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Found 22 papers, 0 papers with code
Convergence of Recursive Least Squares Based Input/Output System Identification with Model Order Mismatch
Next, this work analyzes online identification of input/output models in the case where the order of the identified model is higher than that of the true system.
Efficient Batch and Recursive Least Squares for Matrix Parameter Estimation with Application to Adaptive MPC
Traditionally, batch least squares (BLS) and recursive least squares (RLS) are used for identification of a vector of parameters that form a linear model.
Adaptive Kalman Filtering Developed from Recursive Least Squares Forgetting Algorithms
KFLS is an extension of generalized forgetting recursive least squares (GF-RLS), a general framework which contains various extensions of RLS from the literature as special cases.
SIFt-RLS: Subspace of Information Forgetting Recursive Least Squares
This paper presents subspace of information forgetting recursive least squares (SIFt-RLS), a directional forgetting algorithm which, at each step, forgets only in row space of the regressor matrix, or the \textit{information subspace}.
Experimental Application of Predictive Cost Adaptive Control to Thermoacoustic Oscillations in a Rijke Tube
Since the dynamics of a Rijke tube are difficult to model to a high level of accuracy, the implementation of MPC requires leveraging data from the physical setup as well as knowledge about thermoacoustics, which is labor intensive and requires domain expertise.
A Data-Driven Autopilot for Fixed-Wing Aircraft Based on Model Predictive Control
For applications in which the aerodynamics are unmodeled, the present paper proposes an autopilot based on predictive cost adaptive control (PCAC).
Adaptive Real-Time Numerical Differentiation with Variable-Rate Forgetting and Exponential Resetting
Digital PID control requires a differencing operation to implement the D gain.
Adaptive Output-Feedback Model Predictive Control of Hammerstein Systems with Unknown Linear Dynamics
This paper considers model predictive control of Hammerstein systems, where the linear dynamics are a priori unknown and the input nonlinearity is known.
Output-Feedback Nonlinear Model Predictive Control with Iterative State- and Control-Dependent Coefficients
By optimizing the predicted performance over a receding horizon, model predictive control (MPC) provides the ability to enforce state and control constraints.
Kinematics-Based Sensor Fault Detection for Autonomous Vehicles Using Real-Time Numerical Differentiation
Sensor fault detection is of extreme importance for ensuring the safe operation of vehicles.
Real-Time Numerical Differentiation of Sampled Data Using Adaptive Input and State Estimation
Real-time numerical differentiation plays a crucial role in many digital control algorithms, such as PID control, which requires numerical differentiation to implement derivative action.
Generalized Forgetting Recursive Least Squares: Stability and Robustness Guarantees
This work presents generalized forgetting recursive least squares (GF-RLS), a generalization of recursive least squares (RLS) that encompasses many extensions of RLS as special cases.
Self-Excited Dynamics of Discrete-Time Lur'e Models with Affinely Constrained, Piecewise-C1 Feedback Nonlinearities
Self-excited systems (SES) arise in numerous applications, such as fluid-structure interaction, combustion, and biochemical systems.
Experimental Flight Testing of an Adaptive Autopilot with Parameter Drift Mitigation
This paper modifies an adaptive multicopter autopilot to mitigate instabilities caused by adaptive parameter drift and presents simulation and experimental results to validate the modified autopilot.
Experimental Flight Testing of a Fault-Tolerant Adaptive Autopilot for Fixed-Wing Aircraft
Next, the performance of the adaptive autopilot is examined under failure conditions by simulating a scenario where one of the control surfaces is assumed to be stuck at an unknown angle.
On the Accuracy of the One-step UKF and the Two-step UKF
The most accurate version of the unscented Kalman filter (UKF) involves the construction of two ensembles.
Retrospective Cost Parameter Estimation with Application to Space Weather Modeling
This technique, called retrospective cost parameter estimation (RCPE), is motivated by large-scale complex estimation models characterized by high-dimensional nonlinear dynamics, nonlinear parameterizations, and representational models.
An Adaptive Digital Autopilot for Fixed-Wing Aircraft with Actuator Faults
This paper develops an adaptive digital autopilot for a fixed-wing aircraft and compares its performance with a fixed-gain autopilot.
Self-Excited Dynamics of Discrete-Time Lur'e Systems
Self-excited systems arise in numerous applications, such as biochemical systems, fluid-structure interaction, and combustion.
Regularization-Induced Bias and Consistency in Recursive Least Squares
Within the context of recursive least squares (RLS) parameter estimation, the goal of the present paper is to study the effect of regularization-induced bias on the transient and asymptotic accuracy of the parameter estimates.
Two Modifications of the Unscented Kalman Filter that Specialize to the Kalman Filter for Linear Systems
The ultimate goal is thus to develop modifications of UKF that specialize to the Kalman filter for linear systems and have improved accuracy for nonlinear systems.
Data-Driven Retrospective Cost Adaptive Control for Flight Control Application
The combination of RCAC and RLS-VRF constitutes data-driven RCAC (DDRCAC), where the online system identification is used to construct the target model, which defines the retrospective performance variable.
