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Sliding mode control(SMC) are carried out in this literature. And to make the controllers perform better, fuzzy logic was chosen,it makes PID controller auto-tuning parameters and reduced the chattering problem of sliding mode control. Since SMC take error and derivative of error as inputs, after comparison some results are obtained.PID controller response faster yet sliding mode control is much steadier. However certain problems cannot be ignored that the chattering phenomenal cannot be reduced entirely and this motion may hurt the machine; this project only considered a simple system, there is no guarantee PID can work as well as in this case for a much more complex system. MATLAB simulink was the main approach to obtain the performance of the two controllers: to observe the control output of the two controllers, electric circuit and special controllers are designed and tested in MATLAB.

Recent development of robot technology is revolutionizing the medical field. The concept of using robot assistance in medical surgery has been receiving more and more recognition throughout the world. Robot-assisted surgery has the advantage of reducing surgeons' hand tremor, decreasing post-operative complications, reducing patients' pains, and increasing operation dexterity inside the patients' body. Robotic assistants have been broadly used in many medical fields such as orthopedics, neurology, urology and cardiology, and robot assisted surgery is keeping expanding its influences in more general medical field. Refer to this research, auxiliary sliding variable sliding mode controller is proposed for multi DOF joint with application in surgical robot manipulator. The main problem in this research is design robust chattering free sliding mode controller. The chattering phenomenon problem is reduced in certain/uncertain system by using auxiliary sliding variable. The simulation results exhibit that the sliding mode controller with auxiliary sliding variable works well in certain and uncertain condition.

In order to improve the system performance affected by the model uncertainties and loading moments, a robust tracking controller is proposed for electro-hydraulic servo system. Considering the conventional disturbance observer (DOB) lacks the enough ability in estimating the whole disturbances in electro-hydraulic servo system, one global sliding mode controller (GSMC) is proposed based on the inner-loop DOB. The GSMC not only suppresses the remainder disturbances which cannot be observed by DOB, but also realizes the position tracking of electro-hydraulic servo system. Simulation results present that, compared with other traditional control schemes, the proposed robust control scheme can ensure electro-hydraulic servo system possess higher tracking precision. Moreover, there is no obvious high-frequency chattering at control input, therefore it also possesses higher application value.

Sliding mode controller (SMC) is a highly significant nonlinear controller under condition of certain and partly uncertain dynamic parameters of system. This paper focused on design and implementation of a sliding mode controller for STATCOM. Intend for its application to wind farm connection to the grid. The proposed inverse system sliding mode controller based on nonlinear feedback linearization technology for system decoupling, which enhance the transient stability of wind farms and stabilizes the grid voltage about the given operating point when the fault occurs closer to the wind farms. The simulation results have shown the improved performance of the proposed controller has better control effectiveness and efficiency compared with the conventional controller of inverse system , the proposed controller on wind power system and which assist in improving capability of the wind farms to ride through disturbances, dynamic power flow control of the transmission lines, enhancing power oscillation damping under a sudden fault. Moreover, the proposed control scheme is very simple and easy to implement.

Most of nonlinear controllers need real time mobility operation so 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).Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a FPGA-based chattering free mathematical error-based tuning sliding mode controller (MTSMC) for highly nonlinear dynamic robot manipulator, in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller is selected. Pure sliding mode controller can be used to control of partly known nonlinear dynamic parameters of robot manipulator. Conversely, pure sliding mode controller is used in many applications; it has an important drawback namely; chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers. In order to reduce the chattering this research is used the switching function in presence of mathematical error-based method instead of switching function method in pure sliding mode controller. The results demonstrate that the FPGA-based sliding mode controller with switching function is a model-based controllers which works well in certain and partly uncertain system. Pure sliding mode controller has difficulty in handling unstructured model uncertainties. To solve this problem applied mathematical model-free tuning method to FPGA-based sliding mode controller for adjusting the sliding surface gain ( ). Since the sliding surface gain ( ) is adjusted by mathematical model free-based tuning method, it is nonlinear and continuous. In this research new is obtained by the previous multiple sliding surface slopes updating factor . FPGA-based Chattering free mathematical error-based tuning sliding mode controller is stable controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in mathematical error-based tuning sliding mode controller with switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.8 second, steady state error = 1e-9 and RMS error=1.8e-12). To have higher implementation speed with good performance TVSC is implemented on Spartan 3E FPGA using Xilinx software (controller computation time=30.2 ns, Max frequency=63.7 MHz and controller action frequency=33 MHZ).

Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a robust rule base fuzzy-based tuning fuzzy sliding mode controller for second order nonlinear system in presence of uncertainties. Rule base is one of the important key factors in design of fuzzy based tuning controller. In order to provide high performance nonlinear methodology, fuzzy sliding mode controller is selected. Fuzzy sliding mode controller can be used to control of partly unknown nonlinear dynamic parameters of nonlinear system. Error-based fuzzy sliding mode controller has difficulty in handling unstructured model uncertainties. To solve this problem applied improve rule base fuzzy-based tuning method to error-based fuzzy sliding mode controller for adjusting the rule base in adaptive fuzzy-based tuning. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.4 second, steady state error = 1.3e-11 and RMS error=1.2e-11).

Refer to this research, a linear error-based tuning sliding mode controller (LTSMC) is proposed for robot manipulator. Sliding mode controller (SMC) is an important nonlinear controller in a partly uncertain dynamic system’s parameters. Sliding mode controller has difficulty in handling unstructured model uncertainties. It is possible to solve this problem by combining sliding mode controller and adaption law which this method can helps improve the system’s tracking performance by online tuning method. Since the sliding surface gain ( ) is adjusted by new linear tuning method, it is continuous. In this research new is obtained by the previous multiple sliding surface slopes updating factor which is a coefficient varies between half to one. Linear error-based tuning sliding mode controller is stable model-based controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in linear error-based tuning sliding mode controller based on switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0%, rise time=0.4 second, steady state error = 1.8e-10 and RMS error=1.16e-12).

The main objective in this research is comparative study between three types of control methodology: proportional-integral-derivative (PID) controller, computed torque controller (CTC), and sliding mode controller (SMC) with application to active multi degrees of freedom actuators. PID controller is a linear model-free controller; to control of nonlinear system based on PID controller system linearization is the main challenge. Computed torque controller and sliding mode controller are model-base and nonlinear. In order to design computed torque controller, an accurate dynamic model of nonlinear system plays an important role. To modelling an accurate dynamic system, modelling of complex parameters is needed to form the structure of system’s dynamic model. It may be very difficult to include all the complexities in the system dynamic model. Computed torque controller is work very good in certain condition but in uncertainty, PID and CTC have some challenges. Conventional switching sliding mode controller is an apparent nominates to design a controller using the bounds of the uncertainties and external disturbance. In partly uncertainties, sliding mode controller is more robust than CTC and PID. In this paper these three types controller are test in MATLAB/SIMULINK.

In this paper, a PID model-based adaptive robust control method is proposed in order to design a high performance robust controller in the presence of structured (parametric) uncertainties and unstructured uncertainties. The approach improves performance by using the advantages of sliding mode control, adaptive control, and PID controller, while the disadvantages attributed to these methods are remedied by each other. This is achieved without increasing the complexities of the overall design and analysis of the control system (controller). The proposed controller attenuates the effect of model uncertainties from both structured uncertainties and unstructured uncertainties. Thus, transient performance and final tracking accuracy is guaranteed by proper design of the controller. Therefore, asymptotic tracking (or zero final tracking error) can be achieved without using high-gain feedback. The design is conceptually simple and is reliable in applications because of its high performance and strong robustness.

Following the developments in industrial robot technology, robotics has found its way into the medical field and is used in a range of surgical disciplines. The main purpose of the use of robots is to increase the precision, quality and safety of surgical procedures. Robotics is not yet used in dentistry even though all the necessary technologies have already been developed and could easily be adapted. Some of the technologies are already used in dentistry, such as image-based simulation of implant surgery followed by the use of surgical guides, and creating digital impressions of pre parathions using an intra-oral scanner, after which a milling device produces the restoration, but we have not yet seen any robot able to prepare teeth for crowns, inlays or bridges. Such a robot would fundamentally be a dental drilling device coupled with a navigation device to determine the correct position of the device in relation to the patient. However this technique is very essential to have the minimal pain and reduce bleeding but control of these robots/joints are very complicated. This paper proposes new fuzzy type sliding mode controller for multi-DOF joints to control of motor vibration. The fuzzy (PD+I)2 single-input single-output (SISO) fuzzy system is used to control of chattering in sliding mode controller. In most research al researchers wants to eliminate the chattering but, this research focuses on chattering control. Various operation situations such as the set point control and the trajectory control are simulated. The simulation results demonstrate that the chattering and the steady state errors, which usually occur in the classical sliding mode control, are limitation control and satisfactory trajectory tracking is achieved.

