Hệ thống phi tuyến

Xem 1-20 trên 741 kết quả Hệ thống phi tuyến
  • Ước lượng mờ trạng thái hệ thống phi tuyến. Các nhà khoa học đã phát triển các lý thuyết về các hệ thống phức tạp mà thành tựu là hợp nhất với các lý thuyết hỗn độn (chaos theory), lý thuyết phức tạp (complexity theory)... nghiên cứu những hệ thống động, sự hỗn loạn và thích nghi phức tạp, đưa công cụ toán học vào để mô tả hành vi hệ thống.

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  • Những kỹ thuật điều khiển truyền thống như điều khiển Tích phân t ỉ lệ(PI) hay điều khiển Vi tích phân tỉ lệ(PID) được ứng dụng thành công trong điều khiển những quá trình tuyến tính. Gần đây, điều khiển tiên đoán mô hình (MPC) cũng thực hiện thành công trong điều khiển những hệ thống tuyến tính.

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  • Bài báo trình bày về một phương pháp mô hình hóa hệ thống phi tuyến động trên cơ sở mạng nơ-ron nhân tạo ADN. Để tăng độ chính xác của mô hình, các tác giả đã sử dụng phương pháp huấn luyện mạng là sự kết hợp giữa giải thuật di truyền và giải thuật di truyền và giải thuật gradient. Phương pháp mô hình hóa này được ứng dụng để xấp xỉ mô hình con lắc ngược. Kết quả mô phỏng cho thấy khả năng ứng dụng của phương pháp trong thực tế.

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  • Overview The goal of a control system is to enhance automation within a system while providing improved performance and robustness. For instance, we may develop a cruise control system for an automobile to release drivers from the tedious task of speed regulation while they are on long trips. In this case, the output of the plant is the sensed vehicle speed, y, and the input to the plant is the throttle angle, u, as shown in Figure 1.1. Typically, control systems are designed so that the plant output follows some reference input (the driver-specified speed in the case of our...

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  • In this chapter, we will study the control of MIMO systems where constraints are placed on the flow of information. In particular we will consider the control of decentralized systems where there are constraints on information exchange between subsystems. Decentralized control systems often arise from either the physical inability of subsystem information exchange or the lack of computing capabilities required for a single central controller.

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  • Overview The use of function approximation actually has a long history in control systems. For instance, we use function approximation ideas in the development of models for control design and analysis, and conventional adaptive control generally involves the on-line tuning of linear functions (linear approximators) to match unknown linear functions (e.g., tuning a linear model to match a linear plant with constant but unknown parameters) as we discussed in Chapter 1.

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  • The purpose of this chapter is to summarize a collection of standard control design techniques for certain classes of nonlinear systems. Later we will use these control techniques to develop adaptive control approaches that are suitable for use when there is additional uncertainty in the plant dynamics. Since the linear concept

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  • In Chapters 7 and 8 we saw how adaptive control may be used as a systematic design tool to develop dynamic controllers for systems with a great deal of uncertainty while still being able to achieve good closed-loop performance. The techniques presented there, however, assumed that full state information is available for feedback. In this chapter we will extend these tools to develop adaptive output-feedback controllers for both stabilization and tracking.

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  • Thus far we have discussed how to control continuous-time systems for both state-feedback and output-feedback problems. With today’s high performance real-time systems, the control algorithms developed in the continuous-time framework are typically implemented on a sampled-data system. As long as the sampling rate of the controller is high with

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  • Adaptive fuzzy and neural systems do not exist as an isolated topic devoid of relationships to other fields. It is important to understand how they relate to other fields in order to strengthen your understanding of them, and to see how general the ideas on adaptation are. We have emphasized that the methods of this book have their foundations in conventional adaptive control and that there are many relationships to techniques, ideas, and methodologies there.

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  • Engineers have applied knowledge gained in certain areas of science in order to develop control systems. Physics is needed in the development of mathematical models of dynamical systems so that we may analyze and test our adaptive controllers.

