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1.
    
Recently proposed adaptive dynamic programming (ADP) tracking controllers assume that the reference trajectory follows time-invariant exo-system dynamics—an assumption that does not hold for many applications. In order to overcome this limitation, we propose a new Q-function that explicitly incorporates a parametrized approximation of the reference trajectory. This allows learning to track a general class of trajectories by means of ADP. Once our Q-function has been learned, the associated controller handles time-varying reference trajectories without the need for further training and independent of exo-system dynamics. After proposing this general model-free off-policy tracking method, we provide an analysis of the important special case of linear quadratic tracking. An example demonstrates that our new method successfully learns the optimal tracking controller and outperforms existing approaches in terms of tracking error and cost.  相似文献   

2.
    
In this article, an observer-based adaptive boundary iterative learning control law is developed for a class of two-link rigid-flexible manipulator with input backlash, the unknown external disturbance, and the endpoint constraint. To tackle the backlash nonlinearities and ensure the vibration suppression, the disturbance observers based upon the iterative learning conception are considered in the adaptive boundary control design. A barrier Lyapunov function is incorporated with boundary control law to restrict the endpoint state. Based on the defined barrier composite energy function, the tracking angle error convergence of the rigid part is guaranteed, and the vibrations of the flexible part are suppressed through the rigorous analysis. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed control.  相似文献   

3.
    
Concurrent learning (CL) is a recently developed adaptive update scheme that can be used to guarantee parameter convergence without requiring persistent excitation. However, this technique requires knowledge of state derivatives, which are usually not directly sensed and therefore must be estimated. A novel integral CL method is developed in this paper that removes the need to estimate state derivatives while maintaining parameter convergence properties. Data recorded online is exploited in the adaptive update law, and numerical integration is used to circumvent the need for state derivatives. The novel adaptive update law results in negative definite parameter error terms in the Lyapunov analysis, provided an online‐verifiable finite excitation condition is satisfied. A Monte Carlo simulation illustrates improved robustness to noise compared to the traditional derivative formulation. The result is also extended to Euler‐Lagrange systems, and simulations on a two‐link planar robot demonstrate the improved performance compared to gradient‐based adaptation laws.  相似文献   

4.
非线性系统自适应控制及其在电力系统中的应用   总被引：4，自引：1，他引：3  
王康  兰洲  甘德强  倪以信 《电网技术》2007,31(11):11-16
电力系统中存在众多不确定的参数,所面临的运行条件和外界扰动复杂多变,有必要引入自适应控制理论来处理这些不确定因素。非线性系统的自适应控制与线性系统相比,在研究方法等方面都有着很大的不同,目前处在继续发展的阶段。文章首先对近十几年来非线性系统的参数自适应控制、鲁棒自适应以及智能化自适应控制做了简明的归纳,然后介绍了其中一些研究成果在电力系统稳定与控制研究中的应用,最后对全文做了总结和展望。  相似文献   

5.
    
Adaptive filter has been applied in adaptive feedback and feedforward control systems, where the filter dimension is often determined by trial‐and‐error. The controller design based on a near‐optimal adaptive filter in digital signal processor (DSP) is developed in this paper for real‐time applications. The design integrates the adaptive filter and the experimental design such that their advantages in stability and robustness can be combined. The near‐optimal set of controller parameters, including the sampling rate, the dimension of system identification model, the dimension (order) of adaptive controller in the form of an FIR filter, and the convergence rate of adaptation is shown to achieve the best possible system performance. In addition, the sensitivity of each design parameter can be determined by analysis of means and analysis of variance. Effectiveness of the adaptive controller on a DSP is validated by an active noise control experiment. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

6.
    
