Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems

This paper presents an adaptive PI Hermite neural control (APIHNC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The proposed APIHNC system is composed of a neural controller and a robust compensator. The neural controller uses a three-layer Hermite neural network (HNN) to online mimic an ideal controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Moreover, a proportional–integral learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed APIHNC system is applied to an inverted double pendulums and a two-link robotic manipulator. Simulation results verify that the proposed APIHNC system can achieve high-precision tracking performance. It should be emphasized that the proposed APIHNC system is clearly and easily used for real-time applications.     

Supervisory adaptive dynamic RBF-based neural-fuzzy control system design for unknown nonlinear systems

Many published papers show that a TSK-type fuzzy system provides more powerful representation than a Mamdani-type fuzzy system. Radial basis function (RBF) network has a similar feature to the fuzzy system. As this result, this article proposes a dynamic TSK-type RBF-based neural-fuzzy (DTRN) system, in which the learning algorithm not only online generates and prunes the fuzzy rules but also online adjusts the parameters. Then, a supervisory adaptive dynamic RBF-based neural-fuzzy control (SADRNC) system which is composed of a DTRN controller and a supervisory compensator is proposed. The DTRN controller is designed to online estimate an ideal controller based on the gradient descent method, and the supervisory compensator is designed to eliminate the effect of the approximation error introduced by the DTRN controller upon the system stability in the Lyapunov sense. Finally, the proposed SADRNC system is applied to control a chaotic system and an inverted pendulum to illustrate its effectiveness. The stability of the proposed SADRNC scheme is proved analytically and its effectiveness has been shown through some simulations.     

Design of intelligent power controller for DC–DC converters using CMAC neural network

DC–DC converters are the devices which can convert a certain electrical voltage to another level of electrical voltage. They are very popularly used because of the high efficiency and small size. This paper proposes an intelligent power controller for the DC–DC converters via cerebella model articulation controller (CMAC) neural network approach. The proposed intelligent power controller is composed of a CMAC neural controller and a robust controller. The CMAC neural controller uses a CMAC neural network to online mimic an ideal controller, and the robust controller is designed to achieve L ₂ tracking performance with desired attenuation level. Finally, a comparison among a PI control, adaptive neural control and the proposed intelligent power control is made. The experimental results are provided to demonstrate the proposed intelligent power controller can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response and simple computation.     

Design of an adaptive self-organizing fuzzy neural network controller for uncertain nonlinear chaotic systems

Though the control performances of the fuzzy neural network controller are acceptable in many previous published papers, the applications are only parameter learning in which the parameters of fuzzy rules are adjusted but the number of fuzzy rules should be determined by some trials. In this paper, a Takagi–Sugeno-Kang (TSK)-type self-organizing fuzzy neural network (TSK-SOFNN) is studied. The learning algorithm of the proposed TSK-SOFNN not only automatically generates and prunes the fuzzy rules of TSK-SOFNN but also adjusts the parameters of existing fuzzy rules in TSK-SOFNN. Then, an adaptive self-organizing fuzzy neural network controller (ASOFNNC) system composed of a neural controller and a smooth compensator is proposed. The neural controller using the TSK-SOFNN is designed to approximate an ideal controller, and the smooth compensator is designed to dispel the approximation error between the ideal controller and the neural controller. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived based on the Lyapunov stability theory, thus not only the system stability can be achieved but also the convergence of tracking error can be speeded up. Finally, the proposed ASOFNNC system is applied to a chaotic system. The simulation results verify the system stabilization, favorable tracking performance, and no chattering phenomena can be achieved using the proposed ASOFNNC system.     

Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance

The advantage of using cerebellar model articulation control (CMAC) network has been well documented in many applications. However, the structure of a CMAC network which will influence the learning performance is difficult to select. This paper proposes a dynamic structure CMAC network (DSCN) which the network structure can grow or prune systematically and their parameters can be adjusted automatically. Then, an adaptive dynamic CMAC neural control (ADCNC) system which is composed of a computation controller and a robust compensator is proposed via second-order sliding-mode approach. The computation controller containing a DSCN identifier is the principal controller and the robust compensator is designed to achieve L₂ tracking performance with a desired attenuation level. Moreover, a proportional–integral (PI)-type adaptation learning algorithm is derived to speed up the convergence of the tracking error in the sense of Lyapunov function and Barbalat’s lemma, thus the system stability can be guaranteed. Finally, the proposed ADCNC system is applied to control a chaotic system. The simulation results are demonstrated that the proposed ADCNC scheme can achieve a favorable control performance even under the variations of system parameters and initial point.     

直齿圆柱齿轮的参数化设计二次开发

以直齿圆柱齿轮的三维参数化设计为例,研究基于UG的参数化零件库的开发方法,介绍了UG／Open Grip程序开发的过程。在分析了标准渐开线直齿轮设计模型的基础上,建立了直齿圆柱齿轮的三维参数化模型,并编写程序实现,简化了齿轮建模过程,提高效率。     

Robust cerebellar model articulation controller design for unknown nonlinear systems

In this study, a robust cerebellar model articulation controller (RCMAC) is designed for unknown nonlinear systems. The RCMAC is comprised of a cerebellar model articulation controller (CMAC) and a robust controller. The CMAC is utilized to approximate an ideal controller, and the weights of the CMAC are on-line tuned by the derived adaptive law based on the Lyapunov sense. The robust controller is designed to guarantee a specified H/sup /spl infin// robust tracking performance. In the RCMAC design, the sliding-mode control method is utilized to derive the control law, so that the developed control scheme has more robustness against the uncertainty and approximation error. Finally, the proposed RCMAC is applied to control a chaotic circuit. Simulation results demonstrate that the proposed control scheme can achieve favorable tracking performance with unknown the controlled system dynamics.     

Adaptive fuzzy sliding-mode control for induction servomotor systems

An adaptive fuzzy sliding-mode control design method is proposed for induction servomotor system control. The proposed adaptive fuzzy sliding-mode control system is comprised of a fuzzy controller and a compensation controller. The fuzzy controller is the main tracking controller, which is used to approximate an ideal computational controller. The compensation controller is designed to compensate for the difference between the ideal computational controller and the fuzzy controller. A tuning methodology is derived to tune the premise and consequence parts of the fuzzy rules. The online tuning algorithm is derived in the Lyapunov sense; thus, the stability of the control system can be guaranteed. Moreover, to relax the requirement for the uncertain bound in the compensation controller, an estimation mechanism is investigated to observe the uncertain bound, so that the chattering phenomena of the control efforts can be relaxed. To illustrate the effectiveness of the proposed design method, a comparison between a conventional fuzzy control and the proposed adaptive fuzzy sliding-mode control is made. Simulation and experimental results verify that the proposed adaptive fuzzy sliding-mode control design method can achieve favorable control performance with regard to parameter variations and external disturbances.     

Recurrent-neural-network-based adaptive-backstepping control for induction servomotors

This study is concerned with the position control of an induction servomotor using a recurrent-neural-network (RNN)-based adaptive-backstepping control (RNABC) system. The adaptive-backstepping approach offers a choice of design tools for the accommodation of system uncertainties and nonlinearities. The RNABC system is comprised of a backstepping controller and a robust controller. The backstepping controller containing an RNN uncertainty observer is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the uncertainty observer. Since the RNN has superior capabilities compared to the feedforward NN for dynamic system identification, it is utilized as the uncertainty observer. In addition, the Taylor linearization technique is employed to increase the learning ability of the RNN. Meanwhile, the adaptation laws of the adaptive-backstepping approach are derived in the sense of the Lyapunov function, thus, the stability of the system can be guaranteed. Finally, simulation and experimental results verify that the proposed RNABC can achieve favorable tracking performance for the induction-servomotor system, even with regard to parameter variations and input-command frequency variation.     

Recurrent wavelet neural backstepping controller design with a smooth compensator

Recurrent wavelet neural network (RWNN) has the advantages in its dynamic responses and information storing ability. This paper develops a recurrent wavelet neural backstepping control (RWNBC) scheme for multiple-input multiple-output (MIMO) mechanical systems. This proposed RWNBC comprises a neural controller and a smooth compensator. The neural controller using an RWNN is the principal tracking controller utilized to mimic an ideal backstepping control law; and the parameters of RWNN are online tuned by the derived adaptation laws from the Lyapunov stability theorem. The smooth compensator is designed to dispel the approximation error introduced by the neural controller, so that the asymptotic stability of the closed-loop system can be guaranteed. Finally, two MIMO mechanical systems, a mass-spring-damper system and a two-inverted pendulum system, are performed to verify the effectiveness of the proposed RWNBC scheme. From the simulation results, it is verified that the proposed RWNBC scheme can achieve favorable tracking performance without any chattering phenomenon.