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  • Few technologies have been used for such a vast variety of applications as neural networks and fuzzy systems. They have been found to be truely interdisciplinary tools appearing in the fields of economics, business, science, psychology, biology, and engineering to name a few. Based upon the structure of a biological nervous system, artificial neural networks use a number of interconnected simple processing elements (“neurons”) to accomplish complicated classification and function approximation tasks....

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  • As humans, we are intuitively familiar with the process of optimization because of our constant exposure to it. For instance, in business investments we seek to maximize our profits; in recreational games we seek to maximize our own score or minimize that of our opponent. It is not surprising that optimizaStion plays a key role in engineering and many other fields. In circuit design we may want to maximize power transfer, in motor design we may want to design for the highest possible torque delivery for a given amount of current, or in communication system design we may want to...

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  • In the previous chapter we explained_how to develop stable direct adaptive controllers of the form u = .F(z, O), where .F is an approximator and 8 E RP is a vector of adjustable parameters. The approximator may be defined using knowledge of the system dynamics or using a generic universal approximator. We found that as long as there exists a parameter set for the approximator such that an appropriate static stabilizing controller may be represented, then the parameters of the approximator may be adjusted on-line to achieve stability using either the a-modification or the e-modification.

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  • In Chapters 6, 7 and 8 we discussed certain important classes of continuous time nonlinear systems, and presented general methods based on state feedback for control of such systems, including in particular direct and indirect adaptive control methods. Here, we will illustrate these adaptive approaches by applying them to the problem of controlling a rotational inverted pendulum apparatus. Moreover, we will compare the performance of these methods with that of “conventional” adaptive control techniques.

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  • In Chapter 6 we introduced non-adaptive control design tools for certain classes of nonlinear systems. All of them were based on the assumption that the state of the plant is available for feedback. The scope of this chapter is to remove this restriction by dealing with the case when the state of the system is not available for

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  • In Chapters 10 and 11 we discussedtechniques for output-feedback control of nonlinear systems both via non-adaptive and adaptive methods. In this chapter we apply these techniques to three illustrative examples. In the first example we consider the problem of controlling stall and surge in a jet engine compressor and we seek to find a controller which only employs the measurement of the differential pressure acrossthe compressor to reject stall and surge while regulating the pressure at a desired value.

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  • Nhận dạng tham số hệ động phi tuyến kết hợp chỉnh hóa. Điều khiển phản hồi âm (negative feedback) là nền tảng trung tâm của Điều khiển học. Nó ở khắp mọi nơi trong các hệ thống điều khiển cơ khí, hệ sinh thái, cân bằng xã hội, ổn định thị trường, và cả những quá trình hóa sinh như chu trình Insulin. Nói chung, nó hình thành cơ sở khả năng trở lại tới ổn định sau khi bất kỳ hỗn loạn nào. ...

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  • In Chapter 6 we found that it is possible to define static (non-adaptive) stabilizing controllers, u = V,(X) with u E R”, for a wide variety of nonlineas plants. In addition to being able to define control laws for systems in input-output feedback linearizable and strict-feedback forms, it was shown how nonlinear damping and dynamic normalization may be used to compensate for system uncertainty. In this and subsequent chapters we will h consider using the dynamic (adaptive) controller u = Y, (z, 6) where now e(t) is allowed to vary with time.

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  • Xuất hiện vào những năm cuối của thập kỷ 80, phương pháp backstepping được đánh giá là công cụ thiết kế đầy triển vọng cho một số lớp hệ thống phi tuyến. Phương pháp dựa trên cách thiết kế từng bước bộ điều khiển phản hồi thoả mãn ổn định Lyapunov. Bằng việc sử dụng phương pháp thiết kế đệ qui để xây dựng hàm điều chỉnh, backstepping cho phép xây dựng luật điều khiển phản hồi chế ngự được tính phi tuyến của đối tượng.

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