Industrial robot system is a kind of dynamic system w ith strong nonlinear coupling and high position precision. A lot of control ways , such as nonlinear feedbackdecomposition motion and adaptive control and so o n, ...Industrial robot system is a kind of dynamic system w ith strong nonlinear coupling and high position precision. A lot of control ways , such as nonlinear feedbackdecomposition motion and adaptive control and so o n, have been used to control this kind of system, but there are some deficiencie s in those methods: some need accurate and some need complicated operation and e tc. In recent years, in need of controlling the industrial robots, aiming at com pletely tracking the ideal input for the controlled subject with repetitive character, a new research area, ILC (iterative learning control), has been devel oped in the control technology and theory. The iterative learning control method can make the controlled subject operate as desired in a definite time span, merely making use of the prior control experie nce of the system and searching for the desired control signal according to the practical and desired output signal. The process of searching is equal to that o f learning, during which we only need to measure the output signal to amend the control signal, not like the adaptive control strategy, which on line assesses t he complex parameters of the system. Besides, since the iterative learning contr ol relies little on the prior message of the subject, it has been well used in a lot of areas, especially the dynamic systems with strong non-linear coupling a nd high repetitive position precision and the control system with batch producti on. Since robot manipulator has the above-mentioned character, ILC can be very well used in robot manipulator. In the ILC, since the operation always begins with a certain initial state, init ial condition has been required in almost all convergence verification. Therefor e, in designing the controller, the initial state has to be restricted with some condition to guarantee the convergence of the algorithm. The settle of initial condition problem has long been pursued in the ILC. There are commonly two kinds of initial condition problems: one is zero initial error problem, another is non-zero initial error problem. In practice, the repe titive operation will invariably produce excursion of the iterative initial stat e from the desired initial state. As a result, the research on the second in itial problem has more practical meaning. In this paper, for the non-zero initial error problem, one novel robust ILC alg orithms, respectively combining PD type iterative learning control algorithm wit h the robust feedback control algorithm, has been presented. This novel robust ILC algorithm contain two parts: feedforward ILC algorithm and robust feedback algorithm, which can be used to restrain disturbance from param eter variation, mechanical nonlinearities and unmodeled dynamics and to achieve good performance as well. The feedforward ILC algorithm can be used to improve the tracking error and perf ormance of the system through iteratively learning from the previous operation, thus performing the tracking task very fast. The robust feedback algorithm could mainly be applied to make the real output of the system not deviate too much fr om the desired tracking trajectory, and guarantee the system’s robustness w hen there are exterior noises and variations of the system parameter. In this paper, in order to analyze the convergence of the algorithm, Lyapunov st ability theory has been used through properly selecting the Lyapunov function. T he result of the verification shows the feasibility of the novel robust iterativ e learning control in theory. Finally, aiming at the two-freedom rate robot, simulation has been made with th e MATLAB software. Furthermore, two groups of parameters are selected to validat e the robustness of the algorithm.展开更多
As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely...As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.展开更多
