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Assignment代写:Adaptive control

2018-09-17 来源: 51due教员组 类别: 更多范文

下面为大家整理一篇优秀的assignment代写范文- Adaptive control,供大家参考学习,这篇论文讨论了自适应控制。自适应控制是一种基于数学模型的控制方法,但是其所依据的关于模型和扰动的先验知识比较少,需要在系统的运行过程中去不断提取有关模型的信息,使模型逐渐完善。对于那些对象特性或扰动特性变化范围很大,同时又要求经常保持高性能指标的一类系统,采用自适应控制是合适的。

The development of adaptive control has a history of more than 40 years, and it has achieved rapid development in the past 20 years. It has become one of the few hot frontier research fields in the contemporary automatic control field. The research object of adaptive control is a system with a certain degree of uncertainty. The so-called "uncertainty" refers to that the mathematical model describing the controlled object and its environment is not completely certain, which contains some unknown factors and random factors. In the face of these objective uncertainties, how to design appropriate control functions to achieve and maintain the optimal or approximate optimal performance of a given performance index is the problem to be solved by adaptive control. Adaptive control is a kind of control method based on mathematical model. However, the prior knowledge about model and disturbance based on adaptive control is relatively few. It is necessary to extract information about the model continuously during the operation of the system to gradually improve the model. It can be seen that adaptive control is suitable for those kinds of systems whose object characteristics or perturbation characteristics vary widely and require high performance indexes frequently.

The design methods of adaptive control system are mainly two kinds, one is based on the theory of self-correction control, the other is based on model reference adaptive control theory. Model reference adaptive control technology was proposed by professor Whitaker of the Massachusetts institute of technology to solve the problem of aircraft autopilot. Self-correction control technology was proposed by Kalman in 1958. Due to inadequate theoretical and technological development at that time, it has not received due attention and application. In the 1960s, modern control theory developed rapidly, and some achievements were obtained, such as state space method, stability theory, optimal control, stochastic control, parameter estimation, etc., and the rapid development of electronic computers provided necessary technical basis for realizing adaptive control in the industrial production process. Since the 1970s, the adaptive control theory has made remarkable progress, and some scholars have made outstanding contributions to the adaptive control theory of deterministic and stochastic, continuous and discrete systems respectively.

The adaptive control of linear systems has been developed very well up to now. In recent years, the adaptive control of affine nonlinear systems based on the theory of differential geometry has made great progress. Firstly, it is difficult to find a model structure suitable for uncertain nonlinear dynamic systems. Secondly, there is no general form of adaptive control law, and different nonlinear systems have different adaptive control laws. Due to the difficult and the complexity of the adaptive nonlinear system, makes the self-tuning control and model reference adaptive control and so on the mature theory of adaptive control in the face of general nonlinear systems is no people on the one hand, as far as possible use of the existing adaptive control theory to deal with the adaptive control problem of nonlinear systems, on the other hand, in an effort to look for new ideas and methods.

The emergence of neural network, especially its ability to approximate general nonlinear functions, brings hope to the adaptive control of the once trapped nonlinear system. People combine the adaptive control with neural network appropriately to form a variety of adaptive control systems based on neural network. Corresponding to traditional adaptive control, neural network adaptive control can be divided into model reference control and self-correction control. The difference between the two is that the self-correction control will directly adjust the internal parameters of the controller according to the identification results of the forward and backward models of the system, so that the system can meet the given index performance. In the model reference control, the expected performance of the closed-loop control system is described by a stable reference model, which is defined as r, ym input and output pair. The purpose of the control system is to make the output y of the controlled object uniformly asymptotically approach the output of the reference model.

Stable adaptive control is the mainstream of neural network based adaptive control in recent years. According to the types of neural networks used, they can be divided into stable adaptive control based on linear parameterized neural network, multi-layer and dynamic neural network.

Stable adaptive control based on linear parameterized neural network is established by using linear parameterized neural network. Therefore, the strict conclusion of the traditional adaptive control can be directly used to adjust the weights of the neural network to obtain a stable closed-loop control system. Compared with the linear parameterized neural network, the multi-layer neural network has many unique advantages. Firstly, the multi-layer neural network has better approximation accuracy. Secondly, the adaptive controller with multi-layer neural network has few adjustable parameters, which is very important for real-time control. Because of the feedback connection, the inherent dynamic memory of the dynamic neural network makes it particularly suitable for the modeling and control of dynamic systems. The dynamic neural network can not only simulate some dynamic behaviors, such as limit cycle and chaos, but also provide the performance of multi-layer neural network with much larger scale on a smaller scale.

In short, the adaptive control of nonlinear systems has been developing rapidly in recent years, but there are still many problems, which are still the difficulties and hot spots in the adaptive control research.

Since the adaptive control was proposed, some applications have been obtained. Li yanyu et al. applied a new adaptive control method to the missile guidance system. Yang lijun designed a combined adaptive controller and applied it to the control of boiler combustion system. Hu yansu et al. proposed an adaptive reference model for Web servers, aiming at offline identification and poor real-time application of the feedback method in Web QoS control. Through online identification, the model parameters and controller parameters are updated in time according to the changes of the object model to reduce the system error as soon as possible. The results of MATLAB simulation and actual network test show that the controller can not only maintain a good proportion of delay guarantee under the harsh network environment, but also has some advantages over the traditional control method.

Adaptive control has been applied in all fields and achieved certain results. With the further development of the adaptive control theory, its application will be more extensive and its excellent performance can be better reflected.

Adaptive control system is a kind of essentially non-linear system, and its theoretical progress is slow.

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