Data-Driven Active Disturbance Rejection Control of Plant-Protection Unmanned Ground Vehicle Prototype: A Fuzzy Indirect Iterative Learning Approach

Dear Editor,This letter proposes a fuzzy indirect iterative learning(FIIL)active disturbance rejection control(ADRC)scheme to address the impact of uncertain factors of plant-protection unmanned ground vehicle(UGV),in which ADRC is a data-driven model-free control algorithm that only relies on the input and output data of the system.Based on the established nonlinear time-varying dynamic model including dynamic load(medicine box),the FIIL technology is adopted to turn the bandwidth and control channel gain online,in which the fuzzy logic system is used to update the gain parameters of iterative learning in real time.Simulation and experiment show the FIIL-ADRC scheme has better control performance.

Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning Approach

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation.Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

Iterative physical optics method based on efficient occlusion judgment with bounding volume hierarchy technology

This paper builds a binary tree for the target based on the bounding volume hierarchy technology,thereby achieving strict acceleration of the shadow judgment process and reducing the computational complexity from the original O(N^(3))to O(N^(2)logN).Numerical results show that the proposed method is more efficient than the traditional method.It is verified in multiple examples that the proposed method can complete the convergence of the current.Moreover,the proposed method avoids the error of judging the lit-shadow relationship based on the normal vector,which is beneficial to current iteration and convergence.Compared with the brute force method,the current method can improve the simulation efficiency by 2 orders of magnitude.The proposed method is more suitable for scattering problems in electrically large cavities and complex scenarios.

A Modified Iterative Learning Control Approach for the Active Suppression of Rotor Vibration Induced by Coupled Unbalance and Misalignment

This paper proposes a modified iterative learning control(MILC)periodical feedback-feedforward algorithm to reduce the vibration of a rotor caused by coupled unbalance and parallel misalignment.The control of the vibration of the rotor is provided by an active magnetic actuator(AMA).The iterative gain of the MILC algorithm here presented has a self-adjustment based on the magnitude of the vibration.Notch filters are adopted to extract the synchronous(1×Ω)and twice rotational frequency(2×Ω)components of the rotor vibration.Both the notch frequency of the filter and the size of feedforward storage used during the experiment have a real-time adaptation to the rotational speed.The method proposed in this work can provide effective suppression of the vibration of the rotor in case of sudden changes or fluctuations of the rotor speed.Simulations and experiments using the MILC algorithm proposed here are carried out and give evidence to the feasibility and robustness of the technique proposed.

Co-salient object detection with iterative purification and predictive optimization

Background Co-salient object detection(Co-SOD)aims to identify and segment commonly salient objects in a set of related images.However,most current Co-SOD methods encounter issues with the inclusion of irrelevant information in the co-representation.These issues hamper their ability to locate co-salient objects and significantly restrict the accuracy of detection.Methods To address this issue,this study introduces a novel Co-SOD method with iterative purification and predictive optimization(IPPO)comprising a common salient purification module(CSPM),predictive optimizing module(POM),and diminishing mixed enhancement block(DMEB).Results These components are designed to explore noise-free joint representations,assist the model in enhancing the quality of the final prediction results,and significantly improve the performance of the Co-SOD algorithm.Furthermore,through a comprehensive evaluation of IPPO and state-of-the-art algorithms focusing on the roles of CSPM,POM,and DMEB,our experiments confirmed that these components are pivotal in enhancing the performance of the model,substantiating the significant advancements of our method over existing benchmarks.Experiments on several challenging benchmark co-saliency datasets demonstrate that the proposed IPPO achieves state-of-the-art performance.

Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control(ILC) scheme based on the zeroing neural networks(ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer(IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise,an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

Adaptive state-constrained/model-free iterative sliding mode control for aerial robot trajectory tracking

This paper develops a novel hierarchical control strategy for improving the trajectory tracking capability of aerial robots under parameter uncertainties.The hierarchical control strategy is composed of an adaptive sliding mode controller and a model-free iterative sliding mode controller(MFISMC).A position controller is designed based on adaptive sliding mode control(SMC)to safely drive the aerial robot and ensure fast state convergence under external disturbances.Additionally,the MFISMC acts as an attitude controller to estimate the unmodeled dynamics without detailed knowledge of aerial robots.Then,the adaption laws are derived with the Lyapunov theory to guarantee the asymptotic tracking of the system state.Finally,to demonstrate the performance and robustness of the proposed control strategy,numerical simulations are carried out,which are also compared with other conventional strategies,such as proportional-integralderivative(PID),backstepping(BS),and SMC.The simulation results indicate that the proposed hierarchical control strategy can fulfill zero steady-state error and achieve faster convergence compared with conventional strategies.

The Alternating Group Explicit Iterative Method for the Regularized Long-Wave Equation

An Alternating Group Explicit (AGE) iterative method with intrinsic parallelism is constructed based on an implicit scheme for the Regularized Long-Wave (RLW) equation. The method can be used for the iteration solution of a general tridiagonal system of equations with diagonal dominance. It is not only easy to implement, but also can directly carry out parallel computation. Convergence results are obtained by analysing the linear system. Numerical experiments show that the theory is accurate and the scheme is valid and reliable.

