Design high frequency surgical robot controller: design FPGA-based controller for surgical robot manipulator simscape modeling

Recent developments of robotics allocated many of industrial and medical activities. So that most of industries turned to use surgical robots in their production line or in their surgery. Being precise, spent less time-consuming, present uniform quality with less cost and reducing waste and energy are some advantages of using robots in industry. This paper has two important objectives: a) study on modeling and controlling of 4 degrees of freedom (DOF) based on Simscape software and b) design FPGA-based controller for this type of surgical robot manipulator. Simscape provides an environment for modeling and simulating physical systems. Simscape modeling can be designed to control and test system-level performance. Conventional PID controller is a stable linear type model-free controller that reduces the delay time in highly nonlinear system. In this research, linear controller need real time mobility operation, and one of the most important devices which can be used to solve this challenge is Field Programmable Gate Array (FPGA). FPGA can be used to design a controller in a single chip Integrated Circuit (IC). To design PID type FPGA-based controller two types algorithm are needed: derivative algorithm and integral algorithm. In HDL based derivative algorithm the minimum input arrival time before clock is 16.466 ns and the maximum frequency is 60.73 MHz, but in the best design action, the maximum frequency to design this single chip algorithm should be 63.629 MHz. In HDL integral algorithm the minimum input arrival time before clock is 15.599 ns and the maximum frequency is 64.1 MHz, but in the best design action, the maximum frequency to design this single chip algorithm should be 178.190 MHz

Adaptive MIMO Fuzzy Compensate Fuzzy Sliding Mode Algorithm: Applied to Second Order Nonlinear System.

This research is focused on proposed adaptive fuzzy sliding mode algorithms with the adaptation laws derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Adaptive MIMO fuzzy compensate fuzzy sliding mode method design a MIMO fuzzy system to compensate for the model uncertainties of the system, and chattering also solved by linear saturation method. Since there is no tuning method to adjust the premise part of fuzzy rules so we presented a scheme to online tune consequence part of fuzzy rules. Classical sliding mode control is robust to control model uncertainties and external disturbances. A sliding mode method with a switching control low guarantees the stability of the certain and/or uncertain system, but the addition of the switching control low introduces chattering into the system. One way to reduce or eliminate chattering is to insert a boundary layer method inside of a boundary layer around the sliding surface. Classical sliding mode control method has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a sliding mode controller and artificial intelligence (e.g. fuzzy logic). To approximate a timevarying nonlinear dynamic system, a fuzzy system requires a large amount of fuzzy rule base. This large number of fuzzy rules will cause a high computation load. The addition of an adaptive law to a fuzzy sliding mode controller to online tune the parameters of the fuzzy rules in use will ensure a moderate computational load. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. Asymptotic stability of the closed loop system is also proved in the sense of Lyapunov

Comparative study between ARX and ARMAX system identification

System Identification is used to build mathematical models of a dynamic system based on measured data. To design the best controllers for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. System identification is divided into different algorithms. In this research, two important types algorithm are compared to identifying the highly nonlinear systems, namely: Auto-Regressive with eXternal model input(ARX) and Auto Regressive moving Average with eXternal model input (Armax) Theory. These two methods are applied to the highly nonlinear industrial motor

Artificial Tune of Fuel Ratio: Design a Novel SISO Fuzzy Backstepping Adaptive Variable Structure Control

This paper examines single input single output (SISO) chattering free variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm. VSC methodology is selected as a framework to construct the control law and address the stability and robustness of the close loop system based on Lyapunove formulation. The main goal is to guarantee acceptable fuel ratio result and adjust. The proposed approach effectively combines the design technique from variable structure controller is based on Lyapunov and fuzzy estimator to estimate the nonlinearity of undefined system dynamic in backstepping controller. The input represents the function between variable structure function, error and the rate of error. The outputs represent fuel ratio, respectively. The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. The performance of the SISO VSC which controller coefficient is on-line tuned by fuzzy backstepping algorithm (FBSAVSC) is validated through comparison with VSC and proposed method. Simulation results signify good performance of trajectory in presence of uncertainty torque load. DOI:http://dx.doi.org/10.11591/ijece.v3i2.209

