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1.
A two-step iterative method (1,2) for a reduction in the order of linear continuous-time systems, given in the state equation or the transfer function, is extended to reduce discrete-time systems. The method requires the optimization of the residues and eigenvalues (or poles) belonging to an objective function. The objective function to be minimized is chosen as the finite sum of the squares of the error between the step responses of the reduced model and the original system. This scheme is continued cyclically until the objective function is satisfactorily minimized. By investigating the initial selection of the eigenvalues in the reduced-order model, it is found that the dominant eigenvalues of the original system give a good approximation. Further, the resulting model is always stable, assuming the original system is stable. As shown in a numerical example, the proposed method is superior to the other methods of model reduction in both steady-state and transient responses, and in the value of the sum of the squares of the error.  相似文献   

2.
This brief communication establishes a two-step iterative algorithm based on the orthogonal projection for reducing order of the high-order system transfer function or state variable equations. A two-step iterative algorithm which has been developed by the authors (1) consists of the residue and pole (or eigenvalue) optimization with respect to the objective function. Here, the optimum residues in the first step can be determined by using the reciprocal basis in the projection theorem. The reciprocal basis allows one to avoid performing the Grammian inversion. Selecting the new basis, the optimum poles in the second step can be also applied for the orthogonal projection. Although the resulting reduced-order models derived from this geometrical point of view are consistent with models of a two-step iterative algorithm, the algorithm is thus a computationally much simpler way to derive the formula.  相似文献   

3.
The terminal iterative learning control is designed for nonlinear systems based on neural networks. A terminal output tracking error model is obtained by using a system input and output algebraic function as well as the differential mean value theorem. The radial basis function neural network is utilized to construct the input for the system. The weights are updated by optimizing an objective function and an auxiliary error is introduced to compensate the approximation error from the neural network. Both time-invariant input case and time-varying input case are discussed in the note. Strict convergence analysis of proposed algorithm is proved by the Lyapunov like method. Simulations based on train station control problem and batch reactor are provided to demonstrate the effectiveness of the proposed algorithms.  相似文献   

4.
In this paper, moment matching model reduction problem for negative imaginary systems is considered. For a given negative imaginary system with poles at the origin, our goal is to find a reduced-order negative imaginary system such that a prescribed number of the moments and the poles at the origin are preserved. Firstly, the original negative imaginary system is split into an asymptotically stable subsystem, a lossless negative imaginary subsystem and an average subsystem. Then, moment matching model reduction is implemented on the asymptotically stable subsystem and the lossless negative imaginary subsystem. The resulting reduced-order system preserves the negative imaginary structure and the poles at the origin. Also, the proposed model reduction method is extended to the positive real systems. Numerical examples demonstrate the effectiveness of the proposed model reduction method.  相似文献   

5.
This paper presents a novel iterative learning feedback control method for linear parabolic distributed parameter systems with multiple collocated piecewise observation. Multiple actuators and sensors distributed at the same position of the spatial domain are utilized to perform collocated piecewise control and measurement operations. The advantage of the proposed method is that it combines the iterative learning algorithm and feedback technique. Not only can it use the iterative learning algorithm to track the desired output trajectory, but also the feedback control approach can be utilized to achieve real-time online update. By utilizing integration by parts, triangle inequality, mean value theorem for integrals and Gronwall lemma, two sufficient conditions based on the inequality constraints for the convergence analysis of the tracking error system are presented. Some simulation experiments are provided to prove the effectiveness of the proposed method.  相似文献   

6.
In this paper, the optimal consensus control problem of nonlinear multi-agent systems(MASs) with completely unknown dynamics is considered. The problem is formulated in a differential graphical game approach which can be solved by Hamilton-Jacobi (HJ) equations. The main difficulty in solving the HJ equations lies in the nonlinear coupling between equations. Based on the Adaptive Dynamic Programming (ADP) technique, an VI-PI mixed HDP algorithm is proposed to solve the HJ equations distributedly. With the PI step, a suitable iterative initial value can be obtained according to the initial policies. Then, VI steps are run to get the optimal solution with exponential convergence rate. Neural networks (NNs) are applied to approximate the value functions, which makes the data-driven end-to-end learning possible. A numerical simulation is conducted to show the effectiveness of the proposed algorithm.  相似文献   

7.
Standard forms are presented which define transfer functions for optimum type 1 and type 2 feedback control systems, where systems with minimum integral of time multiplied absolute value of error in the presence of a step displacement input are considered to be optimum. The ITAE criterion adopted in this paper was previously introduced by Graham and Lathrop.The optimization procedure leading to the standard forms presented here, is based upon an all digital simulation coupled to an unconstrained optimization algorithm to minimize the ITAE criterion value.It is shown with the aid of examples how actual systems can be compensated by use of appropriate standard forms to obtain optimum responses.  相似文献   

