A new fuzzy sliding mode control scheme (original) (raw)
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Design a robust self-tuning fuzzy sliding mode control for second order systems
2012
Robotic systems as second-order systems are essential part in the industry world. Controls of These systems are critical outstanding to a wide range of their application. Robustness and stability are two important demands in any control system. Control of Robot manipulators is challenged because they are multi-input multi-output (MIMO), nonlinear, time variant and have uncertainty. Furthermore, the robot manipulators used in unknown and unstructured situation, so they provide sophisticated systems. Therefore, it is a challenge to design an adaptive nonlinear robust controller, with has had suitable performance (e.g., minimum error, good trajectory, disturbance rejection). Non-classical Control methods used the artificial intelligence theory (e.g., fuzzy logic, neural network, and neuro fuzzy) to reduce the limitation of these types of systems. In This paper by using fuzzy rules and a non-classic method, an adaptive fuzzy sliding mode controller will be presented.
International Journal of Advanced Science and Technology, 46:39-70, 2012
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).
Robust control by fuzzy sliding mode
Automatica, 1994
Abstrae1--Most fuzzy controllers (FCs) for nonlinear second order systems are designed with a two-dimensional phase plane in mind. We show that the performance and the robustness of this kind of FC stems from their property of driving the system into the sliding mode (SM), in which the controlled system is invariant to parameter fluctuations and disturbances. Additionally, the continuous distribution of the control values in the phase plane causes a behavior similar to that of a sliding mode controller (SMC) with a boundary layer (BL) near the switching line. This gives assured tracking quality even in the presence of high model uncertainties. Tracing the FC back to the principle of an SMC one obtains evidence about the stability of the closed-loop system. The choice of the scaling factors for the crisp inputs and outputs can be guided by the comparison of the FC with the SMC and with the modified SMC, respectively. At the end of the paper, an FC for a higher-order system is proposed. Simulation results show the practicability of the method.
Fuzzy Sliding Mode Controllers and Sliding Mode Fuzzy Controllers: A survey
The airn of this study is to provide basic concepts, Inathernatical structure, clesign aspects and a review of the literature available on the topic that combines tirzzy logic control and sliding mocle control sub-iects. Sliding mode coutrol provides insensitivity to paranterer varizitions auci conrplete le.iection of clisttrlbances. Sliding tlode has solne drawbacks such as chattering and trade-olT between perfbrtlance arrcl robustness. Fuzzy logic based contt'ol provides a way of converting hul.uan expert knowledge into automatic control strategy. Fuzzy logic control, on the other hapd. does not possess a general stability analysis method. Combining these two ty,pes of control subjects to exploit the superior sides of therr is an active area in control theory. In this papef. an overviely of the researclt ft'om the literature is presented. Copl'right e )A03 IFAC'
Adaptive fuzzy sliding mode control scheme for uncertain systems
Communications in Nonlinear Science and Numerical Simulation, 2009
Most physical systems inherently contain nonlinearities which are commonly unknown to the system designer. Therefore, in modeling and analysis of such dynamic systems, one needs to handle unknown nonlinearities and/or uncertain parameters. This paper proposes a new adaptive tracking fuzzy sliding mode controller for a class of nonlinear systems in the presence of uncertainties and external disturbances. The main contribution of the proposed method is that the structure of the controlled system is partially unknown and does not require the bounds of uncertainty and disturbance of the system to be known; meanwhile, the chattering phenomenon that frequently appears in the conventional variable structure systems is also eliminated without deteriorating the system robustness. The performance of the proposed approach is evaluated for two well-known benchmark problems. The simulation results illustrate the effectiveness of our proposed controller.
International Journal of Engineering, 5 (5): 380-398., 2011
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.
An Adaptive Fuzzy Sliding Mode Control Scheme for Robotic Systems
Intelligent Control and Automation, 2011
In this article, an adaptive fuzzy sliding mode control (AFSMC) scheme is derived for robotic systems. In the AFSMC design, the sliding mode control (SMC) concept is combined with fuzzy control strategy to obtain a model-free fuzzy sliding mode control. The equivalent controller has been substituted for by a fuzzy system and the uncertainties are estimated on-line. The approach of the AFSMC has the learning ability to generate the fuzzy control actions and adaptively compensates for the uncertainties. Despite the high nonlinearity and coupling effects, the control input of the proposed control algorithm has been decoupled leading to a simplified control mechanism for robotic systems. Simulations have been carried out on a two link planar robot. Results show the effectiveness of the proposed control system.
Adaptive fuzzy supervision of the gain of the higher order sliding mode control
Sliding mode control (called SMC) is a control scheme well known with its robustness against modelling uncertainties and external disturbances. This is achieved by using a high frequency switching part into the control law. However, this leads to the appearing of an undesirable chattering phenomenon around the sliding surface. This generates a serious drawback when implementing the controller in real time. So, the higher order sliding mode controller (HOSMC) can be used to overcome these drawbacks. In this paper, we present a novel technique of HOSMC for uncertain nonlinear systems and to show its high performances, robustness and stability a comparative study with three well know techniques of HOSMC is investigated. Besides, to improve the new strategy of control we propose a fuzzy supervision of the controller gain. This makes the controller free chattering and provides an exponential stability on the sliding surface and guarantees the robustness against uncertainties and external matched disturbances. The proposed approach is applied to a model of a car to ensure a robust tracking of a prescribed reference trajectory. Numerical simulations are developed to show the effectiveness of the proposed approaches.
Adaptive fuzzy sliding mode control of uncertain nonlinear systems
Saude E Sociedade, 2010
This paper presents a detailed discussion about the convergence properties of a variable structure controller for uncertain single-input-single-output nonlinear systems (SISO). The adopted approach is based on the sliding mode control strategy and enhanced by an adaptive fuzzy algorithm to cope with modeling inaccuracies and external disturbances that can arise. The boundedness of all closed-loop signals and the convergence properties of the tracking error are analytically proven using Lyapunov's direct method and Barbalat's lemma. This result corrects flawed conclusions previously reached in the literature. An application of this adaptive fuzzy sliding mode controller to a second-order nonlinear system is also presented. The obtained numerical results demonstrate the improved control system performance.
2003
A novel adaptive fuzzy sliding mode control design is developed for trajectory tracking of a class of nonlinear systems in this paper. This control design uses the modelling error to adaptively estimate the deterministic uncertainties as well as the control gain based on the fuzzy systems approach. By this design, the bounds of the uncertainties are not required to be known in advance, and the robust stability of closed loop systems is analysed in the Lyapunov sense. Simulation results are given to demonstrate the improved performance.