A New Estimate Sliding Mode Fuzzy Controller for Robot Manipulator (original) (raw)
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International Journal of Engineering, 5 (5):360-379, 2011
In this research, an artificial chattering free adaptive fuzzy sliding mode control design and application to uncertain robotic manipulator has proposed in order to design high performance nonlinear controller in the presence of uncertainties. Regarding to the positive points in sliding mode controller, fuzzy logic controller and adaptive method, the output has improved. Each method by adding to the previous controller has covered negative points. The main target in this research is design of model free estimator on-line sliding mode fuzzy algorithm for robot manipulator to reach an acceptable performance. Robot manipulators are highly nonlinear, and a number of parameters are uncertain, therefore design model free controller using both analytical and empirical paradigms are the main goal. Although classical sliding mode methodology has acceptable performance with known dynamic parameters such as stability and robustness but there are two important disadvantages as below: chattering phenomenon and mathematical nonlinear dynamic equivalent controller part. To solve the chattering fuzzy logic inference applied instead of dead zone function. To solve the equivalent problems in classical sliding mode controller this paper focuses on applied fuzzy logic method in classical controller. This algorithm works very well in certain environment but in uncertain or various dynamic parameters, it has slight chattering phenomenon. The system performance in sliding mode controller and fuzzy sliding mode controller are sensitive to the sliding function. Therefore, compute the optimum value of sliding function for a system is the next challenge. This problem has solved by adjusting sliding function of the adaptive method continuously in real-time. In this way, the overall system performance has improved with respect to the classical sliding mode controller. This controller solved chattering phenomenon as well as mathematical nonlinear equivalent part by applied fuzzy supervisory method in sliding mode fuzzy controller and tuning the sliding function.
Fuzzy sliding mode control for a robot manipulator
Artificial Life and Robotics, 2008
Fuzzy sliding mode control for a robot manipulator multi-input-multi-output systems. The most signifi cant property of an SMC is its robustness. SMC nowadays enjoys a wide variety of application areas, such as in robotics, 1 in process control, 2 in dc motor control 3 and so on.
International Journal of Intelligent Systems and Applications, vol.6, no.4, pp.63-75, 2014. DOI: 10.5815/ijisa, 2014
Sliding mode controller (SMC) is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators, because this controller is a robust and stable. Conversely, pure sliding mode controller is used in many applications; it has two important 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 can be solved by using artificial intelligence theorem. However fuzzy logic controller is used to control complicated nonlinear dynamic systems, but it cannot guarantee stability and robustness. In this research parallel fuzzy logic theory is used to compensate the system dynamic uncertainty.
International Journal of Robotics and Automation (IJRA), 2013
Sliding mode controller (SMC) is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators, because this controller is a robust and stable. Conversely, pure sliding mode controller is used in many applications; it has two important 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 can be solved by using artificial intelligence theorem. However fuzzy logic controller is used to control complicated nonlinear dynamic systems, but it cannot guarantee stability and robustness. In this research parallel fuzzy logic theory is used to compensate the system dynamic uncertainty.
International Journal of Artificial Intelligence and Expert System, 2 (5):208-228, 2011
This research focuses on design Single Input Single Output (SISO) adaptive sliding mode fuzzy PD fuzzy sliding mode algorithm with estimates the equivalent part derived in the Lyapunov sense. The stability of the closed-loop system is proved mathematically based on the Lyapunov method. Proposed method introduces a SISO fuzzy system to compensate for the model uncertainties of the system and eliminate the chattering by linear boundary layer method. This algorithm is used a SISO fuzzy system to alleviate chattering and to estimate the control gain in the control law and presented a scheme to online tune of sliding function. To attenuate the chattering phenomenon this method developed a linear boundary layer and the parameter of the sliding function is online tuned by adaptation laws. This algorithm will be analyzed and evaluated on robotic manipulators and design adaption laws of adaptive algorithms after that writing Lyapunov function candidates and prove the asymptotic convergence of the closed-loop system using Lyapunov stability theorem mathematically. Compare and evaluate proposed method and sliding mode algorithms under disturbance. In regards to the former, we will be looking at the availability of online tuning methodology and the number of fuzzy if-then rules inherent to the fuzzy system being used and the corresponding computational load. Our analysis of the results will be limited to tracking accuracy and chattering.
