The Proportional Integral Derivative (PID) Fuzzy Sliding Mode Controller (FSMC) is the most widely used control strategy in the Industry (control of robotic arm). The popularity of PID FSMC controllers can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID FSMC controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. Biologically inspired evolutionary strategies have gained importance over other strategies because of their consistent performance over wide range of process models and their flexibility. This paper analyses the modified PID FSMC controllers based on minimum rule base for flexible robot manipulator system and test the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator.

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Design a Novel SISO Off-line Tuning of Modified PID Fuzzy Sliding Mode Controller

This research is focused on proposed Proportional-Integral (PI) like fuzzy adaptive backstopping fuzzy algorithms based on Proportional- Derivative (PD) fuzzy rule base with the adaptation laws derived in the Lyapunov sense. Adaptive SISO PI like fuzzy adaptive backstopping fuzzy method has two main objectives; the first objective is design a SISO fuzzy system to compensate for the model uncertainties of the system, and the second objective is focused on the design PI like fuzzy controller based on PD method as an adaptive methodology. Classical backstopping control is robust to control model uncertainties and external disturbances and is a main controller in this research. The fuzzy controller is used in this method to system compensation. To increase the robust of this controller adaptive PI like fuzzy controller is introduced and applied to backstopping fuzzy controller. Classical backstopping control method has difficulty in handling unstructured model uncertainties. One can overcome this problem by combining a backstopping controller and artificial intelligence (e.g. fuzzy logic). To approximate a time-varying 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 backstopping fuzzy controller to online tune the coefficients in use will ensure a moderate computational load. The adaptive laws in this algorithm are designed based on the Lyapunov stability theorem. This method is applied to continuum robot manipulator to have the best performance.

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Design New Robust Self Tuning Fuzzy Backstopping Methodology

The increasing demand for multi-degree-of- freedom (DOF) actuators in a number of industries has motivated a flurry of research in the development of non-conventional actuators, spherical motor. This motor is capable of providing smooth and isotropic three- dimensional motion in a single joint. Not only can the spherical motor combine 3-DOF motion in a single joint, it has a large range of motion with no singularities in its workspace. The spherical motor, however, exhibits coupled, nonlinear and very complex dynamics that make the design and implementation of feedback controllers very challenging. The orientation- varying torque generated by the spherical motor also contributes to the challenges in controller design. This paper contributes to the on-going research effort by exploring alternate methods for nonlinear and robust controlling the motor. The robust sliding mode controller proposed in this paper is used to further demonstrate the appealing features exhibited by the spherical motor. In opposition, sliding mode controller is used in many applications especially to control of highly uncertain systems; it has two significant drawbacks namely; chattering phenomenon and nonlinear equivalent dynamic formulation in uncertain dynamic parameter. The nonlinear equivalent dynamic formulation problem and chattering phenomenon in uncertain system (e.g., spherical motor) can be solved by using artificial intelligence theorem and applied a modified linear controller to switching part of sliding mode controller. Using Lyapunov-type stability arguments, a robust modified linear fuzzy sliding mode controller is designed to achieve this objective. The controller developed in this paper is designed in a robust stabilizing torque is designed for the nominal spherical motor dynamics derived using the constrained Lagrangian formulation. The eventual stability of the controller depends on the torque generating capabilities of the spherical motor.

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Design Modified Sliding Mode Controller with Parallel Fuzzy Inference System Compensator to Control of Spherical Motor

The minimum rule base Proportional Integral Derivative (PID) Fuzzy Sliding Mode Controller (SMC) with application to spherical motor is presented in this research. The popularity of PID Fuzzy Sliding Mode Controller can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID Fuzzy Sliding Mode Controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing especially in nonlinear and uncertain systems. Proportional Integral Derivative methodology has three inputs and if any input is described with seven linguistic values, and any rule has three conditions, we will need 343 rules. It is too much work to write 343 rules and have lots of problem to design embedded control system e.g., Field Programmable Gate Array (FPGA). In this research the PID-like fuzzy controller can be constructed as a parallel structure of a PD-like fuzzy controller and a conventional PI controller to have the minimum rule base and good trajectory follow disturbance to control of spherical motor. However Sliding Mode Controller is work based on cancelling decoupling and nonlinear terms of dynamic parameters for each direction of three degree of freedom spherical motor, this controller is work based on motor dynamic model and this technique is highly sensitive to the knowledge of all parameters of nonlinear spherical motor‟s dynamic equation which caused to challenge in uncertain system. This research is used to reduce or eliminate the Sliding Mode Controller problem based on minimum rule base fuzzy logic theory to control of three degrees of freedom spherical motor system and testing of the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator. Index Term—PID like fuzzy control, Sliding Mode Controller, PD like fuzzy control, conventional PI control, three degrees of freedom spherical motor.

