Internal iteration gradient estimator based parameter identification for nonlinear sandwich system subject to
Huijie Lei1, Yanwei Zhang2, Xikun Lu1
1School of Electronic, Electrical and Unmanned Aerial Vehicle, Anyang University of Technology, Anyang, People's Republic of China.
Plos One
|April 29, 2025
Summary
This study introduces an improved gradient estimation method for nonlinear systems with sensor quantization and friction. The new approach enhances parameter estimation accuracy and convergence speed for complex systems.
Area of Science:
- Control Systems Engineering
- Nonlinear System Identification
- Signal Processing
Background:
- Existing multi-innovation gradient methods (MISG) face limitations in parameter estimation for nonlinear systems.
- Quantized sensors and friction nonlinearity introduce significant challenges in accurate system modeling.
- Need for robust and efficient parameter estimation techniques for complex dynamic systems.
Purpose of the Study:
- To propose an internal iteration scalar-innovation gradient estimation method for nonlinear sandwich systems.
- To address the shortcomings of conventional MISG, particularly concerning redundant parameter estimation and multi-innovation length.
- To enhance the accuracy and convergence rate of parameter estimation in systems with sensor quantization and friction.
Main Methods:
- Decomposition method to derive an identification model, avoiding redundant parameter estimation.
- Adaptive filter utilizing prior system knowledge for optimal data selection.
- Internal iteration principle to convert multi-innovation updating to scalar-innovation updating.
- Trigger mechanism for suboptimal initial estimates to accelerate parameter adaptive laws.
Main Results:
- Successfully avoided redundant parameter estimation through model decomposition.
- Achieved positive estimation performance by converting multi-innovation to scalar-innovation updating.
- Demonstrated fast convergence rates for parameter adaptive laws.
- Validated the proposed method via numerical simulations and experimental tests on an electromechanical system.
Conclusions:
- The proposed internal iteration scalar-innovation gradient estimation method effectively identifies parameters in nonlinear sandwich systems.
- The method overcomes limitations of traditional MISG, offering improved accuracy and faster convergence.
- The strategy is robust and applicable to real-world systems with sensor quantization and friction nonlinearities.
Related Concept Videos
01:21Linear Approximation in Time Domain
53
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,...
53
01:26Linear Approximation in Frequency Domain
79
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....
79
01:18Second Order systems II
62
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
62
01:20Second Order systems I
111
A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
By reinterpreting the system, one can derive the closed-loop transfer function, which...
111
01:24PI Controller: Design
135
Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
135
01:26Feedback control systems
252
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
252