Mimo Nonlinear(Mimo非线性)研究综述
Mimo Nonlinear Mimo非线性 - This paper adduces the idea of linearization based on piecewise polynomial or essential spline functions as an integral solution to the MIMO nonlinearity and compares with the Crossover Memory polynomial model. [1]本文引入基于分段多项式或本质样条函数的线性化思想作为MIMO非线性的积分解，并与交叉记忆多项式模型进行了比较。 [1]
Order Mimo Nonlinear
This article proposed a new fixed-time output tracking control scheme for a class of high-order MIMO nonlinear systems with unknown nonlinearities, parameter uncertainties and external disturbances. [1] An event-triggered modular neural network controller is designed for containment maneuvering of second-order MIMO nonlinear multi-agent systems under an undirected graph. [2] A new problem of observer-based fractional adaptive type-2 fuzzy backstepping control for a class of fractional-order MIMO nonlinear dynamic systems with dead-zone input nonlinearity is considered in the presence of model uncertainties and external disturbances where the control scheme is constructed by combining the backstepping dynamic surface control (DSC) and fractional adaptive type-2 fuzzy technique. [3] This paper investigates the characteristic modeling problem of a class of high-order MIMO nonlinear dynamical systems with zero dynamics and subjected to strong nonlinearities. [4]针对一类非线性、参数不确定和外部扰动未知的高阶MIMO非线性系统，本文提出了一种新的固定时间输出跟踪控制方案。 [1] 一种事件触发的模块化神经网络控制器被设计用于在无向图下对二阶 MIMO 非线性多智能体系统进行遏制操纵。 [2] 考虑存在模型不确定性和外部扰动的一类具有死区输入非线性的分数阶 MIMO 非线性动态系统的基于观测器的分数自适应 2 型模糊反推控制新问题，控制方案为结合了反推动态表面控制（DSC）和分数自适应2型模糊技术。 [3] 本文研究了一类具有零动力学和强非线性的高阶MIMO非线性动力系统的特征建模问题。 [4]
Different Mimo Nonlinear 不同的 Mimo 非线性
The flexibility of the division method to convert k-input MIMO system to SISOs system combined with the optimal algorithm creates a powerful tool that can be applied to many different MIMO nonlinear systems with high success rates. [1] The proposed SO-DFL-BELC is applied to control two different MIMO nonlinear systems that are a 4D chaotic system and a four-tank system. [2]将 k 输入 MIMO 系统转换为 SISO 系统的划分方法的灵活性与最优算法相结合，创造了一个强大的工具，可以应用于许多不同的 MIMO 非线性系统，成功率很高。 [1] 所提出的 SO-DFL-BELC 用于控制两个不同的 MIMO 非线性系统，即 4D 混沌系统和四槽系统。 [2]
mimo nonlinear system Mimo非线性系统
Originality/valueThe uncertain MIMO nonlinear system described by Type-2 Takagi-Sugeno (T-S) fuzzy model, and successively LMI approach used to determine the system stability conditions. [1] This paper first gives a review on recent advance in semiglobal asymptotic stabilization (SGAS) of MIMO nonlinear systems by sampled-data feedback and discusses their limitations. [2] It is shown that this control problem can be converted into a global robust stabilization problem of a more complicated MIMO nonlinear system with various uncertainties and well solved by a recursive state feedback controller design. [3] In this study, we propose an adaptive tracking dynamic surface back-stepping control based on Nussbaum disturbance observer for uncertain high-order strict-feedback MIMO nonlinear systems with external disturbances, unknown parameters and modelling uncertainties. [4] A general stability of the closed-loop disturbed MIMO nonlinear system is achieved by the Lyapunov theorem. [5] This article proposed a new fixed-time