An adaptive homo‐backstepping control for nonlinear strict‐feedback systems subjected to unknown actuator dead‐zone and disturbance is investigated. A sliding‐mode‐based integral filter is constructed and used to approximate the desired feedback control in the backstepping‐like recursive design technique. Subsequently, the problem of “explosion of complexity” is solved by obviating the analytic derivatives deduction for virtual control in the conventional backstepping technology. The actuator dead‐zone dynamic is modeled as the combination of a line and a disturbance‐like term, which makes the controller design simpler. The interconnected control module and filter module in the resulting closed‐loop system satisfy the input‐to‐state practically stability‐modularity condition, provided that the small‐gain theorem is exploited to ensure the stability of closed‐loop system. The proposed approach cannot only mitigate the effect of dead‐zone but also solve the problem of explosion of complexity in the previous literature. Numerical simulations performed on a manipulator with a brushed DC motor are introduced to illustrate the effectiveness of underlying control scheme.  相似文献   

7.
    
An adaptive internal model control (IMC) framework is proposed in this article for infinite impulse response systems. The innovation in this study stems from the relaxed assumption that the controller needs to know a priori the system order. To bypass this restriction, a lattice filter identifies the system's order as well as its reflection coefficients. Within the IMC structure, a lattice‐based controller is utilized in the forward path in cascade with a low‐pass detuning filter. The controller self‐configures its structure according to the estimated system order, while the filter's bandwidth increases as the identifier estimates more accurately the system dynamics. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

8.
    
Two incompatible topologies appear in the study of adaptive systems: the graph topology in control design, and the coefficient topology in system identification. Their incompatibility is manifest in the stabilization problem of adaptive control. We argue that this problem can be approached by changing the geometry of the sets of control systems under consideration: estimating np parameters in an np‐dimensional manifold whose points all correspond to stabilizable systems. One way to construct the manifold is using the properties of the algebraic Riccati equation. Parameter estimation can be approached as an optimal control problem akin to the deterministic Kalman filter, leading to algorithms that can be used in conjunction with standard observers and controllers to construct stable adaptive systems. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

9.
    
An estimator of the wind speed of a wind turbine coupled to a generator is proposed in this paper. Wind speed enters into the generator dynamics through a highly nonlinear function; hence, we are confronted with a difficult problem of estimation of a nonlinearly parameterized system. To solve this problem, we use the technique of immersion and invariance, recently introduced in the literature. It is assumed that the rotor speed and electrical torque of the generator are measured, which is the case for the machines typically used in this application. The result is of interest for the design of controllers of maximum power extraction, where the knowledge of the wind speed is necessary to express the control objective as a speed tracking problem. Detailed computer simulations are presented to assess the performance of the proposed estimator.Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

10.
    
Nonlinear adaptive filtering has been extensively studied in the literature, using, for example, Volterra filters or neural networks. Recently, kernel methods have been offering an interesting alternative because they provide a simple extension of linear algorithms to the nonlinear case. The main drawback of online system identification with kernel methods is that the filter complexity increases with time, a limitation resulting from the representer theorem, which states that all past input vectors are required. To overcome this drawback, a particular subset of these input vectors (called dictionary) must be selected to ensure complexity control and good performance. Up to now, all authors considered that, after being introduced into the dictionary, elements stay unchanged even if, because of nonstationarity, they become useless to predict the system output. The objective of this paper is to present an adaptation scheme of dictionary elements, which are considered here as adjustable model parameters, by deriving a gradient‐based method under collinearity constraints. The main interest is to ensure a better tracking performance. To evaluate our approach, dictionary adaptation is introduced into three well‐known kernel‐based adaptive algorithms: kernel recursive least squares, kernel normalized least mean squares, and kernel affine projection. The performance is evaluated on nonlinear adaptive filtering of simulated and real data sets. As confirmed by experiments, our dictionary adaptation scheme allows either complexity reduction or a decrease of the instantaneous quadratic error, or both simultaneously. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

11.
    