8.
A new combined time and frequency domain method for the model reduction of discrete systems in z-transfer function is presented. First, the z-transfer functions are transformed into the w-domain by the bilinear transformation, z = (1+w)/(1?w). Then, four model reduction methods—Routh approximation, Hurwitz polynomial approxima- tion, stability equation, and retaining dominant poles—are used respectively to reduce the order of the denominator polynomials in the w-domain. Least squares estimate is then used to find the optimal coefficients in the numerator polynomials of the reduced models so that the unit step response errors are reduced to a minimum. The advantages of the proposed method are that both frequency domain and time domain characteristics of the original systems can be preserved in the reduced models, and the reduced models are always stable provided the original models are stable.  相似文献   

9.
环境因素下确定最优行驶速度的双层规划模型   总被引：2，自引：0，他引：2  
首先给出了考虑环境污染因素的双层规划城市交通配流模型,上层要求系统总的出行时间和CO排放总量最小,下层是一个弹性需求的UE问题;并设计了一个平衡迭代算法,对所给模型进行了求解,从而能得到车辆在各路段上的最优行驶速度;最后给出了两个简单的算例,对该模型及相应的求解算法进行了验证.  相似文献   

10.
In this work, a lifted event-triggered iterative learning control (lifted ETILC) is proposed aiming for addressing all the key issues of heterogeneous dynamics, switching topologies, limited resources, and model-dependence in the consensus of nonlinear multi-agent systems (MASs). First, we establish a linear data model for describing the I/O relationships of the heterogeneous nonlinear agents as a linear parametric form to make the non-affine structural MAS affine with respect to the control input. Both the heterogeneous dynamics and uncertainties of the agents are included in the parameters of the linear data model, which are then estimated through an iterative projection algorithm. On this basis, a lifted event-triggered learning consensus is proposed with an event-triggering condition derived through a Lyapunov function. In this work, no threshold condition but the event-triggering condition is used which plays a key role in guaranteeing both the stability and the iterative convergence of the proposed lifted ETILC. The proposed method can reduce the number of control actions significantly in batches while guaranteeing the iterative convergence of tracking error. Both rigorous analysis and simulations are provided and confirm the validity of the lifted ETILC.  相似文献   

11.
This work presents an iterative concept of the State-space Realization Algorithm with Data Correlation (SSRA-DC) to identify MIMO systems with measurement noise and subjected to a reduced number of samples acquired from the process. The measurement noise is characterized as a random signal with properties of white noise and having up to 1% of the output signal amplitude. The proposed technique is based on the Markov parameters matrix’s feedback in an iterative algorithm supported by the SSRA-DC method. A gain factor takes part in the closed-loop to update the Markov parameters matrix, reducing their residues at each iteration. A fixed value for the gain is applied all over the iterations. The Gaussian White Noise (GWN) is employed as the input excitation signal in simulated experiments of mass-damper-springer models with 50 and 100 degrees of freedom. For some algorithm settings, one hundred simulations, each holding more than 100 iterations, are performed to statistically demonstrate the iterative algorithm’s effectiveness compared to the conventional SSRA-DC. Further comparative analysis is accomplished between the iterative method with the ARMAX and N4SID algorithms.  相似文献   

12.
In this paper, the problem about the false data injection attacks on sensors to degrade the state estimation performance in cyber-physical systems(CPSs) is investigated. The attack strategies for unstable systems and stable ones are both designed. For unstable systems, based on the idea of zero dynamics, an unbounded attack strategy is proposed which can drive the state estimation error variations to infinity. The proposed method is more general than existing unbounded attack strategies since it relaxes the requirement for the initial value of the estimation error. For stable systems, it is difficult to bring unbounded impacts on the estimation error variations. Therefore, in this case, an attack strategy with adjustable attack performance which makes the estimation error variations track predesigned target values is proposed. Furthermore, a uniform attack strategy which aims to deteriorate state estimation for both stable systems and unstable ones is derived. Finally, simulations are provided to illustrate the effectiveness of the proposed attack strategies.  相似文献   

13.
In conventional PID-type iterative learning control (ILC) designs, to determine the learning control gains involved, relevant model knowledge on the controlled systems is often dependent. In this paper, two completely data-driven ILC laws, the extended PD-type ILC law and the extended P-type ILC law, are designed in frequency domain for linear discrete-time (LDT) single-input single-output (SISO) systems. The designs of the proposed ILC laws are based on the approximation/identification to unknown transfer function with a novel adaptive Fourier decomposition (AFD) technique. As a result, the strictly monotonic convergence of ILC tracking error is guaranteed in a deterministic way. A numerical example on a four-axis robot arm is performed to illustrate the effectiveness of the proposed data-driven ILC algorithms  相似文献   

