In this paper an H(infinity) optimal, robust flight control system design for a supersonic aircraft has been described. Separate controllers are designed for longitudinal and lateral motions. A general two-degrees-of-...
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In this paper an H(infinity) optimal, robust flight control system design for a supersonic aircraft has been described. Separate controllers are designed for longitudinal and lateral motions. A general two-degrees-of-freedom controller is proposed, where feedback control is designed for robust performance augmentation, while a series compensator is used to ensure the requisite handling qualities. Three alternative methods to achieve performance robustness have been discussed. The results obtained are very encouraging. It is hoped that this approach will equip the flight control engineers with an alternative to the conventional methods.
The Albus's Cerebellar model Articulation controller (CMAC) network has been used in many practical areas with considerable success. This paper presents a fuzzified CMAC network (FCMAC) acting as a multivariable a...
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The Albus's Cerebellar model Articulation controller (CMAC) network has been used in many practical areas with considerable success. This paper presents a fuzzified CMAC network (FCMAC) acting as a multivariable adaptive controller with the feature of self-organizing association cells and the further ability of self-learning the required teacher signals in real-time. In particular, the original CMAC has been reformulated within a framework of a simplified fuzzy control algorithm (SFCA) and the associated self-learning algorithms have been developed as a result of incorporating the schemes of competitive learning and iterative learning control into the system. By using a similarity-measure-based, instead of coding-algorithm-based, content-addressable scheme, FCMAC is capable of dealing with arbitrary-dimensional continuous input space in a simple manner without involving complicated discretizing, quantizing, coding, and hashing procedures used in the original CMAC. The learning control system described here can be thought of as either a completely unsupervised fuzzy-neural control strategy without relying on the process model or equivalently an automatic real-time knowledge acquisition scheme for the implementation of fuzzy controllers. The proposed approach has been applied to a multivariable blood pressure control problem which is characterized by strong interaction between variables and large time delays.
A new technique is presented for the solution of the exact model matching problem of linear time-invariant systems using generalized sampled-data hold functions. This technique reduces the problem to that of solving a...
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A new technique is presented for the solution of the exact model matching problem of linear time-invariant systems using generalized sampled-data hold functions. This technique reduces the problem to that of solving a nonhomogeneous algebraic system of equations. On the basis of this system of equations, the necessary and sufficient conditions are established and the expressions of the controller matrices are derived.
This paper proposes a control scheme, modeled following control concept, for secondary controlled hydraulic systems with multiple motors. An MIMO bilinear model for the hydraulic system is developed and tested on a te...
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This paper proposes a control scheme, modeled following control concept, for secondary controlled hydraulic systems with multiple motors. An MIMO bilinear model for the hydraulic system is developed and tested on a test stand, which shows a satisfactory approximation. With the help of this model, a control scheme based on the principle of model following control is developed. The derivation of the control law follows the application of Lyapunov theory, by which both the model following error and its integral are taken into account. Supported by a disturbance observer, the control scheme allows nearly global system linearization, stabilization and increase of system robustness. The application of the approach to the secondary controlled hydraulic system shows satisfying performance.
Neural networks have been used extensively in manufacturing as intelligent black boxes that solve operational problems. However, these networks are unsuitable for supporting high level manufacturing problems because t...
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Neural networks have been used extensively in manufacturing as intelligent black boxes that solve operational problems. However, these networks are unsuitable for supporting high level manufacturing problems because the solutions must be justified. One of such high level problems is the design of a manufacturing system organization. An intelligent system that recognizes and justifies the appropriate organization for a manufacturing system, is presented. This system includes a new type of neural network named intelligent referencemodel. The intelligent system is applied to identify the appropriate organization of three factories.
In this paper we investigate some important issues in system identification for the windsurfer approach to robust adaptive control. In particular, correlation function estimates an Power spectrum estimates are compare...
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In this paper we investigate some important issues in system identification for the windsurfer approach to robust adaptive control. In particular, correlation function estimates an Power spectrum estimates are compared as methods for model validation. This comparison leads us to suggest a reliable procedure for deciding (1) when should we identify a better model, and (2) whether we have identified a good model for our purposes.
A novel reconfigurable control system has been designed by combining a newly developed fault detection/diagnosis scheme with a model following control strategy. The fault detection/diagnosis is carried out in s - doma...
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A novel reconfigurable control system has been designed by combining a newly developed fault detection/diagnosis scheme with a model following control strategy. The fault detection/diagnosis is carried out in s - domain by estimating the modal information of the system, and comparing the eigenvalues of the faulty system with a pre-determined set of root loci. The model following control scheme utilizes the pre-fault system model as a referencemodel to compute the reconfigured controller. The performance of the scheme has been evaluated on a simulated DC motor system where various types of faults are introduced.
A methodology incorporating both dynamic inversion and structured singular value synthesis is employed to design a robust nonlinear autopilot for an air-to-air missile. The nonlinear components include fast and slow i...
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A methodology incorporating both dynamic inversion and structured singular value synthesis is employed to design a robust nonlinear autopilot for an air-to-air missile. The nonlinear components include fast and slow inversion loops which approximately lin earize the system when closed in the absence of modeling errors. A feedback control law is designed for this closed-loop system in the presence of structured uncertainties. The current controller architecture differs from previous work in the following two respects: uncertainties and modeling errors in the slow dynamics are treated explicitly, and the autopilot accepts external commands in bank angle rather than stability-axis roll rate.
The situation of control education in Japan is introduced with special focus on teaching exact model matching and adaptive control. General comments on control education in Japan are presented in the first place, and ...
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The situation of control education in Japan is introduced with special focus on teaching exact model matching and adaptive control. General comments on control education in Japan are presented in the first place, and then the author's experience in teaching exact model matching and adaptive control is introduced. It is emphsized that an adaptive control system with multiple control modes based on the principle of EMM with a disturbance predictor is very reliable and should be further developed toward the practical use in industry.
This work is to study the application of neural network model in self-learning control for batch process. A neural network model is developed to simulate the empirical control strategies using by a control engineer. T...
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This work is to study the application of neural network model in self-learning control for batch process. A neural network model is developed to simulate the empirical control strategies using by a control engineer. The process system status and time varying set-point values are treated as input variables of the model. The manipulated variables are used as output variables. A moving-average method is used to describe the batch process dynamics and to condense the past history of process responses. Both efficiency and effectiveness of our neural network model are validated by plentiful experimental data from an E. Colt hatch fermenter.
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