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A structural response reconstruction method based on a continuous-discrete state space model
Structural response reconstruction is an important technique for structural health monitoring. However, aiming at the problem of discretization error...
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Model-free method for LQ mean-field social control problems with one-dimensional state space
This paper presents a novel model-free method to solve linear quadratic (LQ) mean-field control problems with one-dimensional state space and...
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Discrete Time State Space Models
This chapter shows how to define a discrete time state space model in MATLAB, draw its impulse and step responses, calculate its poles and zeros, see...
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Continuous Time State Space Models
This chapter shows how to work with continuous time linear time-invariant state space models.
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Comparison and Performance Analysis of Model Predictive Control Developed by Transfer Function Based Model and State Space Based Model for Brushless Doubly Fed Induction Generator
Model predictive control (MPC) is an important control technique for Brushless doubly-fed induction generators (BDFIGs) which are commonly used for...
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Linearization of Nonlinear State Space Models
A nonlinear state space model can be linearized around a given operating point. This chapter shows how to obtain a linear time invariant model of a...
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State-Space Analysis of Discrete Systems
In this chapter, state-space analysis of discrete systems is presented. A system model, called the state-space model, provides a more suitable model...
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State-Space Analysis of Continuous Systems
In this chapter, the state-space analysis of continuous systems is presented. A system model, called the state-space model, provides a more suitable...
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Discrete-time systems properties defined on state-space regions
In this paper various properties of discrete-time dynamic control systems trajectories with respect to state-space corner regions are considered. The...
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Online Gaussian Process State-space Model: Learning and Planning for Partially Observable Dynamical Systems
This paper proposes an online learning method of Gaussian process state-space model (GP-SSM). GP-SSM is a probabilistic representation learning...
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An Improved Time Domain Approach for Analysis of Floating Bridges Based on Dynamic Finite Element Method and State-Space Model
The floating bridge bears the dead weight and live load with buoyancy, and has wide application prospect in deep-water transportation infrastructure....
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Minimal State-Space Realisation of a Transfer Function Matrix
Given the matrices of an LTI system, the corresponding transfer function matrix is unique and can be directly computed. The converse problem, i.e....
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Expectation-maximization Estimation Algorithm for Bilinear State-space Systems with Missing Outputs Using Kalman Smoother
In this paper, the parameter estimation of bilinear state-space systems with missing outputs is studied. The bilinear model is transformed into a...
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Classical Flutter, State-Space Model, and Root Locus
This chapter discusses the physics of classical flutter and then shows how to practically determine the conditions that lead to instability. The root...
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Incremental design-space model checking via reusable reachable state approximations
The design of safety-critical systems often requires design space exploration : comparing several system models that differ in terms of design...
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General method for state-space modeling and nonlinear control of single-phase cascaded multilevel inverters with LCL coupling
Due to the nonlinear behavior of grid-connected cascaded multilevel inverters (GCCMI), the use of nonlinear controllers can guarantee system...
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State Space Models for Spike Data
State-space methods provide powerful tools to solve a variety of neural data analysis problems. For spike train data, they are used to decode signals...
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State-space theory–based closed-loop control of machining error of thin-walled part modeling and application
The thin-walled part is a key component in the aerospace field. However, its low stiffness makes it highly susceptible to deformation during milling....
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Robust Identification of Stable MIMO Modal State Space Models
As systems become more and more complex, representing them with differential equations or transfer functions becomes cumbersome and even more so if...
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Estimation of Seismic Displacement Response Using a Kalman Filter with Data-Driven State-Space Model Identification
This paper proposes a system identification method combining a data-driven state-space model and the unscented Kalman filter (UKF) to estimate...