Vibration Suppression for Active Magnetic Bearings Using Adaptive Filter with Iterative Search Algorithm

Active Magnetic Bearing(AMB) is a kind of electromagnetic support that makes the rotor movement frictionless and can suppress rotor vibration by controlling the magnetic force. The most common approach to restrain the rotor vibration in AMBs is to adopt a notch filter or adaptive filter in the AMB controller. However, these methods cannot obtain the precise amplitude and phase of the compensation current. Thus, they are not so effective in terms of suppressing the vibrations of the fundamental and other harmonic orders over the whole speed range. To improve the vibration suppression performance of AMBs,an adaptive filter based on Least Mean Square(LMS) is applied to extract the vibration signals from the rotor displacement signal. An Iterative Search Algorithm(ISA) is proposed in this paper to obtain the corresponding relationship between the compensation current and vibration signals. The ISA is responsible for searching the compensating amplitude and shifting phase online for the LMS filter, enabling the AMB controller to generate the corresponding compensation force for vibration suppression. The results of ISA are recorded to suppress vibration using the Look-Up Table(LUT) in variable speed range. Comprehensive simulations and experimental validations are carried out in fixed and variable speed range, and the results demonstrate that by employing the ISA, vibrations of the fundamental and other harmonic orders are suppressed effectively.

Low-complexity signal detection for massive MIMO systems via trace iterative method

Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.

四维水资源供需混沌系统的自适应滑模同步

研究了水资源供需混沌系统的自适应滑模同步,根据李雅普诺夫稳定性理论和同步控制相关理论给出了同步控制的研究结论,并用Matlab仿真技术画出了吸引子相图和系统的误差曲线,并用仿真算例给出了系统参数的选取以及初始值的设置,对所得结果进行了数值验证.

基于多项式混沌展开的电力系统概率可用输电能力评估

大规模开发和利用风能有利于实现电力系统清洁低碳转型,是实现国家“碳达峰、碳中和”战略目标的重要技术手段,但风电出力的强不确定性对电力系统区域间可用输电能力(available transfer capability,ATC)评估带来了全新的挑战,传统用于求解计及风电出力不确定性的概率ATC评估模型在计算效率和计算精度方面均存在一定的不足。为此,该文提出一种基于多项式混沌展开(polynomialchaos expansion,PCE)的电力系统概率ATC评估方法,该方法首先构建基于机会约束的电力系统概率ATC评估模型;然后,根据风电出力预测误差的概率分布特征,选择对应的正交多项式为基函数以近似风电出力预测误差及电力网络中与之相关联的其他随机变量;进一步,借助Galerkin投影和基于一阶矩、二阶矩的机会约束转化方法,将所构建的机会约束模型的概率约束转化为确定性约束,实现基于机会约束的概率ATC评估模型向易于求解的确定性优化模型的转化;进而,将概率ATC评估模型的求解问题转化为ATC的最优多项式逼近系数的求解问题,根据求得的最优多项式逼近系数和选取的基函数计算电力系统ATC的概率分布特征;最后,通过修改后的PJM-5节点测试系统、IEEE-118节点测试系统及吉林西部电网实际算例验证了所提基于多项式混沌展开的电力系统概率ATC评估方法的准确性和有效性。

基于混沌理论与DNA动态编码的卫星图像加密算法

针对卫星图像在传输、存储过程中涉及的信息安全问题,该文提出一种新型的基于混沌理论与DNA动态编码的卫星图像加密算法。首先,提出一种改进型无限折叠混沌映射,拓宽了原有无限折叠混沌映射的混沌区间。之后,结合改进型Chebyshev混沌映射与SHA-256哈希算法,生成加密算法的密钥流,提升算法的明文敏感性。然后,利用混沌系统的状态值对Hilbert局部置乱后的像素进行DNA编码,实现DNA动态编码,解决了DNA编码规则较少所带来的容易受到暴力攻击的弱点。最后,使用混沌序列完成进一步混沌加密,从而彻底混淆原始像素信息,增加加密算法的随机性与复杂性,得到密文图像。实验结果表明,该算法具有较好的加密效果和应对各种攻击的能力。

基于全光混沌注入双环电光相位反馈环的混沌保密通信系统研究

针对现有混沌保密光通信系统时延特征隐藏所需反馈增益系数较大这一问题,设计了一种基于全光混沌注入双环电光相位反馈环的混沌保密通信系统。首先,由全光反馈产生光混沌并将其耦合至2个电光相位混沌反馈环中。然后,通过相位调制的方式实现信息与混沌载波的非线性耦合并参与混沌载波的生成。最后,在检测端通过光混沌同步实现信号的解密。借助MATLAB软件仿真分析了系统中混沌信号的复杂度、时延特征隐藏、同步性、鲁棒性和通信性能。仿真结果表明,所设计的混沌保密通信系统在较小的反馈系数时具有较高的复杂度且时延特征得到较好的隐藏。