Design sliding mode controller for robot manipulator with artificial tunable gain
Canadian journal of Pure and Applied Science,5(2): 1573-1579, 2011
One of the most active research areas in the field of robotics is robot manipulators control, because these systems are multi-input multi-output (MIMO), nonlinear, and uncertainty. At present, robot manipulators are used in unknown and unstructured situation and caused to provide complicated systems, consequently strong mathematical tools are used in new control methodologies to design nonlinear robust controller with satisfactory performance (e.g., minimum error, good trajectory, disturbance rejection). Robotic systems controlling is vital due to the wide range of application. Obviously stability and robustness are the most minimum requirements in control systems; even though the proof of stability and robustness is more important especially in the case of nonlinear systems. The strategies of robotic manipulators control are classified into two main groups: classical and non-classical methods, where the conventional control theory uses the classical method and the artificial intelligence theory (e.g., fuzzy logic, neural network, and neuro fuzzy) uses the non-classical methods. However both of classical and non-classical theories have applied successfully in many applications, but they also have some limitations. One of the best nonlinear robust controllers which can be used in uncertainty nonlinear systems is sliding mode controller (SMC). Sliding mode controller has two most important challenges: chattering phenomenon and nonlinear dynamic equivalent part. This paper is focused on the applied nonclassical method (e.g., Fuzzy Logic) in robust classical method (e.g., Sliding Mode Controller) in the presence of uncertainties and external disturbance to reduce the limitations. Applying the Mamdani’s error based fuzzy logic controller with 7 rules is the main goal that causes the elimination chattering phenomenon with regard to the variety of uncertainty and external disturbance; as a result this paper focuses on the sliding mode controller with artificial tuneable gain (SMCAT) to adjusting the sliding surface slope coefficient depends on applying fuzzy method.
—The main contribution of this paper is the design of a robust model reference fuzzy sliding mode observation technique to control multi-input, multi-output (MIMO) nonlinear uncertain dynamical robot manipulators. A fuzzy sliding mode controller was used in this study to control the robot manipulator in the presence of uncertainty and disturbance. To address the challenges of robustness, chattering phenomenon, and error convergence under uncertain conditions, the proposed sliding mode observer was applied to the fuzzy sliding mode controller. This theory was applied to a six-degrees-of-freedom (DOF) PUMA robot manipulator to verify the power of the proposed method.
Voltage-Base Control of Robot Manipulator Using Adaptive Fuzzy Sliding Mode Control
International Journal of Fuzzy Systems, 2016
In this paper, a controller is proposed that is able to overcome existing structured and unstructured uncertainties in the dynamic equations of robot manipulator and its actuators. In this method, at first, through sliding mode control and by using defined dynamic equations of robot manipulator, robust nonlinear controller is designed that is capable of overcoming the existing uncertainties. In the following, due to incidence of the control input chattering, a first-order TSK fuzzy approximator is designed in such a way that is able to overcome undesirable chattering phenomenon. The presented fuzzy sliding mode control has a small number of calculations. However, the design structure of proposed control is in such a way that leads to increase the number of needed sensors for the practical implementation of this controller. Next, to overcome these problems, an adaptive fuzzy approximator is used to approximate the bounds of the existing uncertainties. The proposed adaptive fuzzy sliding mode control has low volume of calculations, and due to the use of single-input, single-output fuzzy rules in the adaptive fuzzy approximator, the problem of the increasing number of sensors is resolved. Mathematical proof investigates that a closedloop system with the proposed control and in the presence of existing uncertainties in the dynamic equations of robot manipulator and its actuators has global asymptotic stability. Finally, to demonstrate the performance of the proposed controller, a two-link elbow robot manipulator is used as a case study. The simulation results show the favorable efficiency of the proposed controller.
Comparison of conventional & fuzzy based sliding mode PID controller for robot manipulator
2013 International Conference on Individual and Collective Behaviors in Robotics (ICBR), 2013
High accuracy trajectory tracking is challenging topic in robotic manipulator control. This is due to nonlinearities and input coupling present in robotic arm. In this paper, a chattering free sliding mode control (SMC) for a robot manipulator including PID part with a fuzzy tunable gain is designed. The main idea is that the robustness property of SMC and good response characteristics of PID are combined with fuzzy tuning gain approach to achieve more acceptable performance. A PID sliding surface is considered such that the robot dynamic equation can be rewritten in terms of sliding surface. Then in order to decrease the reaching time to the sliding surface and deleting the oscillation of the response, a fuzzy tuning system is used for adjusting both controller gains including sliding controller gain parameter and PID coefficient. Controller is applied to two link robot manipulator including model uncertainty and external disturbance as a case study. Simulation study has been done in MATLAB/Simulink environment shows the improvements of the results compare to conventional SMC.
Comparison of Conventional & Fuzzy based Sliding Mode PID Controller for Robot Manipulator
IEEE
High accuracy trajectory tracking is challenging topic in robotic manipulator control. This is due to nonlinearities and input coupling present in robotic arm. In this paper, a chattering free sliding mode control (SMC) for a robot manipulator including PID part with a fuzzy tunable gain is designed. The main idea is that the robustness property of SMC and good response characteristics of PID are combined with fuzzy tuning gain approach to achieve more acceptable performance. A PID sliding surface is considered such that the robot dynamic equation can be rewritten in terms of sliding surface. Then in order to decrease the reaching time to the sliding surface and deleting the oscillation of the response, a fuzzy tuning system is used for adjusting both controller gains including sliding controller gain parameter and PID coefficient. Controller is applied to two link robot manipulator including model uncertainty and external disturbance as a case study. Simulation study has been done in MATLAB/Simulink environment shows the improvements of the results compare to conventional SMC.