This survey paper studies deterministic control systems that integrate three of the most active research areas during the last years: (1) online learning control systems, (2) distributed control of networked multiagent systems, and (3) hybrid dynamical systems (HDSs). The interest for these types of systems has been motivated mainly by two reasons: First, the development of cheap massive computational power and advanced communication technologies, which allows to carry out large computations in complex networked systems, and second, the recent development of a comprehensive theory for HDSs that allows to integrate continuous‐time dynamical systems and discrete‐time dynamical systems in a unified manner, thus providing a unifying modeling language for complex learning‐based control systems. In this paper, we aim to give a comprehensive survey of the current state of the art in the area of online learning control in multiagent systems, presenting an overview of the different types of problems that can be addressed, as well as the most representative control architectures found in the literature. These control architectures are modeled as HDSs, which include as special subsets continuous‐time dynamical systems and discrete‐time dynamical systems. We highlight the different advantages and limitations of the existing results as well as some interesting potential future directions and open problems.  相似文献   

12.
In this brief note we make three remarks concerning adaptive implementations of neural networks and fuzzy systems. First, we bring to the readers attention the fact that the potential power of these systems as function approximators is lost when, as in some recently published works, the adjustable parameters are only the linear combination weights of the basis functions. Second, we show that the stability analysis in those papers in any way uses properties particular to neural nets or fuzzy systems and follows immediately from well-established results in adaptive systems theory. The second fact is well known to people familiar with adaptive systems theory, but not necessarily so to the neuro-fuzzy community. On the other hand, the opposite seems to be the case for the first remark. Finally, we present a simple version of a result on adaptive stablilization of non-linearly parametrized non-linear systems which might be useful for the stability analysis of adaptive neuro-fuzzy systems. This result, though well known in the Russian literature for a long time, has apparantly been overlooked in ‘western’ publications.  相似文献   

13.
    
In this paper, we propose a model reference adaptive control (MRAC) strategy for continuous‐time single‐input single‐output (SISO) linear time‐invariant (LTI) systems with unknown parameters, performing repetitive tasks. This is achieved through the introduction of a discrete‐type parametric adaptation law in the ‘iteration domain’, which is directly obtained from the continuous‐time parametric adaptation law used in standard MRAC schemes. In fact, at the first iteration, we apply a standard MRAC to the system under consideration, while for the subsequent iterations, the parameters are appropriately updated along the iteration‐axis, in order to enhance the tracking performance from iteration to iteration. This approach is referred to as the model reference adaptive iterative learning control (MRAILC). In the case of systems with relative degree one, we obtain a pointwise convergence of the tracking error to zero, over the whole finite time interval, when the number of iterations tends to infinity. In the general case, i.e. systems with arbitrary relative degree, we show that the tracking error converges to a prescribed small domain around zero, over the whole finite time interval, when the number of iterations tends to infinity. It is worth noting that this approach allows: (1) to extend existing MRAC schemes, in a straightforward manner, to repetitive systems; (2) to avoid the use of the output time derivatives, which are generally required in traditional iterative learning control (ILC) strategies dealing with systems with high relative degree; (3) to handle systems with multiple tracking objectives (i.e. the desired trajectory can be iteration‐varying). Finally, simulation results are carried out to support the theoretical development. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

14.
    
This paper presents a technique to localize the vehicle with respect to known landmarks using on‐board navigation sensors, overcoming uncertainities in vehicle dynamics and its operating environment. Results of computer simulation and pool experimentation are presented. Those results show that the system is capable of controlling the vehicle with high accuracy using the estimated position and velocity from the on‐board sensor‐based navigation system. Copyright © 2000 John Wiley & Sons, Ltd.  相似文献   

15.
在分析无刷直流电机运行原理的基础上,提出了基于TMS32OLF2407A的永磁无刷直流电机控制系统的解决方案,即将单神经元与PID控制结合应用于无刷直流电机中。仿真试验表明,采用单神经元PID控制能取得令人满意的动静态性能,具有较强的鲁棒性和自适应性,改善了电机的调速性能。  相似文献   

16.
    
This article reports the development, stability analysis, and experimental evaluation of a novel adaptive identification (AID) algorithm for underwater vehicles (UVs) for on-line estimation of plant parameters (hydrodynamic mass, quadratic drag, righting moment, and buoyancy parameters) that enter linearly into 6 degree-of-freedom (6-DOF) second-order rigid-body UV plant dynamic models. The reported UV AID method does not require instrumentation of vehicle acceleration as is required of other standard plant parameter identification methods such as conventional least squares. All but one previously reported adaptive methods for second-order nonlinear plants have addressed the problem of model-based adaptive tracking control—approaches in which adaptive plant model identification is performed simultaneously with model-based trajectory-tracking control of fully-actuated second-order plants; however, these approaches are not applicable when the plant is either uncontrolled, under open-loop control, underactuated, or using any control law other than an algorithm-specific adaptive tracking controller. The UV AID algorithm reported herein does not require simultaneous reference trajectory-tracking control, nor does it require instrumentation of linear acceleration or angular acceleration; thus this novel approach complements previously reported adaptive tracking methods and is applicable to a broader class of UV applications for which fully-actuated tracking control is impractical or infeasible. We report a experimental performance analysis of the UV AID algorithm in comparison to conventional least-square identification methods, including comparison in cross-validation where the performance of the experimentally identified plant models obtained in identification trials are compared to experimental trials differing from the identification trials.  相似文献   

17.
    
This article addresses the issue of adaptive intelligent asymptotic tracking control for a class of stochastic nonlinear systems with unknown control gains and full state constraints. Unlike the existing systems in the literature in which the prior knowledge of the control gains is available for the controller design, the salient feature of our considered system is that the control gains are allowed to be unknown but have a positive sign. By introducing an auxiliary virtual controller and employing the new properties of Numbness functions, the major technique difficulty arising from the unknown control gains is overcome. At the same time, the -type barrier Lyapunov functions are introduced to prevent the violation of the state constraints. What's more, neural networks' universal online approximation ability and gain suppression inequality technology are combined in the frame of adaptive backstepping design, so that a new control method is proposed, which cannot only realize the asymptotic tracking control in probability, but also meet the requirement of the full state constraints imposed on the system. In the end, the simulation results for a practical example demonstrate the effectiveness of the proposed control method.  相似文献   

18.
    
Repetitive control (RC) algorithms for a plant, which contain pairs of complex conjugate poles at low frequency, resulting in a resonant system, is the subject area of this paper where the experimental results given are for a gantry robot and conveyor system in which the gantry is required to transfer payloads to a constant velocity conveyor by performing a repeating ‘pick and place’ operation. Initially, the gantry robot is controlled by means of a PID feedback controller in parallel with a proportional (P‐type) repetitive feed‐forward loop, while the conveyor operates under proportional feedback control. It is found that the RC system is unable to achieve long‐term performance. The performance degrades within a relatively small number of repetitions due to the build up of resonant frequencies in the learning loop. To prevent this, a batch aliasing technique, originally developed for iterative learning control, is modified to work in the RC framework, and is implemented in real‐time. The superior performance potential of the aliasing system is demonstrated experimentally. In the second part of this paper, multi‐machine systems, are considered where the critical new factor is the relative error between the conveyor and the robot. Here a second supervisory learning loop is proposed for use to shift the reference trajectory of one machine so that the relative placement error is also reduced. Again, supporting experimental results are given. Copyright © 2006 John Wiley & Sons, Ltd.  相似文献   

19.
针对汽包锅炉给水过程的特点,采用了一种带在线辨识的自适应P ID控制器。利用递推最小二乘法循环调整P ID参数,并将该控制器应用于锅炉汽包水位三冲量控制系统。MATLAB仿真研究表明,与传统的P ID算法比较,该控制方案表现出了良好的动、静态性能,并能适应被控制对象参数的变化,具有较强的鲁棒性和自适应能力。  相似文献   

20.
    
The objective of this paper is to present a new algorithm to improve the adaptation rate of a predictive adaptive controller. For that sake, the possible plant dynamic outcomes are covered by a bank of models. Each model is used to re‐initialize the adaptive controller every time there is a large change in dynamics. The contribution of the paper consists in the development of a procedure that includes additional models in the bank when found suitable according to defined criteria. The algorithm is demonstrated in a benchmark problem consisting of the position control of two masses coupled by a spring of varying stiffness. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

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