当今科技飞速发展,隐私保护成为一个重要议题.为了确保数据的安全性,通常选择将数据加密后存储在云服务器上,然而这样云服务器无法对加密后的数据进行计算、统计等有效处理,从而使得很多应用场景受限.为了解决这个问题,提出一种基于环...当今科技飞速发展,隐私保护成为一个重要议题.为了确保数据的安全性,通常选择将数据加密后存储在云服务器上,然而这样云服务器无法对加密后的数据进行计算、统计等有效处理,从而使得很多应用场景受限.为了解决这个问题,提出一种基于环上容错学习(ring learning with error,R-LWE)问题的PKE-MET(public-key encryption with a multiple-ciphertext equality test)方案,并给出了正确性和安全性分析.该方案允许云服务器同时对多个密文执行相等性测试,还能够抵抗量子计算攻击.基于Palisade库对方案进行了实现,从理论与实现的角度与其他方案进行了比较分析.相较于其他方案,该方案具有高效、运行时间短的优点.展开更多
保护测量回路是电力系统继电保护的基石,其误差评估对电网安稳运维举足轻重。针对保护测量回路静态隐藏误差可能诱发保护误动/拒动的风险且难以在线监测问题,提出了一种基于递推主元分析和改进灰狼算法优化极限学习机(recursive princip...保护测量回路是电力系统继电保护的基石,其误差评估对电网安稳运维举足轻重。针对保护测量回路静态隐藏误差可能诱发保护误动/拒动的风险且难以在线监测问题,提出了一种基于递推主元分析和改进灰狼算法优化极限学习机(recursive principal component analysis and extreme learning machine optimized by grey wolf optimization,RPCA-GELM)数据驱动的保护测量回路误差评估方法。首先基于电力系统正常运行下历史数据与实时数据,应用RPCA技术在线更新主元特征模型以缩短评估时间,进一步引入4种统计算法生成4类误差监测特征量,构建误差综合评判方法进行特征优选,提升误差评估准确率。然后针对模型评估精度取决于关键参数C、σ,引入国际无限折叠混沌映射策略对灰狼算法进行优化,以提升参数寻优精度和收敛速度,在此基础上结合ELM算法提出了基于GELM的保护测量回路误差评估方法。最后通过多组对比实验验证了所提方法能实现模型性能优化,且相对其他方法有效提升了保护测量回路误差评估准确率与精度。展开更多
利用格上LWE(Leaning With Error)困难性假设,将保密地比较两个数是否相等转化为判断对随机串加密后的解密是否正确,有效地解决了数和集合关系的判定、求集合交集和集合相等安全多方计算问题,并利用模拟范例证明该协议在半诚实模型下是...利用格上LWE(Leaning With Error)困难性假设,将保密地比较两个数是否相等转化为判断对随机串加密后的解密是否正确,有效地解决了数和集合关系的判定、求集合交集和集合相等安全多方计算问题,并利用模拟范例证明该协议在半诚实模型下是安全的。与传统的基于数论的协议相比,该方案由于不需要模指数运算,因而具有较低的计算复杂度,同时因其基于格中困难问题,因而能抵抗量子攻击。展开更多
该文提出了一种基于LWE(Learning With Errors)算法的密文域可逆隐写方案,利用LWE公钥密码算法对数据加密,用户在密文中嵌入隐藏信息,对于嵌入信息后的密文,用户使用隐写密钥可以有效提取隐藏信息,使用解密密钥可以无差错恢复出加密前...该文提出了一种基于LWE(Learning With Errors)算法的密文域可逆隐写方案,利用LWE公钥密码算法对数据加密,用户在密文中嵌入隐藏信息,对于嵌入信息后的密文,用户使用隐写密钥可以有效提取隐藏信息,使用解密密钥可以无差错恢复出加密前数据实现了提取过程与解密过程的可分离。通过推导方案在解密与提取信息过程中出错的概率,得到直接影响方案正确性的参数为所选噪声的标准差,实验获得并验证了标准差的合理取值区间;通过推导嵌入后密文的分布函数,分析密文统计特征的变化情况,论证了嵌入密文的隐藏信息的不可感知性。该方案是在密文域进行的可逆隐写,与原始载体无关,适用于文本、图片、音频等各类载体。实验仿真结果表明该方案不仅能够保证可逆隐写的可靠性与安全性,而且1 bit明文在密文域最大可负载1 bit隐藏信息。展开更多
密文域可逆信息隐藏是一种以密文为载体进行信息嵌入与提取,同时能够对嵌入信息后的密文进行无失真解密并恢复出原始明文的信息隐藏技术,具有隐私保护与信息隐藏双重功能,在密文域数据处理与管理中具有较好的应用前景.因此,提出了一种基...密文域可逆信息隐藏是一种以密文为载体进行信息嵌入与提取,同时能够对嵌入信息后的密文进行无失真解密并恢复出原始明文的信息隐藏技术,具有隐私保护与信息隐藏双重功能,在密文域数据处理与管理中具有较好的应用前景.因此,提出了一种基于R-LWE(ring-learning with errors)的密文域多比特可逆信息隐藏方案.首先使用R-LWE算法对载体明文进行快速高强度加密,然后通过对单位比特明文在密文空间映射区域的重量化以及对应密文的再编码,实现了在密文中嵌入多比特隐藏信息;嵌入信息时,根据加密过程中的数据分布特征来进行嵌入编码,保证了加解密与信息提取的鲁棒性;解密与提取信息时,先计算量化系数,而后采用不同的量化标准分别进行解密或信息提取,实现了解密与提取过程的可分离.分析方案的正确性时,首先推导方案出错的概率,说明了算法中引入的噪声的标准差对方案正确性的影响,然后结合理论分析与实验得出了保证方案正确性的噪声标准差的取值区间;通过推导嵌入后密文的分布函数,分析密文统计特征的变化,论证了密文中嵌入隐藏信息的不可感知性.实验结果表明:该文方案不仅能够实现嵌入后密文的无差错解密与秘密信息的可靠提取,并且单位比特明文在密文域能够负载多比特隐藏信息,密文嵌入率最高可达到0.2353bpb.展开更多
文摘Industrial robot system is a kind of dynamic system w ith strong nonlinear coupling and high position precision. A lot of control ways , such as nonlinear feedbackdecomposition motion and adaptive control and so o n, have been used to control this kind of system, but there are some deficiencie s in those methods: some need accurate and some need complicated operation and e tc. In recent years, in need of controlling the industrial robots, aiming at com pletely tracking the ideal input for the controlled subject with repetitive character, a new research area, ILC (iterative learning control), has been devel oped in the control technology and theory. The iterative learning control method can make the controlled subject operate as desired in a definite time span, merely making use of the prior control experie nce of the system and searching for the desired control signal according to the practical and desired output signal. The process of searching is equal to that o f learning, during which we only need to measure the output signal to amend the control signal, not like the adaptive control strategy, which on line assesses t he complex parameters of the system. Besides, since the iterative learning contr ol relies little on the prior message of the subject, it has been well used in a lot of areas, especially the dynamic systems with strong non-linear coupling a nd high repetitive position precision and the control system with batch producti on. Since robot manipulator