arXiv subjects

Syed Aseem Ul Islam

Publications and source records attributed to Syed Aseem Ul Islam.

7 recordsLinked to original sources

arXiv2026

Nonlinear Predictive Cost Adaptive Control of Pseudo-Linear Input-Output Models Using Polynomial, Fourier, and Cubic Spline Observables

Control of nonlinear (NL) systems with high levels of uncertainty is practically relevant and theoretically challenging. This paper presents a numerical investigation of an adaptive NL model predictive control (MPC) technique that relies entirely on online system identification without prior modeling, training, or data collection. In particular, the paper extends predictive cost adaptive control (PCAC) for linear systems, which is an extension of generalized predictive control, to NL systems. NL PCAC (NPCAC) uses recursive least squares (RLS) with subspace of information forgetting (SIFt) to identify a discrete-time, pseudo-linear, input-output model, which is used with iterative MPC for NL receding-horizon optimization. The performance of NPCAC is illustrated using polynomial, Fourier, and cubic-spline basis functions.

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arXiv2021

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. This paper analyzes a discrete-time Lur'e system with a piecewise-linear saturation feedback nonlinearity. The main result provides sufficient conditions under which the Lur'e system is self-excited in the sense that its response is bounded and nonconvergent.

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arXiv2021

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. We consider this question in three stages. First, we consider regression with random data, in which case persistency is guaranteed. Next, we apply RLS to finite-impulse-response (FIR) system identification and, finally, to infinite-impulse-response (IIR) system identification. For each case, we relate the condition number of the regressor matrix to the transient response and rate of convergence of the parameter estimates.

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arXiv2021

Data-Driven Retrospective Cost Adaptive Control for Flight Control Application

Unlike fixed-gain robust control, which trades off performance with modeling uncertainty, direct adaptive control uses partial modeling information for online tuning. The present paper combines retrospective cost adaptive control (RCAC), a direct adaptive control technique for sampled-data systems, with online system identification based on recursive least squares (RLS) with variable-rate forgetting (VRF). 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. This paper investigates the ability of RLS-VRF to provide the modeling information needed for the target model, especially nonminimum-phase (NMP) zeros. DDRCAC is applied to single-input, single-output (SISO) and multiple-input, multiple-output (MIMO) numerical examples with unknown NMP zeros, as well as several flight control problems, namely, unknown transition from minimum-phase to NMP lateral dynamics, flexible modes, flutter, and nonlinear planar missile dynamics.

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arXiv2020

Experimental Implementation of an Adaptive Digital Autopilot

This paper develops an adaptive digital autopilot for quadcopters and presents experimental results. The adaptive digital autopilot is constructed by augmenting the PX4 autopilot control system architecture with adaptive digital control laws based on retrospective cost adaptive control (RCAC). In order to investigate the performance of the adaptive digital autopilot, the default gains of the fixed-gain autopilot are scaled by a small factor, which severely degrades its performance. This scenario thus provides a venue for determining the ability of the adaptive digital autopilot to compensate for the detuned fixed-gain autopilot. The adaptive digital autopilot is tested in simulation and physical flight tests, and the resulting performance improvements are examined.

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arXiv2020

Output-Feedback Model Predictive Control with Online Identification

Model predictive control (MPC) is a widely used modern control technique with numerous successful application in diverse areas. Much of this success is due to the ability of MPC to enforce state and control constraints, which are crucial in many applications of control. In order to avoid the need for an observer, output-feedback model predictive control with online identification (OFMPCOI) uses the block observable canonical form whose state consists of past values of the control inputs and measured outputs. Online identification is performed using recursive least squares (RLS) with variable-rate forgetting. The article describes the algorithmic details of OFMPCOI and numerically investigates its performance through a collection of numerical examples that highlight various control challenges, such as model order uncertainty, sensor noise, prediction horizon, stabilization, magnitude and move-size saturation, and stabilization. The numerical examples are used to probe the performance of OFMPCOI in terms of persistency, consistency, and exigency. Since OFMPCOI does not employ a separate control perturbation to enhance persistency, the focus is on self-generated persistency during transient operation. For closed-loop identification using RLS, sensor noise gives rise to bias in the identified model, and the goal is to determine the effect of the lack of consistency. Finally, the numerical examples reveal exigency, which is the extent to which the online identification emphasizes model characteristics that are most relevant to meeting performance objectives.

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arXiv2020

A Discrete-Time, Time-Delayed Lur'e Model with Biased Self-Excited Oscillations

Self-excited systems arise in many applications, such as biochemical systems, mechanical systems with fluid-structure interaction, and fuel-driven systems with combustion dynamics. This paper presents a Lur'e model that exhibits biased self-excited oscillations under constant inputs. The model involves asymptotically stable linear dynamics, time delay, a washout filter, and a saturation nonlinearity. For all sufficiently large scalings of the loop transfer function, these components cause divergence under small signal levels and decay under large signal amplitudes, thus producing an oscillatory response. A bias-generation mechanism is used to specify the mean of the oscillation. The main contribution of the paper is a detailed analysis of a discrete-time version of this model.

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