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This paper addresses the issues of universal fuzzy controllers and shows that the Mamdani-type fuzzy controllers are universal fuzzy controllers. Furthermore, a constructive procedure is also presented to obtain the universal fuzz...
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This paper addresses the issues of universal fuzzy controllers and shows that the Mamdani-type fuzzy controllers are universal fuzzy controllers. Furthermore, a constructive procedure is also presented to obtain the universal fuzzy controller if the reference fuzzy model is given.
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This paper is concerned with the use of fuzzy inputs in fuzzy logic controllers. A precise representation of fuzzy logic controllers by means of mappings is used to introduce different ways for dealing with fuzzy inputs. Two types...
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This paper is concerned with the use of fuzzy inputs in fuzzy logic controllers. A precise representation of fuzzy logic controllers by means of mappings is used to introduce different ways for dealing with fuzzy inputs. Two types of fuzzy inputs are presented and their potential use in fuzzy control is discussed. The proposed concepts are applied to control a first order process with a PI controller. This simple process is chosen to clearly illustrate the behavior of the closed-loop system using fuzzy inputs for fuzzy reference and fuzzy measurement. Finally, a nonlinear process is used to illustrate the effects of fuzzy inputs on a more complex system. Although it is sometimes speculated that fuzzy inputs may improve the behavior of fuzzy controllers, experiments developed in this paper show this point is not straightforward and that the relevance of fuzzy inputs should be questioned in closed-loop fuzzy control.
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The objective of this paper is to achieve tracking control of a class of unknown feedback linearizable nonlinear dynamical systems using a discrete-time fuzzy logic controller (FLC). Discrete-time FLC design is significant because...
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The objective of this paper is to achieve tracking control of a class of unknown feedback linearizable nonlinear dynamical systems using a discrete-time fuzzy logic controller (FLC). Discrete-time FLC design is significant because almost all FLCsare implemented on digital computers. A repeatable design algorithm and a stability proof for an adaptive fuzzy logic controller is presented that uses basis functions based on the fuzzy system, unlike most standard adaptive control approaches whichgenerate basis vectors by computing a "regression matrix". A new approach to adapt the fuzzy system parameters is attempted. With mild assumptions on the class of discrete-time nonlinear systems, using this adaptive fuzzy logic controller the uniformultimate boundedness of the closed-loop signals is shown under a persistency of excitation(PE) condition. New passivity properties of fuzzy logic systems are described. The result is a model-free universal fuzzy controller that works for any system in the given class systems.
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Since the emergence of fuzzy logic controllers (FLCs), control system engineers are in pursuit of more and more sophisticated versions of these controllers to achieve better performance, particularly in situations where providing ...
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Since the emergence of fuzzy logic controllers (FLCs), control system engineers are in pursuit of more and more sophisticated versions of these controllers to achieve better performance, particularly in situations where providing a control action to even a minimal degree of satisfaction is a problem. The present paper is an attempt to contribute in this field. This work proposes a fuzzy PID controller comprising fuzzy P, fuzzy I and fuzzy D controllers in parallel. While fuzzy P and fuzzy I controllers are implemented in incremental form, the fuzzy D controller is realized in position form. Simulation studies reveal that the new scheme has a significantly improved performance compared to the Ziegler-Nichols tuned PID controller and static fuzzy PID controllers currently in use.
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In this paper, a stable fuzzy neural tracking control of a class of unknown nonlinear systems based on the fuzzy hierarchy approach is proposed. The adaptive fuzzy neural controller is constructed from the fuzzy neural network wit...
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In this paper, a stable fuzzy neural tracking control of a class of unknown nonlinear systems based on the fuzzy hierarchy approach is proposed. The adaptive fuzzy neural controller is constructed from the fuzzy neural network with a set of fuzzy rules. The corresponding network parameters are adjusted online according to the control law and update law for the purpose of controlling the plant to track a given trajectory. A stability analysis of the unknown nonlinear system is discussed based on the Lyapunov principle. In order to improve the convergence of the nonlinear dynamical systems, a fuzzy hierarchy error approach (FHEA) algorithm is incorporated into the adaptive update and control scheme. The simulation results for an unstable nonlinear plant demonstrate the control effectiveness of the proposed adaptive fuzzy neural controller and are consistent with the theoretical analysis.
