Related Experiment Video
Updated: Oct 2, 2025
09:44
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
Published on: March 8, 2024
5.2K
Adaptive Set-Membership State Estimation for Nonlinear Systems Under Bit Rate Allocation Mechanism: A
IEEE Transactions on Neural Networks and Learning Systems
|February 23, 2022
Summary
This study introduces an adaptive neural-network-based (NN-based) state estimation method for nonlinear systems with bit rate constraints. The approach optimizes bit rate allocation and designs an NN estimator for improved accuracy and guaranteed convergence.
Area of Science:
- Control Systems Engineering
- Artificial Intelligence
- Signal Processing
Background:
- Nonlinear systems often face challenges with state estimation due to noise and communication constraints.
- Bit rate limitations in sensor networks can degrade the accuracy of remote state estimation.
- Adaptive set-membership estimation is crucial for systems with uncertainties.
Purpose of the Study:
- To develop an adaptive neural-network-based (NN-based) set-membership state estimation method.
- To address bit rate constraints and unknown-but-bounded noises in nonlinear systems.
- To enhance state estimation accuracy under communication limitations.
Main Methods:
- A bit rate allocation mechanism is designed via constrained optimization.
- A neural network (NN)-based set-membership estimator with a prediction-correction structure is developed.
- Mathematical induction and set theory are used to derive conditions for parameter and estimator existence.
Main Results:
- Sufficient conditions for adaptive tuning parameters and set-membership estimators are established.
- Optimization problems are solved to calculate estimator gains.
- Monotonicity of estimation error bounds and NN weight convergence are analyzed.
Conclusions:
- The proposed NN-based adaptive set-membership state estimation effectively handles bit rate constraints and noise.
- The method ensures the ellipsoidal set contains the system state.
- An illustrative example validates the algorithm's performance.
Related Concept Videos
01:27State Space Representation
319
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Consider an RLC circuit, a...
319
01:26Linear Approximation in Frequency Domain
147
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
147
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
112
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
112
01:21Linear Approximation in Time Domain
134
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
134
01:23Linear time-invariant Systems
508
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
508
01:22Multi-input and Multi-variable systems
185
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
In the absence...
185