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- Advanced Control of Aircraft, Spacecraft and Rockets
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- Advanced Control Of Aircraft Spacecraft And Rockets
- ADVANCED CONTROL OF AIRCRAFT, SPACECRAFT AND ROCKETS
Advanced Control of Aircraft, Spacecraft and Rockets
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Tewari, Ashish. Unless otherwise mentioned, the axes of a frame are specified by unit vectors in the same case as that of respective axis labels, for example, the axis ox would be represented by the unit vector, i, while OX is given by I.
The axes are labeled in order to constitute a right-handed triad e. Often, only electrical or mechanical signals are generated by the controller through wires cables, hydraulic lines , which must be converted into physical inputs for the plant by a separate subsystem called an actuator.
Also, controllers generally require measurement of the output variables of the plant. Whenever a measurement of a variable is involved, it is necessary to model the dynamics of the measurement process as a separate subsystem called a sensor. Generally, there are as many sensors and actuators as there are measured scalar variables and scalar control inputs, respectively.
The sensors and actuators can be modeled as part of either the plant or the controller. For our purposes, we shall model them as part of the plant. The design of a control system requires an accurate mathematical model for the plant. The dimension n of the state vector is the order of the system. The nonlinear vector functional, f. The nominal trajectory usually satisfies equation 1. The output variables of a plant result from either direct or indirect measurements related to the state variables and control inputs through sensors.
Certain errors due to sensor imperfections, called measurement noise, are invariably introduced in the measurement process. The most common task of a control system is to bring the plant to a desired state in the presence of disturbances, which can be achieved by either an open-loop or a closed-loop control system. In an open-loop control system, the controller has no knowledge of the actual state of the plant at a given time, and the control is exercised based upon a model of the plant dynamics, as well as an estimate of its state at a previous time instant, called the initial condition.
Obviously, such a blind application of control can be successful in driving the plant to a desired state if and only if the plant model is exact, and the external disturbances are absent, which is seldom possible in practice. Therefore, a closed-loop control system is the more practical alternative, in which the actual state of the plant is provided to the controller through a feedback loop, so that the control input, u t , can be appropriately adjusted.
In practice, the feedback consists of measurements of an output vector, y t , through which an estimate of the plant's state can be obtained by the controller. If the feedback loop is removed, the control system becomes an openloop type. Control SystemsOur principal task in this book is to design and analyze automatic controllers that perform their duties without human intervention. Generally, a control system can be designed for a plant that is controllable. Controllability of the plant is a sufficient but not necessary condition for the ability to design a successful control system, as discussed in Chapter 2.
For achieving a given control task, a controller must obey well-defined mathematical relationships between the plant's state variables and control inputs, called control laws. Based upon the nature of the control laws, we can classify control systems into two broad categories: terminal control and tracking control. Examples of terminal control include guidance of spacecraft and rockets. The objective of a tracking control system is to maintain the plant's state, x t , quite close to a nominal, reference state, x d t , that is available as a solution to the unforced plant state equation 1.
Most flight control problems -such as aircraft guidance, orbital control of spacecraft, and attitude control of all aerospace vehicles -fall in this category. While the design of a terminal controller is typically based on a nonlinear plant equation 1. A tracking control system can be further classified into state feedback and output feedback systems. While a state feedback system involves measurement and feedback of all the state variables of the plant which is rarely practical , an output feedback system is based upon measurement and feedback of some output variables that form the plant's output vector, y t.
The estimation of plant's state vector from the available outputs and applied inputs is called observation or state estimation , and the part of the controller that performs this essential task is called an observer.
An observer can only be designed for observable plants Appendix A. Since the observation is never exact, one only has the state estimate, x o t , in lieu of the actual state, x t. Apart from the observer, the controller has a separate subsystem called the regulator for driving the error vector, e t , to zero over a reasonable time interval. The regulator is thus the heart of the tracking control system and generates a control input based upon the detected error. Hence, the control input, u t , depends upon e t.
Moreover, u t may also depend explicitly upon the nominal, reference state, x d t , which must be fed forward in order to contribute to the total control input. Therefore, there must be a third subsystem of the tracking controller, called a feedforward controller, which generates part of the control input depending upon the desired state. A schematic block diagram of the tracking control system with an observer is shown in Figure 1.
Evidently, the controller represents mathematical relationships between the plant's estimated state, x o t , the reference state, x d t , the control input, u t , and time, t.
Such relationships constitute a control law. Both K, K d could be time-varying. Note that in Figure 1. A linear feedback control law of the form given by equation 1. Thus the PNG control law is quite simple to implement, and nearly all practical air-to-air missiles are guided by PNG control laws.
As the missile is usually rocket powered, its thrust during the engagement is nearly constant. We shall have occasion to discuss the PNG law a little later. The control system's performance is additionally judged by the time taken by the plant's state to reach the desired error tolerance about the reference state, as well as the magnitude of the control inputs required in the process.
A successful control system is one in which the maximum overshoot is small, and the time taken to reach within a small percentage of the desired state is also reasonably small. A first-order Taylor series expansion of the control system's governing nonlinear differential equations about the reference state thus yields a linear tracking system Appendix A , and the given reference solution, x d t , is regarded as the nominal state of the resulting linear system.
A great simplification occurs by making such an assumption, because we can apply the principle of linear superposition to a linearized system, in order to yield the total output due to a linear combination of several input vectors. Linear superposition also enables us to utilize operational calculus such as Laplace and Fourier transforms and linear algebraic methods for design and analysis of control systems.
