation positional errors was <0.55 mm and 0.85°, respectively, for all directions. The c 3%/2mm values under translation with/without gating were 97.6 AE 2.2%/ 80.9 AE 18.1% and 96.8 AE 2.3%/80.0 AE 17.0% in the coronal and sagittal planes, respectively. Further, the c 3%/2mm values under rotation with/without gating were 91.5 AE 6.5%/72.8 AE 18.6% and 90.3 AE 6.1%/72.9 AE 15.7% in the coronal and sagittal planes, respectively. Conclusions: The developed 6DoF robotic phantom system could determine the translational and rotational motions of lung tumors with high accuracy. Further, respiratory-gating radiotherapy with real-time tumor monitoring using an internal surrogate marker was effective in compensating for the translational motion of lung tumors but not for correcting their rotational motion.
In this study, we assess a developed novel dynamic moving phantom system that can reproduce patient three-dimensional (3D) tumor motion and patient anatomy, and perform patient-specific quality assurance (QA) of respiratory-gated radiotherapy using SyncTraX. Three patients with lung cancer were enrolled in a study. 3D printing technology was adopted to obtain individualized lung phantoms using CT images. A water-equivalent phantom (WEP) with the 3D-printed plate lung phantom was set at the tip of the robotic arm. The log file that recorded the 3D positions of the lung tumor was used as the input to the dynamic robotic moving phantom. The WEP was driven to track 3D respiratory motion. Respiratory-gated radiotherapy was performed for driving the WEP. The tracking accuracy was calculated as the differences between the actual and measured positions. For the absolute dose and dose distribution, the differences between the planned and measured doses were calculated. The differences between the planned and measured absolute doses were <1.0% at the isocenter and <4.0% for the lung region. The gamma pass ratios of γ 3 mm/3% and γ 2 mm/2% under the conditions of gating and no-gating were 99.9 ± 0.1% and 90.1 ± 8.5%, and 97.5 ± 0.9% and 68.6 ± 17.8%, respectively, for all the patients. Furthermore, for all the patients, the mean ± SD of the root mean square values of the positional error were 0.11 ± 0.04 mm, 0.33 ± 0.04 mm, and 0.20 ± 0.04 mm in the LR, AP, and SI directions, respectively. Finally, we showed that patient-specific QA of respiratory-gated radiotherapy using SyncTraX can be performed under realistic conditions using the moving phantom.
The present note addresses the development of a lung tumor position predictor to be used in dynamic tumor tracking radiotherapy, abbreviated as DTT-RT. As there exists 50–500 ms positioning lag in the control of the multi-leaf collimator (MLC) of commercial medical linear accelerators, prediction of future lung tumor position with sufficiently long prediction horizon is inevitable for the successful implementation of DTT-RT. The present article proposes a lung tumor position predictor, which is classified as a nonlinear autoregressive model with exogenous input (NARX). The proposed predictor was trained using seven lung tumor motion trajectories of patients who underwent respiratory gated radiotherapy at Yamaguchi University Hospital. We considered three different prediction horizons, 600 ms, 800 ms and 1 s, which were sufficiently long to compensate for the possible positioning control lag of the MLC. A patient-specific model corresponding to an intended prediction horizon was obtained by training it using the selected tumor motion trajectory with the specified horizon. Accordingly, we obtained three NARX predictors for a single patient. We calculated two performance metrics: the RMS prediction errors and the rate of coverage of the entire tumor trajectory defined by the number of samples of the measured tumor position which was inside the 4 mm cube centered at the corresponding predicted tumor position. The latter quantifies the feasibility of the predictors to generate future gating cubes in the implementation of DTT-RT. The (mean standard deviation) values of the rates of 600 ms, 800 ms and 1 s prediction horizon calculated using the proposed NARX predictors were %, % and %, respectively.
This paper proposes an algorithm to generate the augmented reference position trajectory for the realtime 3D robotic phantom system which is used in the quality assurance of the radiation therapy. Quality assuarance is the important clinical part of the radiation therapy which ensures the delivery of the prescribed dose to the tumor of the patient. High precision quality assurance can be a difficult task if the tumor exhibits respiratory induced motion inside the body of the patient, as the motion fluctuates along with time and it suffers large inter-patient difference. This paper presents several algorithms to modify and/or correct the reference tumor trajectory of the patient and generate the augmented reference trajectory sequence for the already existing and tuned control system of the robot manipulator to yield high precision tracking to the original tumor motion trajectory. Experimental validation has been carried out for four lung tumor trajectories in the clinical environment using equipments used in the radiotherapy treatment and it has been shown that the clinical demand on the precision of tumor motion tracking has been satisfied for all the four cases.
This paper presents a neural network based positioning control system of a piezo-ceramic actuator which exhibits hysteretic behavior. Proposed control system utilizes two neural networks with radial basis function (RBF) as their activation functions: one is used for modeling hysteretic behavior of the actuator and the other is assigned the role of a feedback controller for hysteresis compensation and tracking. The particle swarm optimization algorithm has been applied to the training of RBF-NN for modeling PZT dynamics to achieve high precision, whereas back propagation has been used for online controller parameters update. An internal model control (IMC) structure is employed which combines aforementioned two neural networks for positioning control of the actuator. Results of the positioning control simulation of PZT will be shown to indicate the validity of the proposed two RBF-NN internal model control system.
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