混沌密码理论研究与应用新进展

混沌密码研究主要包括混沌序列密码、混沌分组密码和混沌公钥密码。混沌序列密码具有软硬件实现简单、加解密处理速度快、没有或只有有限的错误传播等特点。混沌序列密码的设计方面研究成果极为丰硕,但其安全性分析一般停留在统计性验证分析阶段,而对可证明安全性或其他有条件安全性的证明和分析很少,因此证明混沌序列密码的安全性已成为该领域亟需填补的空白。混沌分组密码的研究颇为成熟,但其整体结构一般受限于Feistel结构、SPN结构、Lai-Massey结构及其变形结构。因此,构建突破传统结构的创新性密码架构成为混沌分组密码亟待解决的关键问题之一。混沌公钥密码的研究相对薄弱,利用混沌系统特有的密码特性和公钥密码系统相结合的混沌公钥密码算法不仅具有理论研究意义,而且具有实际应用价值。混沌系统也被广泛应用于通信传输、语音加密、图像加密和扩频通信等保密通信领域。电、光混沌系统实现简便,其计算复杂性不会随着密钥空间的增大而明显增大,能够为无线通信、物理层链路安全、应用层数据安全等不同场景提供安全保护。

多电平级联H桥逆变器通用型离散模型及分岔与混沌行为特性分析

多电平级联H桥逆变器内部功率器件多、等效开关频率高、开关组合状态繁杂、离散数学模型建立难度大,其分岔、混沌动力学行为难以进行系统性分析。基于虚拟遍历法建立了具有N个单元的多电平级联H桥逆变器通用型离散映射数学模型;引入对交流变量跟踪性能更佳的准比例谐振控制器,并重点分析了比例增益、谐振增益、单元数量N变化以及主电路和控制电路中其他参数变化情况下的分岔、混沌行为特性;对比分析了引入混沌抑制前后系统稳定性变化情况。从方法论角度分别采用根轨迹图和折叠图验证了稳定性分析结果的正确性,并通过PSIM仿真和实验验证了相关结论的正确性和抑制分岔与混沌行为的有效性。

基于三维超混沌映射的图像加密及其FPGA实现

随着信息技术的不断发展,确保信息安全已成为重要问题,而图像作为被广泛使用的多媒体工具,时常面临着泄露的风险,针对该问题提出一种基于三维超混沌映射的图像加密算法。该算法首先构造一个离散三维超混沌系统,此系统相较于传统的混沌系统拥有更好的混沌特性,所生成的序列具有更强的随机性。然后在此序列的基础上对图片进行置乱和扩散:在置乱阶段,通过混沌序列对原始图片的像素点进行索引排序,实现像素置乱;在扩散阶段,使用处理后的混沌序列对置乱图像的像素值进行分层异或,实现像素扩散。软件仿真和性能分析表明,该算法具有较高的安全性和较强的抗干扰能力,最后通过FPGA硬件平台完成图像加密。整个算法包括软件仿真和硬件实现两个部分,能够有效提升学生将理论和实践相结合的能力,增强学生对非线性理论的理解。

基于扰动时空混沌的三维OFDM星座加密方案

无线通信网络的迅猛发展使得传输信息量迅速增加,这对系统的传输效率和通信安全提出了更高的要求。为满足上述要求,提出了一种基于扰动时空混沌的三维OFDM星座加密方案。首先,设计了一种反馈元胞自动机,以提高元胞自动机的周期性和伪随机性。将反馈元细胞自动机的迭代结果进行归一化后,作为扰动加入时空混沌系统中。通过分岔图、李亚普诺夫指数、回归映射分析和随机性测试,证明了添加该扰动的系统具有良好的混沌特性。其次,设计了一种新的三维16-ary星座图,将星座点之间的最小欧氏距离扩大了6.3%,并结合扰动的时空混沌系统实现了三维星座旋转的物理层调制加密。实验仿真结果表明,与其他三维16-ary星座映射旋转加密算法相比,该算法的误码率性能提高了约1 dB。此外,通过密钥空间、密钥灵敏度和统计攻击的安全分析,证明了其良好的安全性能。

基于混沌控制的采摘机器人防碰撞系统设计

针对采摘机器人在作业过程中遇到障碍物导致路径规划出现混沌现象的问题,基于混沌控制对采摘机器人防碰撞系统进行设计,并采用模糊自适应控制算法和滑模变结构控制算法结构的方式对系统进行控制。为了验证防碰撞系统的有效性,对采摘机器人进行了防碰撞和采摘仿真试验,结果表明:采摘机器人在作业过程中可以避开障碍物,按照规划路线到达预设位置,完成果蔬的采摘。

时滞SMIB混沌系统的同步及其在通信干扰对消中的应用

在实施通信干扰时,为了消除干扰信号对己方通信的影响,研究了干扰信号对消问题。考虑到混沌的类随机性使得混沌信号可以代替高斯噪声作为通信干扰源达到较好的干扰效果,以时滞SMIB混沌系统为干扰源,证明了存在外干扰时,参数失配的两个时滞SMIB混沌系统在线性状态误差反馈控制下达到有误差界的同步的判据,给出了误差界的估计,并进行了仿真验证。结果表明:利用该研究结论,可以在通信接收端对混沌干扰信号进行对消,基本消除混沌干扰对自身的影响。