Appendix A briefly presents the linear systems theory, which can be found in detail in any textbook on linear systems, such as Kailath In order to maintain the system's state close to a given reference trajectory, the tracking system must possess a special property, namely stability about the nominal reference trajectory. While stability can be defined in various ways, for our purposes we will consider stability in the sense of Lyapunov Appendix B , which essentially implies that a small control perturbation from the nominal input results in only a small deviation from the nominal trajectory.
In a tracking system, the system is driven close to the nominal trajectory by the application of the control input, u t , defined as the difference between the actual and the nominal input vectors: 1. Often, the system's governing equations are linearized before expressing them as a set of first-order, nonlinear, state equations equation 1.
For the time being, we are ignoring the disturbance inputs to the system, which can be easily included through an additional term on the right-hand side. Only in some special cases can the exact closed-form expressions for t, t i be derived. Whenever t, t i cannot be obtained in closed form, it is necessary to apply approximate numerical techniques for the solution of the state equation. Linear Time-Invariant Tracking SystemsIn many flight control applications, the coefficient matrices of the plant linearized about a reference trajectory equations 1.
This may happen because the time scale of the deviations from the reference trajectory is too small compared to the time scale of reference dynamics. Examples of these include orbital maneuvers and attitude dynamics of spacecraft about a circular orbit, and small deviations of an aircraft's flight path and attitude from a straight and level, equilibrium flight condition.
In such cases, the tracking system is approximated as a linear time-invariant system, with A, B treated as constant matrices. By substituting equation 1. All flight vehicles require manipulation i.
A transport aircraft navigating between points A and B on the Earth's surface must follow a flight path that ensures the smallest possible fuel consumption in the presence of winds. A fighter aircraft has to maneuver in a way such that the normal acceleration is maximized while maintaining the total energy and without exceeding the structural load limits. A spacecraft launch rocket must achieve the necessary orbital velocity while maintaining a particular plane of flight.
A missile rocket has to track a maneuvering target such that an intercept is achieved before running out of propellant. An atmospheric entry vehicle must land at a particular point with a specific terminal energy without exceeding the aero-thermal load limits.
In all of these cases, precise control of the vehicle's attitude is required at all times since the aerodynamic forces governing an atmospheric trajectory are very sensitive to the body's orientation relative to the flight direction. Furthermore, in some cases, attitude control alone is crucial for the mission's success.
For example, a tumbling or oscillating satellite is useless as an observation or communications platform, even though it may be in the desired orbit. Similarly, a fighter or bomber aircraft requires a stable attitude for accurate weapons delivery. Flight vehicle control can be achieved by either a pilot, an automatic controller or autopilot , or both acting in tandem. The process of controlling a flight vehicle is called flying. A manned vehicle is flown either manually by the pilot, or automatically by the autopilot that is programmed by the pilot to carry out a required task.
Unmanned vehicles can be flown either remotely or by onboard autopilots that receive occasional instructions from the ground. It is thus usual to have some form of human intervention in flying, and rarely do we have a fully automated or autonomous flight vehicle that selects the task to be performed for a given mission and also performs it. Therefore, the job of designing an automatic control system or autopilot is quite simple and consists of: i generating the required flight tasks for a given mission that are then stored in the autopilot memory as computer programs or specific data points; and ii putting in place a mechanism that closely performs the required or reference flight tasks at a given time, despite external disturbances and internal imperfections.
In i , the reference tasks are generally a set of positions, velocities, and attitudes to be followed as functions of time, and can be updated or modified by giving appropriate signals by a human being pilot or ground controller.
Account Options Sign in. Top charts. New arrivals. Ashish Tewari Jun Advanced Control of Aircraft, Spacecraft and Rockets introduces the reader to the concepts of modern control theory applied to the design and analysis of general flight control systems in a concise and mathematically rigorous style. It presents a comprehensive treatment of both atmospheric and space flight control systems including aircraft, rockets missiles and launch vehicles , entry vehicles and spacecraft both orbital and attitude control.
Advanced Control Of Aircraft Spacecraft And Rockets
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Advanced Control of Aircraft, Spacecraft and Rockets introduces the reader to the concepts of modern control theory applied to the design and analysis of general flight control systems in a concise and mathematically rigorous style. It presents aMoreAdvanced Control of Aircraft, Spacecraft and Rockets introduces the reader to the concepts of modern control theory applied to the design and analysis of general flight control systems in a concise and mathematically rigorous style. It presents a comprehensive treatment of both atmospheric and space flight control systems including aircraft, rockets missiles and launch vehicles , entry vehicles and spacecraft both orbital and attitude control. The broad coverage of topics emphasizes the synergies among the various flight control systems and attempts to show their evolution from the same set of physical principles as well as their design and analysis by similar mathematical tools.
ADVANCED CONTROL OF AIRCRAFT, SPACECRAFT AND ROCKETS
Senkin V. The aim of this paper is to develop a methodology for optimizing, at the initial design stage, the key characteristics of a rocket with a solid-propellant sustainer engine which can follow a ballistic, an aeroballistic, or a combined trajectory, including the formalization of the combined problem of simultaneous optimization of the rocket design parameters, trajectory parameters, and flight control programs. The problem is formulated as an optimal control problem with imposed equalities and differential constraints. The parameters to be optimized include the rocket design parameters and the parameters of the rocket control programs in different portions of the trajectory. The rocket control programs are proposed to be formed in polynomial form, which allows one to reduce the optimal control problem to a nonlinear programming problem. Optimization methods are overviewed, and random search methods are compared with gradient ones.
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