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Design Intelligent PID like Fuzzy Sliding Mode Controller for Spherical Motor

Fuzzy logic controller (FLC) is an important nonlinear controller in an uncertain dynamic system"s parameters. This controller is used to control of nonlinear dynamic systems particularly for spherical motor, because it has a suitable control performance and it is a stable. Conversely pure fuzzy logic controller is a high-quality intelligent nonlinear controller; it has two important problems; reliability and robustness in uncertain dynamic parameter. To increase the reliability and robustness, this research is focused on applied feedback linearization method in pure fuzzy logic controller. In this research the nonlinear equivalent dynamic (equivalent part) formulation problem in uncertain condition is also solved by combine pure fuzzy logic control and feedback linearization method. In this method feedback linearization theorem is applied to fuzzy logic controller to increase the stability, reliability and robustness, which it is based on nonlinear dynamic formulation. To achieve this goal, the dynamic-based formulation feedback linearization method is design. This method is robust and model-based nonlinear control therefore can reduce the nonlinearity term of system and reduce the effect of coupling. In this research MAMDANI fuzzy inference system is used as a main controller. It has minimum rule base to practical implementation. This technique was employed to obtain the desired control behavior with a number of information about dynamic model of system and a feedback linearization control was applied to reinforce system performance.

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Design Serial Intelligent Modified Feedback Linearization like Controller with Application to Spherical Motor

The increasing demand for multi-degree-of- freedom (DOF) continuum robot in presence of highly nonlinear dynamic parameters in a number of industries has motivated a flurry of research in the development of soft computing nonlinear methodology This robot is capable of providing smooth and isotropic three- dimensional motion in each joint Compared to conventional robotic manipulators that offer the same motion capabilities, the innovative spherical motor possesses several advantages Not only can the spherical motor combine 3-DOF motion in a single joint, it has a large range of motion with no singularities in its workspace This research contributes to the on-going research effort by exploring alternate methods for controlling the continuum robot manipulator This research addresses two basic issues related to the control of a continuum robots; (1) a more accurate representation of the dynamic model of an existing prototype, and (2) the design of a robust feedback controller The robust backstepping controller proposed in this research is used to further demonstrate the appealing features exhibited by the continuum robot Robust feedback controller is used to position control of continuum robot in presence of uncertainties Using Lyapunov type stability arguments, a robust backstepping controller is designed to achieve this objective The controller developed in this research is designed in two steps Firstly, a robust stabilizing torque is designed for the nominal continuum robot dynamics derived using the constrained Lagrangian formulation Next, the fuzzy logic methodology applied to it to solution uncertainty problem The fuzzy model free problem is formulated to minimize the nonlinear formulation of continuum robot The eventual stability of the controller depends on the torque generating capabilities of the continuum robots

Design Novel Soft Computing Backstepping Controller with Application to Nonlinear Dynamic Uncertain System

In this paper, a linear proportional derivative (PD) controller is designed for highly nonlinear and uncertain system by using robust factorization approach. To evaluate a linear PD methodology two type methodologies are introduced; sliding mode controller and fuzzy logic methodology. This research aims to design a new methodology to fix the position in continuum robot manipulator. PD method is a linear methodology which can be used for highly nonlinear system's (e.g., continuum robot manipulator). To estimate this method, new parallel fuzzy sliding mode controller (PD.FSMC) is used. This estimator can estimate most of nonlinearity terms of dynamic parameters to achieve the best performance. The asymptotic stability of fuzzy PD control with first- order sliding mode compensation in the parallel structure is proven. For the parallel structure, the finite time convergence with a super-twisting second-order sliding-mode is guaranteed.

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Design Parallel Linear PD Compensation by Fuzzy Sliding Compensator for Continuum Robot

Among scaffolds used in tissue engineering, natural biomaterials such as plant-based materials show a crucial role in cellular function due to their biocompatibility and chemical indicators. Because of environmentally friendly behavior and safety, green methods are so important in designing scaffolds. A key bioactive flavonoid of the Epimedium plant, Icariin (ICRN), has a broad range of applications in improving scaffolds as a constant and non-immunogenic material, and in stimulating the cell growth, differentiation of chondrocytes as well as differentiation of embryonic stem cells towards cardiomyocytes. Moreover, fusion of ICRN into the hydrogel scaffolds or chemical crosslinking can enhance the secretion of the collagen matrix and proteoglycan in bone and cartilage tissue engineering. To scrutinize, in various types of cancer cells, ICRN plays a decisive role through increasing cytochrome c secretion, Bax/Bcl2 ratio, poly (ADP-ribose) polymerase as well as caspase stimulations. Surprisingly, ICRN can induce apoptosis, reduce viability and inhibit proliferation of cancer cells, and repress tumorigenesis as well as metastasis. Moreover, cancer cells no longer grow by halting the cell cycle at two checkpoints, G0/G1 and G2/M, through the inhibition of NF-κB by ICRN. Besides, improving nephrotoxicity occurring due to cisplatin and inhibiting multidrug resistance are the other applications of this biomaterial.

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Mohammadreza Hashemzadeh

Papers

Design Novel Soft Computing Backstepping Controller with Application to Nonlinear Dynamic Uncertain System

Design Parallel Linear PD Compensation by Fuzzy Sliding Compensator for Continuum Robot

Icariin: A Promising Natural Product in Biomedicine and Tissue Engineering