水下机器人的神经网络自适应控制

研究了水下机器人神经网络直接自适应控制方法,采用Lyapunov稳定性理论,证明了存在有界外界干扰和有界神经网络逼近误差条件下,水下机器人控制系统的跟踪误差一致稳定有界.为了进一步验证该水控制方法的正确性和稳定性,利用水下机器人实验平台进行了动力定位实验、单自由度跟踪实验和水平面跟踪实验等验证实验.

基于模糊神经网络水下机器人直接自适应控制

提出了基于广义动态模糊神经网络的水下机器人直接自适戍控制方法,该控制方法既不需要预先知道模糊神经结构,也不需要预先的训练阶段,完全通过在线自适应学习算法构建水下机器人的逆动力学模型.首先,本文提出了基于这种网络结构的水下机器人直接自适应控制器,然后,利用Lyapunov稳定理论,证明了基于该控制器的水下机器人控制系统闭环稳定性,最后,采用某水下机器人模型仿真验证了该控制方法的有效性。

深海机器人推进电机系统的混沌控制

深海机器人推进电机系统中出现的混沌现象,直接影响深海机器人稳定性、可靠性和安全性.采用自适应控制技术对其混沌行为加以控制,对该方法的可行性和有效性进行了证明.设计和构造了易于工程实施的混沌控制器,用于深海机器人推进电机系统混沌控制.仿真实验表明,推进电机系统在自适应控制器的作用下可迅速脱离混沌状态,并进入持续稳定状态,控制效果明显.可以为深海机器人推进电机系统中可能出现的混沌运行行为提供控制策略和抑制预案,有利于混沌控制嵌入软件的开发,确保深海机器人稳定、可靠和安全地运行,具有一定的实用价值.

工业机器人机械手的小脑模型自适应控制

本文为动力学控制工业机器人机械手提出一种综合控制算法。该控制算法,利用小脑模型算术计算机模块模拟机器人机械手的动力学方程并计算实现期望运动所需力矩作为前馈力矩控制项;利用自适应控制器实现反馈控制,以消除由输入扰动和参数变化而引起的机器人机械手运动误差。这种控制方法在时间上是有效的,且很适合于定点实现。控制方法的有效性通过四自由度的直接驱动机器人前两个关节的计算机仿真实验得到验证。

智能机器人基于作业子空间划分的自学习自适应控制方法

现有的机器人自适应控制基本上都是在建立机器人线性化的动力学模型的基础上,采用某种显式或隐式参数辨识的方法,在线地修正控制作用.本文针对机器人运动和动力学参数变化的固有特点,提出一种完全不同的自学习自适应方法.这种方法基于智能机器人分级系统中的两级结构,并且在空间域里而不是在时间域里处理机器人参数的变化.把机器人的作业空间划分成子空间,其中包括重力载荷的作用,每个子空间对应一组控制器.规划的轨迹映射到作业空间形成子空间序列.用自学习方法选择与这个序列对应的最佳控制器序列.该方法算法简单,计算量小.避开了通常的自适应方法遇到的一系列困难问题.

一种新的机器人自适应控制方式

本文提出了一种新的、有效的机器人自适应控制方式,克服了其他方法由于模型不准或计算量大等所带来的一系列问题。本文首先将 Lagrange 运动方程转化为 ARMA 模型,并用虚拟噪声补偿模型误差(即由于线性化、解耦、观测不准和干扰等误差).然后利用改进的 Kalman 自适应滤波算法在线进行参数辨识和状态估计,将获得的参数用于机器人控制系统自适应控制器的设计.最后给出了该算法的仿真结果并对此进行了讨论。

Roles of Knowledge in Motor Learning

The goal of this thesis is to apply the computational approach to motor learning, i.e., describe the constraints that enable performance improvement with experience and also the constraints that must be satisfied by a motor learning system, describe what is being computed in order to achieve learning, and why it is being computed. The particular tasks used to assess motor learning are loaded and unloaded free arm movement, and the thesis includes work on rigid body load estimation, arm model estimation, optimal filtering for model parameter estimation, and trajectory learning from practice. Learning algorithms have been developed and implemented in the context of robot arm control. The thesis demonstrates some of the roles of knowledge in learning. Powerful generalizations can be made on the basis of knowledge of system structure, as is demonstrated in the load and arm model estimation algorithms. Improving the performance of parameter estimation algorithms used in learning involves knowledge of the measurement noise characteristics, as is shown in the derivation of optimal filters. Using trajectory errors to correct commands requires knowledge of how command errors are transformed into performance errors, i.e., an accurate model of the dynamics of the controlled system, as is demonstrated in the trajectory learning work. The performance demonstrated by the algorithms developed in this thesis should be compared with algorithms that use less knowledge, such as table based schemes to learn arm dynamics, previous single trajectory learning algorithms, and much of traditional adaptive control.

SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-Oriented Navigation System

Both animals and mobile robots, or animats, need adaptive control systems to guide their movements through a novel environment. Such control systems need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once the environment is familiar. How reactive and planned behaviors interact together in real time, and arc released at the appropriate times, during autonomous navigation remains a major unsolved problern. This work presents an end-to-end model to address this problem, named SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation system. The model comprises several interacting subsystems, governed by systems of nonlinear differential equations. As the animat explores the environment, a vision module processes visual inputs using networks that arc sensitive to visual form and motion. Targets processed within the visual form system arc categorized by real-time incremental learning. Simultaneously, visual target position is computed with respect to the animat's body. Estimates of target position activate a motor system to initiate approach movements toward the target. Motion cues from animat locomotion can elicit orienting head or camera movements to bring a never target into view. Approach and orienting movements arc alternately performed during animat navigation. Cumulative estimates of each movement, based on both visual and proprioceptive cues, arc stored within a motor working memory. Sensory cues are stored in a parallel sensory working memory. These working memories trigger learning of sensory and motor sequence chunks, which together control planned movements. Effective chunk combinations arc selectively enhanced via reinforcement learning when the animat is rewarded. The planning chunks effect a gradual transition from reactive to planned behavior. The model can read-out different motor sequences under different motivational states and learns more efficient paths to rewarded goals as exploration proceeds. Several volitional signals automatically gate the interactions between model subsystems at appropriate times. A 3-D visual simulation environment reproduces the animat's sensory experiences as it moves through a simplified spatial environment. The SOVEREIGN model exhibits robust goal-oriented learning of sequential motor behaviors. Its biomimctic structure explicates a number of brain processes which are involved in spatial navigation.

Moderately elevated plasma homocysteine, MTHFR genotype and risk for stroke, vascular dementia and alzheimer's disease in northern ireland

BACKGROUND AND PURPOSE: Elevated plasma homocysteine level has been associated with increased risk for cardiovascular and cerebrovascular disease. Variation in the levels of this amino acid has been shown to be due to nutritional status and methylenetetrahydrofolate reductase (MTHFR) genotype. METHODS: Under a case-control design we compared fasting levels of homocysteine and MTHFR genotypes in groups of subjects consisting of stroke, vascular dementia (VaD), and Alzheimer disease patients and normal controls from Northern Ireland. RESULTS: A significant increase in plasma homocysteine was observed in all 3 disease groups compared with controls. This remained significant after allowance for confounding factors (age, sex, hypertension, cholesterol, smoking, creatinine, and nutritional measures). MTHFR genotype was not found to influence homocysteine levels, although the T allele was found to increase risk for VaD and perhaps dementia after stroke. CONCLUSIONS: We report that moderately high plasma levels of homocysteine are associated with stroke, VaD, and Alzheimer disease. This is not due to vascular risk factors, nutritional status, or MTHFR genotype

Modelling the MAPK signalling pathway using a two-stage identification algorithm

Signal transduction pathways describe the dynamics of cellular response to input signalling molecules at receptors on the cell membrane. The Mitogen-Activated Protein Kinase (MAPK) cascade is one of such pathways that are involved in many important cellular processes including cell growth and proliferation. This paper describes a black-box model of this pathway created using an advanced two-stage identification algorithm. Identification allows us to capture the unique features and dynamics of the pathway and also opens up the possibility of regulatory control design. In the approach described, an optimal model is obtained by performing model subset selection in two stages, where the terms are first determined by a forward selection method and then modified using a backward selection model refinement. The simulation results demonstrate that the model selected using the two-stage algorithm performs better than with the forward selection method alone.

Genetic association analyses of non-synonymous single nucleotide polymorphisms in diabetic nephropathy

Aims/hypothesis: Diabetic nephropathy, characterised by persistent proteinuria, hypertension and progressive kidney failure, affects a subset of susceptible individuals with diabetes. It is also a leading cause of end-stage renal disease (ESRD). Non-synonymous (ns) single nucleotide polymorphisms (SNPs) have been reported to contribute to genetic susceptibility in both monogenic disorders and common complex diseases. The objective of this study was to investigate whether nsSNPs are involved in susceptibility to diabetic nephropathy using a case-control design.

Methods: White type 1 diabetic patients with (cases) and without (controls) nephropathy from eight centres in the UK and Ireland were genotyped for a selected subset of nsSNPs using Illumina's GoldenGate BeadArray assay. A ? 2 test for trend, stratified by centre, was used to assess differences in genotype distribution between cases and controls. Genomic control was used to adjust for possible inflation of test statistics, and the False Discovery Rate method was used to account for multiple testing.

Results: We assessed 1,111 nsSNPs for association with diabetic nephropathy in 1,711 individuals with type 1 diabetes (894 cases, 817 controls). A number of SNPs demonstrated a significant difference in genotype distribution between groups before but not after correction for multiple testing. Furthermore, neither subgroup analysis (diabetic nephropathy with ESRD or diabetic nephropathy without ESRD) nor stratification by duration of diabetes revealed any significant differences between groups.

Conclusions/interpretation: The nsSNPs investigated in this study do not appear to contribute significantly to the development of diabetic nephropathy in patients with type 1 diabetes.