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 model-free 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.

Following the developments in industrial robot technology, robotics has found its way into the medical field and is used in a range of surgical disciplines. The main purpose of the use of robots is to increase the precision, quality and safety of surgical procedures. Robotics is not yet used in dentistry even though all the necessary technologies have already been developed and could easily be adapted. Some of the technologies are already used in dentistry, such as image-based simulation of implant surgery followed by the use of surgical guides, and creating digital impressions of pre parathions using an intra-oral scanner, after which a milling device produces the restoration, but we have not yet seen any robot able to prepare teeth for crowns, inlays or bridges. Such a robot would fundamentally be a dental drilling device coupled with a navigation device to determine the correct position of the device in relation to the patient. The robot would either be operated directly by a dentist or be preprogrammed to perform its functions based on imaging data (CT scan). Finally, an intra-oral scanner would be used to make digital impressions. This data would then be transferred to the lab to produce temporary crowns or bridges in a very short time using a milling machine and to manufacture the final restorations in much shorter time than with conventional procedures. Robotics could offer dentistry improved accuracy, predictability, safety, quality of care and speed of treatment. One might wonder why robots have not yet been introduced to dentistry, as the functions needed are relatively simple. An explanation could be that robotics in dentistry is an example of a disruptive technology, meaning that the current manufacturers of dental equipment might fear a negative effect on their current business and the alienation of dentists, as robots might be seen as a threat to dental professionals. The passive robotic arm will sense the patient’s movement, sending feedback in the form of translation and rotation data to the dental robot giving it the capability to adjust. This device will address the age-old problem in dentistry – precision and safety. This research focuses on the intelligent control of dental drilling procedures on a stationary object. This project addresses the ability of the system to detect movement of the object and accordingly adjust the drill before continuing the procedure.

In this research, intelligent sliding mode controls are presented as robust controls for robot manipulators. The objective of the study is to design controls for robot manipulators without the knowledge of the boundary of the uncertainties by using an intelligent sliding mode control (SMC) while elucidating the robustness of the fuzzy SMC. A sliding mode control provides for unlimited accuracy in presence of bounded disturbance, although the sliding mode controller also causes chattering. Chattering is undesirable for use with actual component, since it might causes damage to them with a subsequent loss of accuracy. Such chatter is caused by overestimation of the controller gain. An intelligent sliding mode is proposed as a solution to the problems created by chattering; to illustrate, a continuum robot manipulator is simulated with an intelligent sliding mode control. The performance of intelligent gain sliding mode controller is demonstrated through the simulation results. The results of the simulations show the effectiveness for chattering mitigation by means of avoiding overestimation, and the robustness of an intelligent sliding mode control.

Rule base is one of the important key factors in design of fuzzy controller. The performance of the system is affected if nonlinear parameters are present in the system. This paper presents a method to select rule base size depending on the nonlinear parameters present in the system in parallel with robust sliding mode controller. The significance of the proposed method is tested on highly nonlinear second order system (robot manipulator) using modified rule bases. A criterion is developed for selection of rule base depending on amount of type of system: nonlinearity, multi input- multi output, time variant. The graphical and analytical results are given to show the importance of the proposed method.

Design a nonlinear controller for second order nonlinear uncertain dynamical systems is one of the most important challenging works. This paper focuses on the design of a chattering free mathematical baseline sliding mode controller (BSMC) for highly nonlinear dynamic robot manipulator, in presence of uncertainties and external disturbance. In order to provide high performance nonlinear methodology, sliding mode controller and baseline methodology are selected. Conversely, pure sliding mode controller is used in many applications; it has an important drawback namely; chattering phenomenon which it can causes some problems such as saturation and heat the mechanical parts of robot manipulators or drivers so baseline sliding mode controller is used to eliminate this important challenge. In order to reduce the chattering this research is used the switching function in presence of baseline method instead of switching function method in pure sliding mode controller. The results demonstrate that baseline sliding mode controller with switching function is a model-based controllers which works well in certain and partly uncertain system and have a better performance compare to pure sliding mode controller. Chattering free baseline sliding mode controller is stable controller which eliminates the chattering phenomenon without to use the boundary layer saturation function.

