共查询到19条相似文献，搜索用时 437 毫秒
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动态系统的状态约束和控制约束等问题可归结为状态空间中某些集合的正不变性,它反映了系统族内部状态的具体性质,在研究轨线界限,过渡过程等方面具有独到的意义和作用.本文利用混合单调分解方法来研究离散时滞非线性凸多面体系统族的线性状态约束集合的鲁棒正不变性.对由矩阵凸多面体和区间扰动所描述的系统族,得到了鲁棒正不变集的充分条件,并给出了证明. 相似文献
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动态系统的状态约束和控制约束等问题可归结为状态空间中某些集合的正不变性,它反映了系统族内部状态的具体性质,在研究轨线界限,过渡过程等方面具有独到的意义和作用。本文利用混合单调分解方法来研究离散时滞非线性凸多面体系统族的线性状态约束集合的鲁棒正不变性。对由矩阵凸多面体和区间扰动所描述的系统族,得到了鲁棒正不变集的充分条件,并给出了证明。 相似文献
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《科技通报》2017,(2)
针对标准Lyapunov函数在工业复杂系统预测控制的应用中还存在控制约束力较差等问题,本文提出了一种基于非线性反馈控制优化的Lyapunov函数模型。首先采用GB公式引入Lyapunov函数,通过其变分方程来构造Lyapunov函数,同时可以将线性方程化为不同的等价系统,然后采用连续映射对原函数的反馈控制能力进行优化,将其转换为线性赋范空间,扩大非线性仿射控制系统的镇定问题,控制Ly Paunov函数的寻找范围,最后将改进算法对某工业复杂系统进行预测控制的实例仿真。仿真实验结果表明,本文提出的基于非线性反馈控制优化的Lyapunov函数具有更好的约束性,能很好地对复杂工业系统进行预测控制。 相似文献
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Ｈａｍｍｅｒｓｔｅｉｎ模型基于神经网络的预测控制方法 总被引：1，自引：0，他引：1
Ｈａｍｍｅｒｓｔｅｉｎ模型是化工过程中最常用的模型之一,它由非线性静态环节和线性动态环节串连 组成,适合描述ｐＨ过程和具有幂函数、死区、开关等非线性特性的过程．这类模型的控制问题可以分解 为：线性模型的控制问题和非线性模型的求根问题．针对Ｈａｍｍｅｒｓｔｅｉｎ模型提出了一种基于神经网络的 模型预测控制策略,采用一组神经网络拟合非线性部分的逆映射．这种方法不需要假设Ｈａｍｍｅｒｓｔｅｉｎ模 型的非线性部分由多项式构成,并且避免已有研究在无根和重根情况下存在的问题．最后通过仿真试验证明了以上结论． 相似文献
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针对一类具有时域硬约束的离散不确定非线性系统,结合预测控制的滚动优化原理提出了一种H∞预测控制方法.首先,采用T-S模糊模型来逼近实际非线性系统,运用平行分布补偿(PDC)原理将该系统转化为一系列线性系统的凸组合.其次,基于线性矩阵不等式(LMI)的方法优化一个同时考虑系统控制要求和H∞干扰抑制能力的多目标性能指标函数... 相似文献
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贾维宏 《科技成果管理与研究》2012,(3):54-57
提出一种针对一类可分非线性系统的广义预测控制算法。首先利用对角回归型神经网络（Diagonal Recurrent Neura Network,简称DRNN）逼近非线性子系统,线性子系统的模型采用受控自回归积分滑动平均模型（CARIMA）,从而建立了一种适合于非线性系统的广义预测模型。在该算法中引入柔化系数矩阵,避免矩阵求逆的计算,减少了在线计算量。仿真结果表明,该广义预测控制算法具有响应速度快、控制效果好的特点。 相似文献
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内河航道交通流预测是保证航道通畅的重要理论根据,传统的航道交通流预测采用线性单态神经网络预测算法,当航运船舶快速聚簇,位置和速度信息参量呈非线性变化时,预测精度不好。提出一种基于船舶自组织网络态势分析的内河航道交通流聚簇预测方法,构建内河航道交通态势分析信息感知模型,通过信息感知和通信定位实现聚簇信息交互,采用非线性卡尔曼方程构建船舶态势分析状态方程,得到航行拥堵程度记忆因子,逐步舍弃内河航道内的交通流历史测量信息,判断航道的拥堵程度,实现对船舶自组织态势分析和流量预测。采用Netlogo设置仿真场景进行仿真实验,实验结果得出采用该算法船舶信息检测和流量预测精度提高显著,预测精度高。 相似文献
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分析了汽车电子节气门控制器的仿真设计方法。电子节气门控制器由参考输入信号整形系统与PID控制器组成,辅以节气门非线性化控制。由于采用了消除摩擦影响的控制方法,使非线性最小化。故可利用线性系统分析技术推导出节气门体在六种工作条件下的传递函数,并对传递函数的动态特性进行频域分析。文采用频域响应技术、Matlab仿真等方法调整节气门控制器。 相似文献
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开关磁阻电动机的非线性模型预测控制 总被引：3，自引：0，他引：3
将非线性预测控制应用于ＳＲＭ传动系统,建立了非线性参数预测模型,并在此基础上优化、校正,设计了ＳＲＭ的非线性速度预测控制器。该控制器具有实时预测、实时优化、实时校正的特点,很好地补偿了ＳＲＭ的非线性特性。与传统ＰＩ控制器相比,非线性模型预测控制器能提供更好的动态、静态特性,转矩脉动大为减小。（仿真结果表明,该控制策略不但正确、有效,而且使ＳＲＭ传动系统性能得到改善。 相似文献
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《Journal of The Franklin Institute》2023,360(10):6953-6975
The main results of this paper are concentrated on the nonlinear model predictive control (MPC) tracking optimization based on high-order fully actuated (HOFA) system approaches. The proposed HOFA MPC strategy makes full use of full-actuation property to eliminate the nonlinear dynamics of the system, and then the nonlinear optimization problem is equivalently transformed into a series of easy-solve linear convex optimization problems. Different from general nonlinear MPC methods and the current optimal control of the HOFA system approach, an analytical controller with smooth and less energy is obtained by the moving horizon optimization. And it is proven that the proposed controller can stabilize the corresponding tracking error closed-loop system. Finally, not limited to FA systems, as examples, a nonlinear numerical under-actuated model in the mathematical sense and a benchmark nonlinear under-actuated mechanical system are transformed into corresponding equivalent HOFA systems, the simulation results are given to verify the effectiveness of the proposed strategy. 相似文献
