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针对一类有界不确定线性离散被控对象,采用Min—Max优化方法,提出一种新的稳定广义预测控制(MMSGPC)算法.引入内模控制结构,将干扰和不确定性从被控对象中分离出来,并利用局部反环节对其进行补偿;采用Min—Max优化方法,将终端约束条件转化为有界不确定性最差情况对应的线性方程;通过引入矩阵的Moore—Penrose逆,得到了终端约束线性方程的通解,并结合性能指标函数求得了最优控制律。通过仿真实例验证了该方法的稳定效果。 相似文献
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含有界扰动系统的多模型自适应控制 总被引:7,自引:1,他引:7
对含有有界扰动和参数不确定性的离散时间被控对象建立多个辨识模型, 覆盖被控对象的参数不确定性. 给定指标切换函数, 构成多模型自适应控制器. 引入“局部化”技术, 在保持计算精度的同时, 提高了计算速度. 同时证明, 多模型自适应控制可以保证闭环系统输入输出稳定, 且保证对给定有界参考输入、被控对象输出可在一给定界范围内跟踪参考输入. 相似文献
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研究具有外部不确定性R¨ossler 混沌系统的鲁棒跟踪控制问题. 基于动态面控制原理设计自适应鲁棒控制器, 给出了系统参数的自适应更新律, 使得被控闭环系统的各误差变量一致有界. 系统输出曲线渐近跟踪任意期望轨道, 且跟踪误差能被控制在任意小的范围内, 而无须知道系统的参数及外部不确定性的界限. 基于稳定理论给出了具体的稳定性分析, 并通过数值仿真验证了该方法的有效性及鲁棒性.
相似文献5.
针对一类多输入多输出非线性被控对象,利用前向神经网络逼近原系统的逆系统,将其作为控制器,采用预测滚动优化性能指标训练该神经网络逆控制器,以克服干扰和不确定性影响,实现对多变量非线性对象的解耦控制。对某微型锅炉对象进行了控制算法仿真,结果表明,所提出的控制方法能够克服模型误差的影响,实现稳定解耦控制,且易于实现。在仿真过程中通过实验方法建立该锅炉对象的神经网络预测模型,并注意采用泛化方法采集训练样本数据和训练神经网络,以提高神经网络模型的泛化能力。 相似文献
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针对一类包含扰动的非线性离散时间系统,提出一种新的无模型自适应离散积分终端滑模控制算法.该算法基于紧格式动态线性化数据模型,利用离散积分终端滑模控制算法设计无模型自适应控制器,并采用扰动估计技术估计系统的扰动项,其中动态线性化方法中“伪偏导数”的估计算法仅依赖于被控系统的I/O测量数据.理论分析证明了系统输入输出有界,并通过仿真实验验证了所提算法的有效性. 相似文献
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针对一类有约束的稳定广义预测控制问题,提出一种基于状态空间的稳定广义预测控制算法。首先通过传递函数的状态空间实现,得到被控对象的离散状态空间形式;然后引入Deadbeat状态反馈矩阵并给出约束条件的等价性定理,实现了约束条件的等价转化;最后通过等价约束条件优化性能指标函数求解控制律。仿真实例表明该方法具有良好的稳定性。 相似文献
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以不确定Lurie系统作为被控对象,研究其在网络环境下的保性能控制问题.在同时考虑随机网络诱导时延和数据丢包的情况下,建立不确定Lurie网络化控制系统模型;利用Lyapunov方法分别给出了存在结构不确定性和范数有界的不确定性时,Lurie网络化控制系统保性能控制器的设计方法.所得结果是以线性矩阵不等式的形式给出的,便于数值求解.最后以数值实例说明了所提出方法的可行性和有效性. 相似文献
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针对一类满足扇形界条件的不确定模糊模型描述的非线性系统,提出一种输出反馈鲁棒预测控制方法.该方法将鲁棒预测控制的Min-Max优化问题转化为具有LMI约束的线性目标最小化问题,并且不需系统状态完全可测,仅仅利用系统测量输出和不可测状态的界限值来确定保证闭环系统鲁棒稳定的输出反馈控制器.仿真实验证明了该方法的有效性. 相似文献
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This paper firstly presents necessary and sufficient conditions for the solvability of discrete time, mean-field, stochastic linear-quadratic optimal control problems. Secondly, the optimal control within a class of linear feedback controls is investigated using a matrix dynamical optimization method. Thirdly, by introducing several sequences of bounded linear operators, the problem is formulated as an operator stochastic linear-quadratic optimal control problem. By the kernel-range decomposition representation of the expectation operator and its pseudo-inverse, the optimal control is derived using solutions to two algebraic Riccati difference equations. Finally, by completing the square, the two Riccati equations and the optimal control are also obtained. 相似文献
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针对多操纵面级联飞行控制结构中执行器存在多面体不确定的问题, 提出了一种基于鲁棒预测控制理论的动态控制分配策略. 考虑位置约束和速率约束, 建立了多面体不确定冗余执行器的增广控制模型; 以执行器状态和虚拟指令跟踪误差为增广变量构造二次型李亚普诺夫函数, 将无穷时域Min-Max非线性规划转化为线性矩阵不等式凸优化问题, 设计了保守性小的鲁棒预测控制律. 各个控制指令汇集到一个混合优化控制分配器, 由它分派控制指令, 以最优地补偿执行器的不确定动态特性. 仿真结果表明, 该策略可综合补偿执行器的多面体不确定性, 在操纵面偏转范围内精确地跟踪虚拟指令, 保证了闭环系统的稳定性, 具有较好的鲁棒性. 相似文献
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离散线性时滞系统的次优控制:逐次逼近法 总被引:9,自引:1,他引:9
A successive approximation approach for designing optimal controllers is presented for discrete linear time-delay systems with a quadratic performance index. By using the successive approximation approach, the original optimal control problem is transformed into a sequence of nonhomogeneous linear two-point boundary value (TPBV) problems without time-delay and time-advance terms. The optimal control law obtained consists of an accurate feedback terms and a time-delay compensation term which is the limit of the solution sequence of the adjoint equations. By using a finite-step iteration of the time-delay compensation term of the optimal solution sequence, a suboptimal control law is obtained. Simulation examples are employed to test the validity of the proposed approach. 相似文献
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TANG Gong-You WANG Hai-Hong 《自动化学报》2005,(3)
A successive approximation approach for designing optimal controllers is presented for discrete linear time-delay systems with a quadratic performance index.By using the successive approximation approach,the original optimal,control problem is transformed into a sequence of nonhomogeneous linear two-point boundary value (TPBV) problems without time-delay and time- advance terms.The optimal control law obtained consists of an accurate feedback terms and a time-delay compensation term which is the limit of the solution sequence of the adjoint equations. By using a finite-step iteration of the time-delay compensation term of the optimal solution sequence, a suboptimal control law is obtained.Simulation examples are employed to test the validity of the proposed approach. 相似文献