This paper expands a sliding mode fuzzy controller which sliding surface gain is optimized by Gradient Descent Optimization Algorithm (GDOA). The main goal is to guarantee acceptable trajectories tracking between the second order nonlinear system (robot manipulator) actual and the desired trajectory. The fuzzy controller in proposed sliding mode fuzzy controller is based on Mamdani’s fuzzy inference system (FIS) and it has one input and one output. The input represents the function between sliding function, error and the rate of error. The outputs represent torque, respectively. The GDOA is the most prominent iterative method for solving sparse systems of nonlinear equations. The GDOA is a composite of simple, elegant ideas that almost anyone can understand. Pure sliding mode fuzzy controller has difficulty in handling unstructured model uncertainties. To solve this problem applied GDOA to sliding mode fuzzy controller for adjusting the sliding surface gain ( ). Since the sliding surface gain ( ) is adjusted by GDOA, it is nonlinear and continuous. GDOA sliding mode fuzzy controller is stable model-free controller which eliminates the chattering phenomenon without to use the boundary layer saturation function. Lyapunov stability is proved in GDOA sliding mode fuzzy controller based on switching (sign) function. This controller has acceptable performance in presence of uncertainty (e.g., overshoot=0.1%, rise time=0.6 second, steady state error = 1.1e-9 and RMS error=1.8e-9).

Robotic manipulators are multi-input multi-output (MIMO), nonlinear and most of dynamic parameters are uncertainty so design a high performance controller for these plants is very important. Today, strong mathematical tools used in new control methodologies to design adaptive nonlinear robust controller with acceptable performance. One of the best nonlinear robust controller which can be used in uncertainty nonlinear systems, are sliding mode controller but pure sliding mode controller has some disadvantages such as nonlinear dynamic uncertainties therefore to design model free sliding mode controller this research focuses on applied fuzzy logic controller in sliding mode controller. One of the most important challenging in pure sliding mode controller and sliding mode fuzzy controller is sliding surface slope coefficient therefore the second target in this research is design a supervisory controller to adjusting the sliding surface slope in sliding mode fuzzy controller.

In this paper, a new approach to the super-twisting sliding mode control of uncertain systems is proposed. The idea behind this control scheme is to utilize an adaptive sliding surface function, in which the slope of the surface is updated on-line using a simple single input-single output fuzzy logic inference system so that the sliding surface is rotated in such a direction that the tracking performance of the system under control is improved. Computer simulations are performed on a system with parameter uncertainties and external disturbances. The results are compared with a conventional super twisting sliding mode controller with a fixed sliding surface. The results have shown the improved performance of the proposed control approach in terms of a decrease in the reaching and settling times and robustness to parameter uncertainties and disturbances as compared to the conventional super-twisting sliding mode controller with a fixed sliding surface. Moreover, the proposed control scheme is very simple and easy to implement.

A new type controller, recurrent fuzzy neural networks-fuzzy-sliding mode controller (FRNN-FSMC), is developed for a class of large-scale systems with unknown bounds of high-order interconnections and disturbances. The main purpose is to eliminate the chattering phenomenon and to overcome the problem of the equivalent control computation. The FRNN-FSMC, which incorporates the recurrent fuzzy neural network (RFNN), fuzzy logic controller (FLC) and the SMC, can eliminate chattering using a fixed boundary layer around the switch surface. Within the boundary layer, where the FLC is applied, the chattering phenomenon, which is inherent in a SMC, is avoided by smoothing the switch signal. Moreover, to compute the equivalent controller, a feed-forward RFNN is used. The stability of the whole system is analyzed via the Lyapunov methodology. In this study, we propose an effective method to select some key controller parameters in an optimal manner by using the genetic algorithm (GA), so that a high performance of the overall system's response can be achieved. The effectiveness and efficiency of the proposed controller and optimization method were tested using highly interconnected nonlinear systems as examples.