Robust auto-intelligent sliding accuracy for high sensitive surgical joints

The objective of this paper is to design and coordinate controllers that will enhance transient stability of three dimensions motor subject to large disturbances. Two specific classes of controllers have been investigated, the first one is a type of disturbance signals added to the excitation systems of the generating units. To address a wide range of operating conditions, a nonlinear control design technique, called highly nonlinear computed torque control, is used. While these two types of controllers improve the dynamic performance significantly, a coordination of these controllers is even more promising. Results show that the proposed control technique provides better stability than conventional computed torque fixed gain controllers

Design sensor-less PID filter controller for first order delays system

The dynamics of a first order delay system is highly nonlinear, time variant, uncertain and coupling effects. The main objectives to control of first order delay system are time response and acceleration measurements. The problem of acceleration measurements can be reduced, based on design sensor-less Proportional-Integral-Derivative (PID) filter controller in this research. Assuming unstructured uncertainties and structure uncertainties can be defined into one term and considered as an uncertainty and external disturbance, the problem of computation burden and large number of parameters can be solved to some extent. To solve the uncertainties acceleration measurements play an important role. In order to design sensor-less PID filter controller, an accurate PD surface and the derivative of PD surface plays important role. To design an accurate PD surface, stable and tuning surface slope is needed to form the structure of main PID controller. In this algorithm, the derivative of PD surface computes the second derivation of error. Regarding to this method, the challenge of system uncertainties and time response have been solved based on sensor-less acceleration linear filter controller. As this point if s = K1e + e + K2Σe is chosen as desired surface, if the dynamic of first order delay is derived to surface then the linearization can be realized. Because, when the system dynamic is on the surface is used the derivative of surface S = K1e + e + K2e is equal to the zero that is a decoupled and linearized closed-loop systems dynamics. Linearization and decoupling by the above method can be obtained in spite of the quality of the first order delay dynamic model

Methodologies of chattering attenuation in sliding mode controller

Uncertain or complicated systems are difficult to control. Modeling the system uncertainties is an especial topics in most of engineering field. On the other hand, since system has uncertainty, design stable and robust controller is crucial importance in control engineering. To solve this challenge nonlinear control technique is the best choice. Sliding mode control is one important type of robust control. Model imprecision may come from actual uncertainty about the plant or from a purposeful simplification of the system's dynamics. Modeling inaccuracies can cause strong adverse effects on the control design of nonlinear systems. For the class of systems to which it applies, sliding mode controller design provides a systematic approach to the problem of maintaining stability and consistent performance in the face of modeling imprecision. However, sliding mode controller is a robust and stable controller but it has an important challenge called, chattering phenomenon. This research focuses on the comparative between three methods to eliminate/reduce the chattering

Prevent the risk of lung cancer progression based on fuel ratio optimization

Lung cancer, also known as carcinoma of the lung or pulmonary carcinoma, is a malignant lung tumor characterized by uncontrolled cell growth in tissues of the lung. Cancer develops following genetic damage to DNA and epigenetic changes. These changes affect the normal functions of the cell, including cell proliferation, programmed cell death (apoptosis) and DNA repair. As more damage accumulates, the risk of cancer increases. Smoking, radon gas, asbestos, air pollution and genetics are the main causes to increase the rate of lung cancer. Outdoor air pollution has a large effect on increasing the risk of lung cancer. In this research, intelligent technique is presented as robust control of fuel ratio for internal combustion (IC) engine. Computed fuel-ratio controller (CFC) is one of the types of feedback linearization nonlinear controller. This controller works very well in certain positions. The main challenge in CFC is instability in presence of uncertainties. In this research low pass filter is used to improve the stability in CFC. To improve the result of this controller intelligent CFC is recommended based on fuzzy logic technique. In this research fuzzy logic theory is used to tune the new low pass filter CFC coefficients. The process of setting of integral intelligent Computed Fuel-ratio Controller can be determined as an optimization task

Research on nonlinear automation for first order delays system

First order delay system (FODS) is in class of nonlinear systems. In these systems design control algorithms are very important. In this research nonlinear terms of incremental Proportional Integral Derivative (PID) algorithm is used to nonlinear modelfree integrate large amounts of control methodology in a single methodology. This work, proposes a developed method to design nonlinear based PID controller. In this methodology nonlinear model-free sliding mode algorithm help incremental PID to estimate and linearization of first order delay system. According to this research, the controller robustness improved based on nonlinear term of sliding mode algorithm and the chattering is reduced/eliminate based on PID incremental method

LDDNet: a deep learning framework for the diagnosis of infectious lung diseases.

This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet