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.     

Intelligent adaptive control for MIMO uncertain nonlinear systems

This paper investigates an intelligent adaptive control system for multiple-input–multiple-output (MIMO) uncertain nonlinear systems. This control system is comprised of a recurrent-cerebellar-model-articulation-controller (RCMAC) and an auxiliary compensation controller. RCMAC is utilized to approximate a perfect controller, and the parameters of RCMAC are on-line tuned by the derived adaptive laws based on a Lyapunov function. The auxiliary compensation controller is designed to suppress the influence of residual approximation error between the perfect controller and RCMAC. Finally, two MIMO uncertain nonlinear systems, a mass–spring–damper mechanical system and a Chua’s chaotic circuit, are performed to verify the effectiveness of the proposed control scheme. The simulation results confirm that the proposed intelligent adaptive control system can achieve favorable tracking performance with desired robustness.     

Self-Organizing CMAC Control for a Class of MIMO Uncertain Nonlinear Systems

This paper presents a self-organizing control system based on cerebellar model articulation controller (CMAC) for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. The proposed control system merges a CMAC and sliding-mode control (SMC), so the input space dimension of CMAC can be simplified. The structure of CMAC will be self-organized; that is, the layers of CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The control system consists of a self-organizing CMAC (SOCM) and a robust controller. SOCM containing a CMAC uncertainty observer is used as the principal controller and the robust controller is designed to dispel the effect of approximation error. The gradient-descent method is used to online tune the parameters of CMAC and the Lyapunov function is applied to guarantee the stability of the system. A simulation study of inverted double pendulums system and an experimental result of linear ultrasonic motor motion control show that favorable tracking performance can be achieved by using the proposed control system.     

Wavelet CMAC for MIMO uncertain nonlinear systems using a modified social ski-driver algorithm

This study uses a Mexican hat wavelet membership function for a cerebellar model articulation controller (CMAC) to develop a more efficient adaptive controller for multiple input multiple output (MIMO) uncertain nonlinear systems. The main controller is called the adaptive Mexican hat wavelet CMAC (MWCMAC), and an auxiliary controller is used to remove the residual error. For the MWCMAC, the online learning laws are derived from the gradient descent method. In addition, the learning rate values are very important and have a great impact on the performance of the control system; however, they are difficult to choose accurately. Therefore, a modified social ski driver (SSD) algorithm is proposed to find optimal learning rates for the control parameters. Finally, a magnetic ball levitation system and a nine-link biped robot are used to illustrate the effectiveness of the proposed SSD-based MWCMAC control system. The comparisons with other existing control algorithms have shown the superiority of the proposed control system.     

Intelligent wavelet fuzzy brain emotional controller using dual function-link network for uncertain nonlinear control systems

This study aims to propose a more efficient hybrid algorithm to achieve favorable control performance for uncertain nonlinear systems. The proposed algorithm comprises a dual function-link network-based multilayer wavelet fuzzy brain emotional controller and a sign(.) functional compensator. The proposed algorithm estimates the judgment and emotion of a brain that includes two fuzzy inference systems for the amygdala network and the prefrontal cortex network via using a dual-function-link network and three sub-structures. Three sub-structures are a dual-function-link network, an amygdala network, and a prefrontal cortex network. Particularly, the dual-function-link network is used to adjust the amygdala and orbitofrontal weights separately so that the proposed algorithm can efficiently reduce the tracking error, follow the reference signal well, and achieve good performance. A Lyapunov stability function is used to determine the adaptive laws, which are used to efficiently tune the system parameters online. Simulation and experimental studies for an antilock braking system and a magnetic levitation system are presented to verify the effectiveness and advantage of the proposed algorithm.

     

Commercial application scenario using patent analysis: Fermentative hydrogen production from biomass

The main purpose of this study is to use patent analysis to investigate scenarios for future commercial applications of dark fermentation or anaerobic fermentation using biomass or organic matter as feedstock materials. The first step in this study includes a patent search procedure and patent content interpretation, in which 29 technology patents were identified from the US patent database and divided into five groups in accordance with the scope of their technical applications. The following five scenarios of commercial applications of biomass fermentation for hydrogen production were established through a combination of group applications: screening and cultivation of hydrogen-producing bacteria, biomass waste sources, biomass energization application, value enhancement of waste or wastewater treatment systems, and the application of a multi-functional hydrogen production system integrated with other technologies.     

Growth inhibitory efficacy of lycopene and β-carotene against androgen-independent prostate tumor cells xenografted in nude mice

Scope : In this study, we evaluated the efficacy of lycopene against the growth of prostate cancer in vivo. Methods and results : Athymic nude mice were implanted subcutaneously with human androgen‐independent prostate carcinoma PC‐3 cells. They were supplemented with a low or a high dose of lycopene (4 and 16 mg/kg) and a single dose of β‐carotene (16 mg/kg) twice a week for 7 wk. At the end of the experiment, both lycopene and β‐carotene strongly inhibited the tumor growth, as evidenced by the decrease in tumor volume and tumor weight. High‐dosage lycopene and β‐carotene significantly decreased the expression of proliferating cell nuclear antigen in tumor tissues and increased the levels of insulin‐like growth factor‐binding protein‐3 in plasma. In addition, high‐dosage lycopene supplementation significantly decreased the vascular endothelial growth factor (VEGF) levels in plasma. In contrast, β‐carotene supplementation significantly increased the VEGF levels, as compared with tumor control group. Conclusion : Lycopene and β‐carotene supplementation suppressed the growth of prostate tumor cells, and the effects are likely associated with reduction of proliferation (attenuation of proliferating cell nuclear antigen expression) and with interference of the insulin‐like growth factor 1 signaling (increased plasma insulin‐like growth factor‐binding protein‐3 levels). Furthermore, the inhibition of VEGF by lycopene suggests that the antitumor mechanisms of lycopene also involve anti‐angiogenesis.     

一种具变转速滚珠螺杆机构之伺服冲床设计

研究针对滚珠螺杆机构,利用变转速输入的概念,以冲床之深引伸加工制程应用为例,设计出满足所需运动特性之输入转速函数,再实作一伺服滚珠螺杆机构的原型机测试系统,透过实验验证了研究之可行性。由结果得知,变转速滚珠螺杆机构之多样化的输出运动特性,确能满足伺服冲床高速下降、等速加工、以及下死点停留时间延长等特性。     

Missile guidance law design using adaptive cerebellar model articulation controller

An adaptive cerebellar model articulation controller (CMAC) is proposed for command to line-of-sight (CLOS) missile guidance law design. In this design, the three-dimensional (3-D) CLOS guidance problem is formulated as a tracking problem of a time-varying nonlinear system. The adaptive CMAC control system is comprised of a CMAC and a compensation controller. The CMAC control is used to imitate a feedback linearization control law and the compensation controller is utilized to compensate the difference between the feedback linearization control law and the CMAC control. The online adaptive law is derived based on the Lyapunov stability theorem to learn the weights of receptive-field basis functions in CMAC control. In addition, in order to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. Then the adaptive CMAC control system is designed to achieve satisfactory tracking performance. Simulation results for different engagement scenarios illustrate the validity of the proposed adaptive CMAC-based guidance law.