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The optimization of the time-invariant bilinear weakly coupled system with a quadratic performance criterion is considered. A sequence of linear state and costate equations is constructed such that the open-loop solution of the optimization problem is obtained in terms of the reduced-order subsystems. This leads to a reduction in the size of the required computations and allows parallel processing of information. The near-optimal closed-loop control is obtained in the form of a linear feedback law, with the feedback gains calculated from two reduced-order independent time-varying linear-quadratic optimal control problems. 相似文献
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O. L. V. Costa R. P. Marques 《Mathematics of Control, Signals, and Systems (MCSS)》1999,12(2):167-195
Discrete-time coupled algebraic Riccati equations that arise in quadratic optimal control and H
∞-control of Markovian jump linear systems are considered. First, the equations that arise from the quadratic optimal control
problem are studied. The matrix cost is only assumed to be hermitian. Conditions for the existence of the maximal hermitian
solution are derived in terms of the concept of mean square stabilizability and a convex set not being empty. A connection
with convex optimization is established, leading to a numerical algorithm. A necessary and sufficient condition for the existence
of a stabilizing solution (in the mean square sense) is derived. Sufficient conditions in terms of the usual observability
and detectability tests for linear systems are also obtained. Finally, the coupled algebraic Riccati equations that arise
from the H
∞-control of discrete-time Markovian jump linear systems are analyzed. An algorithm for deriving a stabilizing solution, if
it exists, is obtained. These results generalize and unify several previous ones presented in the literature of discrete-time
coupled Riccati equations of Markovian jump linear systems.
Date received: November 14, 1996. Date revised: January 12, 1999. 相似文献
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The linear model predictive control which is frequently used for building climate control benefits from the fact that the resulting optimization task is convex (thus easily and quickly solvable). On the other hand, the nonlinear model predictive control enables the use of a more detailed nonlinear model and it takes advantage of the fact that it addresses the optimization task more directly, however, it requires a more computationally complex algorithm for solving the non-convex optimization problem. In this paper, the gap between the linear and the nonlinear one is bridged by introducing a predictive controller with linear time-dependent model. Making use of linear time-dependent model of the building, the newly proposed controller obtains predictions which are closer to reality than those of linear time invariant model, however, the computational complexity is still kept low since the optimization task remains convex. The concept of linear time-dependent predictive controller is verified on a set of numerical experiments performed using a high fidelity model created in a building simulation environment and compared to the previously mentioned alternatives. Furthermore, the model for the nonlinear variant is identified using an adaptation of the existing model predictive control relevant identification method and the optimization algorithm for the nonlinear predictive controller is adapted such that it can handle also restrictions on discrete-valued nature of the manipulated variables. The presented comparisons show that the current adaptations lead to more efficient building climate control. 相似文献
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In this paper, a linear programming method is proposed to solve
model predictive control for a class of hybrid systems. Firstly,
using the (max, +) algebra, a typical subclass of hybrid systems
called max-plus-linear (MPL) systems is obtained. And then, model
predictive control (MPC) framework is extended to MPL systems. In
general, the nonlinear optimization approach or extended linear
complementarity problem (ELCP) were applied to solve the MPL-MPC
optimization problem. A new optimization method based on canonical
forms for max-min-plus-scaling (MMPS) functions (using the
operations maximization, minimization, addition and scalar
multiplication) with linear constraints on the inputs is presented.
The proposed approach consists in solving several linear programming
problems and is more efficient than nonlinear optimization. The
validity of the algorithm is illustrated by an example. 相似文献