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Yan Song Kaiqun Zhu Guoliang Wei Jianhua Wang 《Journal of The Franklin Institute》2019,356(5):2606-2624
This paper is concerned with the adaptive control problem for a class of linear discrete-time systems with unknown parameters based on the distributed model predictive control (MPC) method. Instead of using the system state, the state estimate is employed to model the distributed state estimation system. In this way, the system state does not have to be measurable. Furthermore, in order to improve the system performance, both the output error and its estimation are considered. Moreover, a novel Lyapunov functional, comprised of a series of distributed traces of estimation errors and their transposes, has been presented. Then, sufficient conditions are obtained to guarantee the exponential ultimate boundedness of the system as well as the asymptotic stability of the error system by solving a nonlinear programming (NP) problem subject to input constraints. Finally, the simulation examples is given to illustrate the effectiveness and the validity of the proposed technique. 相似文献
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In this paper, a stable model predictive control approach is proposed for constrained highly nonlinear systems. The technique is a modification of the multistep Newton-type control strategy, which was introduced by Li and Biegler. The proposed control technique is applied on a constrained highly nonlinear aerodynamic test bed, the twin rotor MIMO system (TRMS) to show the efficacy of the control technique. Since the accuracy of the plant model is vital in MPC techniques, the nonlinear state space equations of the system are derived considering all possible effective components. The nonlinear model is adaptively linearized during the prediction horizon. The linearized models of the system are employed to form a linear quadratic objective function subject to a set of inequality constraints due to the system input/output limits. The stability of the control system is guaranteed using the terminal equality constraints technique. The satisfactory performance of the proposed control algorithm on the TRMS validates the effectiveness and the reliability of the approach. 相似文献
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《Journal of The Franklin Institute》2023,360(10):6906-6927
A novel robust hierarchical multi-loop composite control scheme is proposed for the trajectory tracking control of robotic manipulators subject to constraints and disturbances. The inner loop based on inverse dynamics control is used to reduce the nonlinear tracking error system to a set of decoupled linear subsystems to alleviate the computational effort during the sequel optimization. The feasible regions of the equivalent state and control input of each subsystem can be computed efficiently by choosing an appropriate inertia matrix estimate. The external loop, relying on a set of separate disturbance-observer-based tube model predictive composite controllers, is used to robustly stabilize the decoupled subsystems. In particular, the disturbance observers are designed to compensate for the disturbances actively, while the tube model predictive controllers are used to reject the residual disturbances. The robust tightened constraints are obtained by calculating the outer-bounding-tube-type residual disturbance invariant sets of the closed-loop subsystems. Furthermore, the recursive feasibility and input-to-state stability of the closed-loop system are investigated. The effectiveness of the proposed control scheme is verified by the simulation experiment on a PUMA 560 robotic manipulator. 相似文献