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Many fuzzy control schemes used in industrial practice today are based on some simplified fuzzy reasoning methods, which are simple but at the expense of losing robustness, missing fuzzy characteristics, and having inconsistent in...
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Many fuzzy control schemes used in industrial practice today are based on some simplified fuzzy reasoning methods, which are simple but at the expense of losing robustness, missing fuzzy characteristics, and having inconsistent inference. The concept of optimal fuzzy reasoning is introduced in this paper to overcome these shortcomings. The main advantage is that an integration of the optimal fuzzy reasoning with a PID control structure will generate a new type of fuzzy-PID control schemes with inherent optimal-tuning features for both local optimal performance and global tracking robustness. This new fuzzy-PID controller is then analyzed quantitatively and compared with other existing fuzzy-PID control methods. Both analytical and numerical studies clearly show the improved robustness of the new fuzzy-PID controller.
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Research into robot motion control offers research opportunities that will change scientists and engineers for year to come. Autonomous robots are increasingly evident in many aspects of industry and everyday life and a robust rob...
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Research into robot motion control offers research opportunities that will change scientists and engineers for year to come. Autonomous robots are increasingly evident in many aspects of industry and everyday life and a robust robot motion control can be used for homeland security and many consumer applications. This study discussed the adaptive fuzzy knowledge based controller for robot motion control in indoor and outdoor environment. Approach: The proposed method consisted of two components: the process monitor that detects changes in the process characteristics and the adaptation mechanism that used information passed to it by the process monitor to update the controller parameters. Results: Experimental evaluation had been done in both indoor and outdoor environment where the robot communicates with the base station through its Wireless fidelity antenna and the performance monitor used a set of five performance criteria to access the fuzzy knowledge based controller. Conclusion: The proposed method had been found to be robust.
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Different fuzzy reasoning methods were gave by choosing different fuzzy counters. This article generally introduced the basic structure of fuzzy controller, and compared and analysised the reasoning effect of fuzzy reasoning metho...
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Different fuzzy reasoning methods were gave by choosing different fuzzy counters. This article generally introduced the basic structure of fuzzy controller, and compared and analysised the reasoning effect of fuzzy reasoning methods and the effect of computer simulating control basicly on different fuzzy counters.
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In this paper, we propose a new reinforcement learning algorithm to generate a fuzzy controller for robot motions. This algorithm generates a range of continuous real-valued actions, and the reinforcement signal is self-scaled. Th...
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In this paper, we propose a new reinforcement learning algorithm to generate a fuzzy controller for robot motions. This algorithm generates a range of continuous real-valued actions, and the reinforcement signal is self-scaled. This prevents the weights from overshooting when the system receives very large reinforcement values. Therefore, this algorithm can obtain a solution in fewer iterations. The proposed method is applied to the control of the brachiation robot, which moves dynamically from branch to branch like a gibbon swinging its body in a pendulum-like fashion. Through computer simulations, we show the fast convergence and the robustness against disturbances.
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Design and Tuning a fuzzy logic controller (FLCs) arc usually done in two stages. In the first stage, the structure of a FIX is determined based on physical characteristics of the system. In the second stage, the parameters of the...
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Design and Tuning a fuzzy logic controller (FLCs) arc usually done in two stages. In the first stage, the structure of a FIX is determined based on physical characteristics of the system. In the second stage, the parameters of the FLC arc selected to optimize the performance of the system. The task of tuning FLCs can be performed by a number of methods such as adjusting control gains, changing membership functions, modifying control rules and varying control surfaces. A method for the design and tuning of FLCs through modifying their control surfaces is presented in this paper. The method can be summarized as follows. A fuzzy control surface is modeled with Bczier functions. Shapes of the control surface are then adjusted through varying Bezier parameters. A Genetic Algorithm (GA) is used to search for the optimal set of parameters based on the control performance criteria. Simulation results on various control systems show the effectiveness of the proposed method.
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