has the above-mentioned character, ILC can be very well used in robot manipulator. In the ILC, since the operation always begins with a certain initial state, init ial condition has been required in almost all convergence verification. Therefor e, in designing the controller, the initial state has to be restricted with some condition to guarantee the convergence of the algorithm. The settle of initial condition problem has long been pursued in the ILC. There are commonly two kinds of initial condition problems: one is zero initial error problem, another is non-zero initial error problem. In practice, the repe titive operation will invariably produce excursion of the iterative initial stat e from the desired initial state. As a result, the research on the second in itial problem has more practical meaning. In this paper, for the non-zero initial error problem, one novel robust ILC alg orithms, respectively combining PD type iterative learning control algorithm wit h the robust feedback control algorithm, has been presented. This novel robust ILC algorithm contain two parts: feedforward ILC algorithm and robust feedback algorithm, which can be used to restrain disturbance from param eter variation, mechanical nonlinearities and unmodeled dynamics and to achieve good performance as well. The feedforward ILC algorithm can be used to improve the tracking error and perf ormance of the system through iteratively learning from the previous operation, thus performing the tracking task very fast. The robust feedback algorithm could mainly be applied to make the real output of the system not deviate too much fr om the desired tracking trajectory, and guarantee the system’s robustness w hen there are exterior noises and variations of the system parameter. In this paper, in order to analyze the convergence of the algorithm, Lyapunov st ability theory has been used through properly selecting the Lyapunov function. T he result of the verification shows the feasibility of the novel robust iterativ e learning control in theory. Finally, aiming at the two-freedom rate robot, simulation has been made with th e MATLAB software. Furthermore, two groups of parameters are selected to validat e the robustness of the algorithm.
基金supported by the National Natural Science Foundation of China(62375013).
文摘As the core component of inertial navigation systems, fiber optic gyroscope (FOG), with technical advantages such as low power consumption, long lifespan, fast startup speed, and flexible structural design, are widely used in aerospace, unmanned driving, and other fields. However, due to the temper-ature sensitivity of optical devices, the influence of environmen-tal temperature causes errors in FOG, thereby greatly limiting their output accuracy. This work researches on machine-learn-ing based temperature error compensation techniques for FOG. Specifically, it focuses on compensating for the bias errors gen-erated in the fiber ring due to the Shupe effect. This work pro-poses a composite model based on k-means clustering, sup-port vector regression, and particle swarm optimization algo-rithms. And it significantly reduced redundancy within the sam-ples by adopting the interval sequence sample. Moreover, met-rics such as root mean square error (RMSE), mean absolute error (MAE), bias stability, and Allan variance, are selected to evaluate the model’s performance and compensation effective-ness. This work effectively enhances the consistency between data and models across different temperature ranges and tem-perature gradients, improving the bias stability of the FOG from 0.022 °/h to 0.006 °/h. Compared to the existing methods utiliz-ing a single machine learning model, the proposed method increases the bias stability of the compensated FOG from 57.11% to 71.98%, and enhances the suppression of rate ramp noise coefficient from 2.29% to 14.83%. This work improves the accuracy of FOG after compensation, providing theoretical guid-ance and technical references for sensors error compensation work in other fields.