output tracking control scheme for a class of high-order MIMO nonlinear systems with unknown nonlinearities, parameter uncertainties and external disturbances. [6] Also, the nonlinear functions in the MIMO nonlinear systems are not required to follow the linearly parameterization or growth conditions making the control design more generally available. [7] This paper investigates the time-varying output constraints tracking problem for a class of MIMO nonlinear system, where a new design of Iterative Learning Control (ILC) with adaptive sliding mode method is implemented. [8] Acquired results illustrate thatintroduced controller has substantially good performance on MIMO nonlinear systems. [9] A uniform quantizer is adopted to quantize state variables and control inputs of MIMO nonlinear systems. [10] The flexibility of the division method to convert k-input MIMO system to SISOs system combined with the optimal algorithm creates a powerful tool that can be applied to many different MIMO nonlinear systems with high success rates. [11] The proposed SO-DFL-BELC is applied to control two different MIMO nonlinear systems that are a 4D chaotic system and a four-tank system. [12] In this paper, we propose a low-cost and effective neuroadaptive PI control for MIMO nonlinear systems with actuation failures as well as unknown control direction. [13] The MIMO nonlinear systems are approximated by MIMO ARX-Laguerre multiple models. [14] We show that under the proposed novel control scheme, each element in the system output tracking error vector of the MIMO nonlinear system can converge into small sets near zero with fixed-time convergence rate, while the asymmetric output constraint requirements on each element of the output tracking error are satisfied at all time. [15] After compared with other models, the test results show that the proposed model can be applied to obtain more satisfactory control performance and be more suitable to deal with the influence of the uncertainty of the MIMO nonlinear systems. [16] Another advantage of the proposed controller and unlike other works on ILC, we do not need any prior knowledge of the control directions for MIMO nonlinear system. [17] Then, the following discrete-time MIMO nonlinear system is given by. [18] Therefore, this paper proposes an adaptive passive fault tolerant control method for actuator faults of affine class of MIMO nonlinear systems with uncertainties using sliding mode control. [19] A robust direct adaptive fuzzy controller for a class of MIMO nonlinear systems with uncertainties and external disturbances is presented. [20] In this paper, based on adaptive non-backstepping design algorithm, we proposed a novel variable universe fuzzy control (VUFC) algorithm for a class of MIMO nonlinear systems with unknown dead-zone inputs in pure-feedback form. [21]独创性/价值用Type-2 Takagi-Sugeno（T-S）模糊模型描述的不确定MIMO非线性系统，并先后采用LMI方法确定系统的稳定性条件。 [1] 本文首先回顾了基于采样数据反馈的 MIMO 非线性系统的半全局渐近稳定 (SGAS) 的最新进展，并讨论了它们的局限性。 [2] 结果表明，该控制问题可以转化为具有各种不确定性的更复杂的MIMO非线性系统的全局鲁棒稳定问题，并通过递归状态反馈控制器设计得到很好的解决。 [3] 在这项研究中，我们提出了一种基于 Nussbaum 扰动观测器的自适应跟踪动态表面反步控制，用于具有外部扰动、未知参数和建模不确定性的不确定高阶严格反馈 MIMO 非线性系统。 [4] Lyapunov定理实现了闭环受扰MIMO非线性系统的一般稳定性。 [5] 针对一类非线性、参数不确定和外部扰动未知的高阶MIMO非线性系统，本文提出了一种新的固定时间输出跟踪控制方案。 [6] 此外，MIMO 非线性系统中的非线性函数不需要遵循线性参数化或增长条件，从而使控制设计更普遍可用。 [7] 本文研究了一类多输入多输出非线性系统的时变输出约束跟踪问题，其中采用自适应滑模方法实现了一种新的迭代学习控制（ILC）设计。 [8] 获得的结果表明，引入的控制器在 MIMO 非线性系统上具有良好的性能。 [9] 采用均匀量化器对MIMO非线性系统的状态变量和控制输入进行量化。 [10] 将 k 输入 MIMO 系统转换为 SISO 系统的划分方法的灵活性与最优算法相结合，创造了一个强大的工具，可以应用于许多不同的 MIMO 非线性系统，成功率很高。 [11] 所提出的 SO-DFL-BELC 用于控制两个不同的 MIMO 非线性系统，即 4D 混沌系统和四槽系统。 [12] 在本文中，我们针对具有驱动失败和未知控制方向的 MIMO 非线性系统提出了一种低成本且有效的神经自适应 PI 控制。 [13] MIMO非线性系统由MIMO ARX-Laguerre多重模型逼近。 [14] 我们表明，在所提出的新控制方案下，MIMO非线性系统的系统输出跟踪误差向量中的每个元素都可以收敛到接近零的小集合，具有固定的时间收敛速度，而对输出的每个元素的不对称输出约束要求始终满足跟踪误差。 [15] 经过与其他模型的比较，测试结果表明，该模型可以得到更满意的控制性能，更适合处理MIMO非线性系统不确定性的影响。 [16] 所提出的控制器的另一个优点，与 ILC 上的其他工作不同，我们不需要任何关于 MIMO 非线性系统的控制方向的先验知识。 [17] 那么，下面的离散时间 MIMO 非线性系统由下式给出。 [18] 因此，本文针对具有不确定性的仿射类MIMO非线性系统的执行器故障，提出了一种采用滑模控制的自适应被动容错控制方法。 [19] 针对一类具有不确定性和外部干扰的MIMO非线性系统，提出了一种鲁棒的直接自适应模糊控制器。 [20] 本文基于自适应非反推设计算法，针对一类具有未知死区输入的MIMO非线性系统，以纯反馈形式提出了一种新颖的变域模糊控制（VUFC）算法。 [21]
mimo nonlinear model
The design of the MRAC requires the linear state–space representation of the MIMO nonlinear model of the robot arm. [1] The design of the MRAC requires the linear state–space representation of the MIMO nonlinear model of the robot arm. [2]MRAC 的设计需要机械臂的 MIMO 非线性模型的线性状态空间表示。 [1] MRAC 的设计需要机械臂的 MIMO 非线性模型的线性状态空间表示。 [2]
mimo nonlinear dynamic
The proposed controller (GRNNSMC) performance is verified with a generic MIMO nonlinear dynamic system and a hexacopter model with a variable center of gravity. [1] A new problem of observer-based fractional adaptive type-2 fuzzy backstepping control for a class of fractional-order MIMO nonlinear dynamic systems with dead-zone input nonlinearity is considered in the presence of model uncertainties and external disturbances where the control scheme is constructed by combining the backstepping dynamic surface control (DSC) and fractional adaptive type-2 fuzzy technique. [2]所提出的控制器 (GRNNSMC) 性能通过通用 MIMO 非线性动态系统和具有可变重心的六轴飞行器模型进行了验证。 [1] 考虑存在模型不确定性和外部扰动的一类具有死区输入非线性的分数阶 MIMO 非线性动态系统的基于观测器的分数自适应 2 型模糊反推控制新问题，控制方案为结合了反推动态表面控制（DSC）和分数自适应2型模糊技术。 [2]
mimo nonlinear predictive
Experimental results demonstrate that the MIMO nonlinear predictive controller performs better than the MIMO PID controller because employing the former controller, the settling time, the percent overshoot, as well as the steady–state error of the controlled outputs decrease. [1] Experimental results demonstrate that the MIMO nonlinear predictive controller performs better than the MIMO PID controller because employing the former controller, the settling time, the percent overshoot, as well as the steady–state error of the controlled outputs decrease. [2]实验结果表明，MIMO 非线性预测控制器的性能优于 MIMO PID 控制器，因为采用前一种控制器，稳定时间、超调百分比以及受控输出的稳态误差减小。 [1] 实验结果表明，MIMO 非线性预测控制器的性能优于 MIMO PID 控制器，因为采用前一种控制器，稳定时间、超调百分比以及受控输出的稳态误差减小。 [2]
mimo nonlinear dynamical
The capabilities of the toolbox and the modelling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models. [1] This paper investigates the characteristic modeling problem of a class of high-order MIMO nonlinear dynamical systems with zero dynamics and subjected to strong nonlinearities. [2]工具箱和建模方法的功能在两个 SISO 和一个 MIMO 非线性动态基准模型的识别中得到了证明。 [1] 本文研究了一类具有零动力学和强非线性的高阶MIMO非线性动力系统的特征建模问题。 [2]