An approach to neural control of a class of strict-feedback nonlinear systems

研究一类不确定严反馈非线性系统的跟踪控制问题.通过采用单一神经网络逼近系统的所有未知部分,提出一种新的鲁棒自适应控制设计方法.该方法能直接给出实际控制律和自适应律,有效地解决现有方法中存在的控制设计复杂和计算负担重等问题.稳定性分析表明,闭环系统所有信号是半全局一致最终有界的,并且通过调整控制参数可使跟踪误差任意小.仿真结果验证了所提出方法的有效性.The problem of tracking control is studied for a class of uncertain strict-feedback nonlinear systems.A new robust adaptive control design approach is presented by approximating all the unknown parts of the system with a single neural network.By using this approach,the actual control law and the adaptive law of the controller can be given directly,and the problems,such as control design complexity and high computational burden,are dealt with effectively.The stability analysis shows that the closed-loop system signals are semi-globally uniformly ultimately bounded,and the tracking error can be made arbitrary small by choosing control parameters.Simulation results show the effectiveness of the proposed approach.国家自然科学基金项目(61074017;61074004;61273137;51209026); 辽宁省高等学校优秀人才支持计划项目(2009R06); 中央高校基本科研业务费项目(017004

Computational intelligence margin models for radiotherapeutic cancer treatment

The derivation of margins for use in external beam radiotherapy involves a complex balance between ensuring adequate tumour dose coverage that will lead to cure of the cancer whilst sufficiently sparing the surrounding organs at risk (OARs). The treatment of cancer using ionising radiation is currently witnessing unprecedented levels of new treatment techniques and equipment being introduced. These new treatment strategies, with improved imaging during treatment, are aimed at improved radiation dose conformity to dynamic targets and better sparing of the healthy tissues. However, with the adoption of these new techniques for radiotherapy, the validity of the continued use of recommended statistical model based margin formulations to calculate the treatment margins is now being questioned more than ever before. To derive margins for use in treatment planning which address present shortcomings, this study utilised novel applications of fuzzy logic and neural network techniques to the PTV margin problem. As an extension of this work a new hybrid fuzzy network technique was also adopted for use in margin derivation, a novel application of this technique which required new rule formulations and rule base manipulations. The new margin models developed in this study utilised a novel combination of the radiotherapy errors and their radiobiological effects which was previously difficult to establish using mathematical methods. This was achieved using fuzzy rules and neural network input layers. An advantage of the neural network procedure was that fewer computational steps were needed to calculate the final result whereas the fuzzy based techniques required a significant number of iterative computational steps including the definition of the fuzzy rules and membership functions prior to computation of the final result. An advantage of the fuzzy techniques was their ability to use fewer data points to deduce the relationship between the output and input parameters. In contrast the neural network model requires a large amount of training data. The previously stated limitations of currently recommended statistical techniques were addressed by application of the fuzzy and neural network models. A major advantage of the computational intelligence methods from this study is that they allow the calculation of patient-specific margins. Radiotherapy planning currently relies on the use of ‘one size fits all’ class solutions for margins for each tumour site and with the large variability in patient physiology these margins may not be suitable for use in some cases. The models from this study can be applied to other treatment sites, including brain, lung and gastric tumours.EThOS - Electronic Theses Online ServiceGBUnited Kingdo