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为了解决目前常用的非线性预测模型算法中过学习、存在局部极小值等不易解决的问题，本文提出一种基于最小二乘支持向量机对非线性过程建模并用广义预测控制进行在线滚动控制的算法。仿真结果表明，该控制算法具有很好的控制性能。 相似文献
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Mifeng Ren Junghui Chen Peng Shi Gaowei Yan Lan Cheng 《Journal of The Franklin Institute》2021,358(4):2279-2300
In this paper, a two-layer model predictive control (MPC) hierarchical architecture of dynamic economic optimization (DEO) and reference tracking (RT) is proposed for non-Gaussian stochastic process in the framework of statistical information. In the upper layer, with state feedback and dynamic economic information, the economically optimal trajectories are estimated by entropy and mean based dynamic economic MPC, which uses the nonlinear dynamic model instead of the steady-state model. These estimated optimal trajectories from the upper layer are then employed as the reference trajectories of the lower layer control system. A survival information potential based MPC algorithm is used to maintain the controlled variables at their reference trajectories in the nonlinear system with non-Gaussian disturbances. The stability condition of closed-loop system dynamics is proved using the statistical linearization method. Finally, a numerical example and a continuous stirred-tank reactor are used to illustrate the merits of the proposed economic optimization and control method. 相似文献
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In this paper, a method is proposed to reject disturbances in the model predictive control (MPC) strategy. In addition, uncertainties in the system parameters (i.e., internal disturbances) are considered as well. To achieve these goals, adaptive neural networks are designed as the predictor model and as the nonlinear disturbance observer, respectively. The disturbances are rejected via the optimization problem of the MPC. Stability of the closed-loop system is studied based on the Input-to-State Stability method. The proposed method is applied to the pH neutralization process and CSTR system and its effectiveness in optimal rejection of the disturbances and satisfying the system constrains is compared with the feed-forward control method. 相似文献
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《Journal of The Franklin Institute》2022,359(2):1176-1193
The paper describes a novel method of sampled-data in space (spatial variable) nonlinear control of scalar semilinear parabolic and hyperbolic systems with unknown parameters, distributed disturbances and finite number of measurements along the spatial variable. Differently from recent results based on piecewise constant control laws, the proposed one is used piecewise nonlinear functions choosing by designer for providing some properties in the closed-loop system. In particular, we propose several types of functions providing reduced control. The gain design in the control law is found as a solution of linear matrix inequalities with minimum ultimate bound guarantee. The simulations confirm theoretical results and show the efficiency of the proposed control scheme compared with some existing ones. 相似文献