文摘当今科技飞速发展,隐私保护成为一个重要议题.为了确保数据的安全性,通常选择将数据加密后存储在云服务器上,然而这样云服务器无法对加密后的数据进行计算、统计等有效处理,从而使得很多应用场景受限.为了解决这个问题,提出一种基于环上容错学习(ring learning with error,R-LWE)问题的PKE-MET(public-key encryption with a multiple-ciphertext equality test)方案,并给出了正确性和安全性分析.该方案允许云服务器同时对多个密文执行相等性测试,还能够抵抗量子计算攻击.基于Palisade库对方案进行了实现,从理论与实现的角度与其他方案进行了比较分析.相较于其他方案,该方案具有高效、运行时间短的优点.
文摘保护测量回路是电力系统继电保护的基石,其误差评估对电网安稳运维举足轻重。针对保护测量回路静态隐藏误差可能诱发保护误动/拒动的风险且难以在线监测问题,提出了一种基于递推主元分析和改进灰狼算法优化极限学习机(recursive principal component analysis and extreme learning machine optimized by grey wolf optimization,RPCA-GELM)数据驱动的保护测量回路误差评估方法。首先基于电力系统正常运行下历史数据与实时数据,应用RPCA技术在线更新主元特征模型以缩短评估时间,进一步引入4种统计算法生成4类误差监测特征量,构建误差综合评判方法进行特征优选,提升误差评估准确率。然后针对模型评估精度取决于关键参数C、σ,引入国际无限折叠混沌映射策略对灰狼算法进行优化,以提升参数寻优精度和收敛速度,在此基础上结合ELM算法提出了基于GELM的保护测量回路误差评估方法。最后通过多组对比实验验证了所提方法能实现模型性能优化,且相对其他方法有效提升了保护测量回路误差评估准确率与精度。
文摘利用格上LWE(Leaning With Error)困难性假设,将保密地比较两个数是否相等转化为判断对随机串加密后的解密是否正确,有效地解决了数和集合关系的判定、求集合交集和集合相等安全多方计算问题,并利用模拟范例证明该协议在半诚实模型下是安全的。与传统的基于数论的协议相比,该方案由于不需要模指数运算,因而具有较低的计算复杂度,同时因其基于格中困难问题,因而能抵抗量子攻击。
文摘该文提出了一种基于LWE(Learning With Errors)算法的密文域可逆隐写方案,利用LWE公钥密码算法对数据加密,用户在密文中嵌入隐藏信息,对于嵌入信息后的密文,用户使用隐写密钥可以有效提取隐藏信息,使用解密密钥可以无差错恢复出加密前数据实现了提取过程与解密过程的可分离。通过推导方案在解密与提取信息过程中出错的概率,得到直接影响方案正确性的参数为所选噪声的标准差,实验获得并验证了标准差的合理取值区间;通过推导嵌入后密文的分布函数,分析密文统计特征的变化情况,论证了嵌入密文的隐藏信息的不可感知性。该方案是在密文域进行的可逆隐写,与原始载体无关,适用于文本、图片、音频等各类载体。实验仿真结果表明该方案不仅能够保证可逆隐写的可靠性与安全性,而且1 bit明文在密文域最大可负载1 bit隐藏信息。
文摘密文域可逆信息隐藏是一种以密文为载体进行信息嵌入与提取,同时能够对嵌入信息后的密文进行无失真解密并恢复出原始明文的信息隐藏技术,具有隐私保护与信息隐藏双重功能,在密文域数据处理与管理中具有较好的应用前景.因此,提出了一种基于R-LWE(ring-learning with errors)的密文域多比特可逆信息隐藏方案.首先使用R-LWE算法对载体明文进行快速高强度加密,然后通过对单位比特明文在密文空间映射区域的重量化以及对应密文的再编码,实现了在密文中嵌入多比特隐藏信息;嵌入信息时,根据加密过程中的数据分布特征来进行嵌入编码,保证了加解密与信息提取的鲁棒性;解密与提取信息时,先计算量化系数,而后采用不同的量化标准分别进行解密或信息提取,实现了解密与提取过程的可分离.分析方案的正确性时,首先推导方案出错的概率,说明了算法中引入的噪声的标准差对方案正确性的影响,然后结合理论分析与实验得出了保证方案正确性的噪声标准差的取值区间;通过推导嵌入后密文的分布函数,分析密文统计特征的变化,论证了密文中嵌入隐藏信息的不可感知性.实验结果表明:该文方案不仅能够实现嵌入后密文的无差错解密与秘密信息的可靠提取,并且单位比特明文在密文域能够负载多比特隐藏信息,密文嵌入率最高可达到0.2353bpb.
