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- Optimization of Stochastic Systems: Topics in Discrete-time Dynamics - Masanao Aoki - Google книги
- Optimization of Stochastic Systems: Topics in Discrete-Time Dynamics (Masanao Aoki)
- SIAM Review
- Topics in Discrete-Time Systems
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Optimization of Stochastic Systems: Topics in Discrete-time Dynamics - Masanao Aoki - Google книги
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Optimization of Stochastic Systems: Topics in Discrete-Time Dynamics (Masanao Aoki)
Optimization of stochastic systems : topics in discrete-time systems. Responsibility Masanao Aoki. A particular robustness issue is the requirement for a control system to perform properly in the presence of input and state constraints. In the physical world every signal is limited. It could happen that a controller will send control signals that cannot be followed by the physical system, for example, trying to rotate a valve at excessive speed. This can produce undesired behavior of the closed-loop system, or even damage or break actuators or other subsystems.
Specific control techniques are available to solve the problem: model predictive control see later , and anti-wind up systems. The latter consists of an additional control block that ensures that the control signal never exceeds a given threshold. For MIMO systems, pole placement can be performed mathematically using a state space representation of the open-loop system and calculating a feedback matrix assigning poles in the desired positions. In complicated systems this can require computer-assisted calculation capabilities, and cannot always ensure robustness.
Furthermore, all system states are not in general measured and so observers must be included and incorporated in pole placement design. Processes in industries like robotics and the aerospace industry typically have strong nonlinear dynamics. In control theory it is sometimes possible to linearize such classes of systems and apply linear techniques, but in many cases it can be necessary to devise from scratch theories permitting control of nonlinear systems. These, e. Differential geometry has been widely used as a tool for generalizing well-known linear control concepts to the non-linear case, as well as showing the subtleties that make it a more challenging problem.
Control theory has also been used to decipher the neural mechanism that directs cognitive states. When the system is controlled by multiple controllers, the problem is one of decentralized control. Decentralization is helpful in many ways, for instance, it helps control systems to operate over a larger geographical area. The agents in decentralized control systems can interact using communication channels and coordinate their actions. A stochastic control problem is one in which the evolution of the state variables is subjected to random shocks from outside the system.
A deterministic control problem is not subject to external random shocks. Every control system must guarantee first the stability of the closed-loop behavior. For linear systems , this can be obtained by directly placing the poles. Non-linear control systems use specific theories normally based on Aleksandr Lyapunov 's Theory to ensure stability without regard to the inner dynamics of the system. The possibility to fulfill different specifications varies from the model considered and the control strategy chosen.
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From Wikipedia, the free encyclopedia. This article is about control theory in engineering. For control theory in linguistics, see control linguistics. For control theory in psychology and sociology, see control theory sociology and Perceptual control theory.
Main article: Classical control theory. Further information: closed-loop transfer function. Main article: PID Controller. Main articles: Controllability and Observability. Further information: System identification. Main article: State space controls.
Topics in Discrete-Time Systems
Main article: Nonlinear control. Main article: Distributed control system. Main article: Stochastic control. Main article: People in systems and control. Systems science portal. Automation Deadbeat controller Distributed parameter systems Fractional-order control H-infinity loop-shaping Hierarchical control system Model predictive control PID controller Process control Robust control Servomechanism State space controls Vector control.
Coefficient diagram method Control reconfiguration Cut-insertion theorem Feedback H infinity Hankel singular value Krener's theorem Lead-lag compensator Minor loop feedback Multi-loop feedback Positive systems Radial basis function Root locus Signal-flow graphs Stable polynomial State space representation Steady state Transient response Transient state Underactuation Youla—Kucera parametrization Markov chain approximation method.
Automation and remote control Bond graph Control engineering Control—feedback—abort loop Controller control theory Cybernetics Intelligent control Mathematical system theory Negative feedback amplifier People in systems and control Perceptual control theory Systems theory Time scale calculus. A history of control engineering, Proceedings of the Royal Society.
Journal of the American Society of Naval Engineers. Proceedings of the Royal Society of London. Fernandez-Cara1 and E. Stability of motion. Macmillan and co. October Stanford University Technical Report : 8— Cambridge, Mass. The Origins of Feedback Control.
Schaum's outline series. McGraw Hill. American Mathematical Monthly. Nature Communications. Bibcode : NatCo Lay summary. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Fuzzy Sets and Systems.
Fluctuation and Noise Letters. Kundu, S. Backhaus Energy Conversion and Management. Scientific American. Control theory. Adaptive control Control Theory Digital control Energy-shaping control Fuzzy control Hybrid control Intelligent control Model predictive control Multivariable control Neural control Nonlinear control Optimal control Real-time control Robust control Stochastic control.
Artificial neural networks Coefficient diagram method Control reconfiguration Distributed parameter systems Fractional-order control Fuzzy logic H-infinity loop-shaping Hankel singular value Kalman filter Krener's theorem Least squares Lyapunov stability Minor loop feedback Perceptual control theory State observer Vector control. Subfields of and cyberneticians involved in cybernetics.