14.
A simple numerical method for computing the time domain response of linear time invariant systems described by their transfer functions is presented. The method does not require computation of transfer function poles or residues; it is not influenced by the multiplicity of poles or zeroes, nor does it require computation of the matrix exponential. Rather, it is based on a numerical method for inverting Laplace transforms. It is equivalent to very high order, absolutely stable numerical integration. Stiff systems present no problems.  相似文献   

15.
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   

16.
In this paper, the problem of adaptive fuzzy fault-tolerant control is investigated for a class of switched uncertain pure-feedback nonlinear systems under arbitrary switching. The considered actuator failures are modeled as both lock-in-place and loss of effectiveness. By utilizing mean value theorem, the considered pure-feedback systems are transformed into a class of switched nonlinear strict-feedback systems. Under the framework of backstepping design technique and common Lyapunov function (CLF), an adaptive fuzzy fault-tolerant control (FTC) method with predefined performance bounds is developed. It is proved that under the proposed controller, all the signals of the close-loop systems are bounded and the state tracking error for each step remains within the prescribed performance bound (PPB) regardless of actuator faults and the system switchings. In addition, the tracking errors and magnitudes of control inputs can be reduced by adjusting the PPB parameters of errors in the first and last steps. The simulation results are provided to show the effectiveness of the proposed control scheme.  相似文献   

17.
This study investigates the consensus tracking problem for unknown multi-agent systems (MASs) with time-varying communication topology by using the methods of data-driven control and model predictive control. Under the proposed distributed iterative protocol, sufficient conditions for reducing tracking error are analyzed for both time invariable and time varying desired trajectories. The main feature of the proposed protocol is that the dynamics of the multi-agent systems are not required to be known and only local input-output data are utilized for each agent. Numerical simulations are presented to illustrate the effectiveness of the derived consensus conditions.  相似文献   

18.
In this article, a nonlinear iterative learning controller (NILC) is developed using an iterative dynamic linearization (IDL) and a parameter iterative learning identification technique. First, the ideal NILC is transformed into a linear parameterized form by using a controller-oriented compact form IDL (controller-CFIDL) technique. Then an iterative learning identification approach is presented for tuning the parameters of the proposed controller using real-time I/O data. For the sake of analysis, a linear data model of the nonlinear plant is obtained by using the system-oriented IDL technology and a corresponding system parameter identification algorithm is developed in iteration domain. The convergence analysis is provided for the dynamically linearized nonlinear and nonaffine discrete-time system. The results are further extended by using a controller-oriented partial form iterative dynamic linearization (controller-PFIDL) method to gain a higher-order NILC utilizing additional control information from previous iterations. Simulations of two examples show the effectiveness of the proposed methods.  相似文献   

19.
针对两轴伺服系统的研究轮廓误差控制问题,提出了一种串级型迭代学习交叉耦合轮廓误差控制方法,设计了控制器结构并且给出了两轴迭代学习交叉耦合控制算法的收敛条件.仿真结果表明此方法可以实现跟踪误差和轮廓误差的有效补偿.  相似文献   

20.
This study considers the main challenges of presenting an iterative observer under a data-driven framework for nonlinear nonaffine multi-agent systems (MASs) that can estimate nonrepetitive uncertainties of initial states and disturbances by using the information from previous iterations. Consequently, an observer-based iterative learning control is proposed for the accurate consensus tracking. First, the dynamic effect of nonrepetitive initial states is transformed as a total disturbance of the linear data model which is developed to describe I/O iteration-dynamic relationship of nonlinear nonaffine MASs. Second, the measurement noises are considered as the main uncertainty of system output. Then, we present an iterative disturbance observer to estimate the total uncertainty caused by the nonrepetitive initial shifts and measurement noises together. Next, we further propose an observer-based switching iterative learning control (OBSILC) using the iterative disturbance observer to compensate the total uncertainty and an iterative parameter estimator to estimate unknown gradient parameters. The proposed OBSILC consists of two learning control algorithms and the only difference between the two is that an iteration-decrement factor is introduced in one of them to further reduce the effect of the total uncertainty. These two algorithms are switched to each other according to a preset error threshold. Theoretical results are demonstrated by the simulation study. The proposed OBSILC can reduce the influence of nonrepetitive initial values and measurement noises in the iterative learning control for MASs by only using I/O data.  相似文献   

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