Tooth loss and implant outcomes in patients refractory to treatment in a periodontal practice

Aim: The aim of this study was to investigate the factors associated with continued significant tooth loss due to periodontal reasons during maintenance following periodontal therapy in a specialist periodontal practice in Norway.
Material and Methods: A case-control design was used. Refractory cases were patients who lost multiple teeth during a maintenance period of 13.4 (range 8-19) years following definitive periodontal treatment in a specialist practice. Controls were age- and gender-matched maintenance patients from the same practice. Characteristics and treatment outcomes were assessed, and all teeth classified as being lost due to periodontal disease during follow-up were identified. The use of implants in refractory cases and any complications relating to such a treatment were recorded.
Results: Only 27 (2.2%) patients who received periodontal treatment between 1986 and 1998 in a specialist practice met the criteria for inclusion in the refractory to treatment group. Each refractory subject lost 10.4 (range 4-16) teeth, which represented 50% of the teeth present at baseline. The rate of tooth loss in the refractory group was 0.78 teeth per year, which was 35 times greater than that in the control group. Multivariate analysis indicated that being in the refractory group was predicted by heavy smoking (p=0.026), being stressed (p=0.016) or having a family history of periodontitis (p=0.002). Implants were placed in 14 of the refractory patients and nine (64%) of these lost at least one implant. In total, 17 (25%) of the implants placed in the refractory group were lost during the study period.
Conclusions: A small number of periodontal maintenance patients are refractive to treatment and go on to experience significant tooth loss. These subjects also have a high level of implant complications and failure. Heavy smoking, stress and a family history of periodontal disease were identified as factors associated with a refractory outcome.

Association analysis of Notch pathway signalling genes in diabetic nephropathy

Several studies have provided compelling evidence implicating the Notch signalling pathway in diabetic nephropathy. Co-regulation of Notch signalling pathway genes with GREM1 has recently been demonstrated and several genes involved in the Notch pathway are differentially expressed in kidney biopsies from individuals with diabetic nephropathy. We assessed single-nucleotide polymorphisms (SNPs; n = 42) in four of these key genes (JAG1, HES1, NOTCH3 and ADAM10) for association with diabetic nephropathy using a case-control design.
Tag SNPs and potentially functional SNPs were genotyped using Sequenom or Taqman technologies in a total of 1371 individuals with type 1 diabetes (668 patients with nephropathy and 703 controls without nephropathy). Patients and controls were white and recruited from the UK and Ireland. Association analyses were performed using PLINK (http://pngu.mgh.harvard.edu/similar to purcell/plink/) and haplotype frequencies in patients and controls were compared. Adjustment for multiple testing was performed by permutation testing.
In analyses stratified by centre, we identified six SNPs, rs8708 and rs11699674 (JAG1), rs10423702 and rs1548555 (NOTCH3), rs2054096 and rs8027998 (ADAM10) as being associated with diabetic nephropathy before, but not after, adjustment for multiple testing. Haplotype and subgroup analysis according to duration of diabetes also failed to find an association with diabetic nephropathy.
Our results suggest that common variants in JAG1, HES1, NOTCH3 and ADAM10 are not strongly associated with diabetic nephropathy in type 1 diabetes among white individuals. Our findings, however, cannot entirely exclude these genes from involvement in the pathogenesis of diabetic nephropathy.

Association Analysis of Canonical Wnt Signalling Genes in Diabetic Nephropathy.

Several studies have provided compelling evidence implicating the Wnt signalling pathway in the pathogenesis of diabetic nephropathy. Gene expression profiles associated with renal fibrosis have been attenuated through Wnt pathway modulation in model systems implicating Wnt pathway members as potential therapeutic targets for the treatment of diabetic nephropathy. We assessed tag and potentially functional single nucleotide polymorphisms (SNPs; n = 31) in four key Wnt pathway genes (CTNNB1, AXIN2, LRP5 and LRP6) for association with diabetic nephropathy using a case-control design.

Replication of the association of GLT6D1 with aggressive periodontitis in a Sudanese population

BACKGROUND: Susceptibility to aggressive periodontitis (AgP) is influenced by genetic as well as environmental factors. Studies linking gene variants to AgP have been mainly centred in developed countries with limited data from Africa.
AIM: To investigate whether previously reported candidate gene associations with AgP could be replicated in a population from Sudan.


METHODS: The investigation was a case-control design. Cases with AgP (n = 132) and controls (n = 136) were identified from patients attending the Periodontal Department in Khartoum Dental Hospital. Genotyping was performed using the Sequenom MassARRAY iPLEX platform. Analysis focused on gene variants with a minor allele frequency (MAF) > 25% in the Sudanese subjects that had previously been reported to be associated with AgP.


RESULTS: One candidate gene rs1537415 (GLT6D1) was significantly associated with AgP, OR = 1.50 (95% CI 1.04-2.17), p = 0.0295 (increasing to p = 0.09 after correction for multiple testing). The association strengthened to OR = 1.56 (95% CI 1.15-2.16), p = 0.0042 when the controls were supplemented with data from the Hap map for the Yoruba in Ibadan (n = 147) and remained significant (p = 0.013) after correction for multiple testing.


CONCLUSION: The study independently replicated the finding that rs1537415, a variant in glycosyl transferase gene GLT6D1, is associated with AgP and provided the first report of genetic associations with AgP in a Sudanese population.