Low-complexity RLS algorithms using dichotomous coordinate descent iterations

In this paper, we derive low-complexity recursive least squares (RLS) adaptive filtering algorithms. We express the RLS problem in terms of auxiliary normal equations with respect to increments of the filter weights and apply this approach to the exponentially weighted and sliding window cases to derive new RLS techniques. For solving the auxiliary equations, line search methods are used. We first consider conjugate gradient iterations with a complexity of O(N-2) operations per sample; N being the number of the filter weights. To reduce the complexity and make the algorithms more suitable for finite precision implementation, we propose a new dichotomous coordinate descent (DCD) algorithm and apply it to the auxiliary equations. This results in a transversal RLS adaptive filter with complexity as low as 3N multiplications per sample, which is only slightly higher than the complexity of the least mean squares (LMS) algorithm (2N multiplications). Simulations are used to compare the performance of the proposed algorithms against the classical RLS and known advanced adaptive algorithms. Fixed-point FPGA implementation of the proposed DCD-based RLS algorithm is also discussed and results of such implementation are presented

An adaptive learning control system for aircraft

A learning control system and its utilization as a flight control system for F-8 Digital Fly-By-Wire (DFBW) research aircraft is studied. The system has the ability to adjust a gain schedule to account for changing plant characteristics and to improve its performance and the plant's performance in the course of its own operation. Three subsystems are detailed: (1) the information acquisition subsystem which identifies the plant's parameters at a given operating condition; (2) the learning algorithm subsystem which relates the identified parameters to predetermined analytical expressions describing the behavior of the parameters over a range of operating conditions; and (3) the memory and control process subsystem which consists of the collection of updated coefficients (memory) and the derived control laws. Simulation experiments indicate that the learning control system is effective in compensating for parameter variations caused by changes in flight conditions

Event-based synchronisation of linear discrete-time dynamical networks

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Connections Between Adaptive Control and Optimization in Machine Learning

This paper demonstrates many immediate connections between adaptive control and optimization methods commonly employed in machine learning. Starting from common output error formulations, similarities in update law modifications are examined. Concepts in stability, performance, and learning, common to both fields are then discussed. Building on the similarities in update laws and common concepts, new intersections and opportunities for improved algorithm analysis are provided. In particular, a specific problem related to higher order learning is solved through insights obtained from these intersections.Comment: 18 page

Automatic Control and Fault Diagnosis of MEMS Lateral Comb Resonators

Recent advancements in microfabrication of Micro Electro Mechanical Systems have made MEMS an important part of many applications such as safety and sensor/control devices. Miniature structure of MEMS makes them very sensitive to the environmental and operating conditions. In addition, fault in the device might change the parameters and result in unwanted behavioral variations. Therefore, imperfect device structure, fault and operating point dependencies suggest for active control of MEMS.;This research is focused on two main areas of control and fault diagnosis of MEMS devices. In the control part, the application of adaptive controllers is introduced for trajectory control of the device under health and fault conditions. Fault in different forms in the structure of the device are modeled and foundry manufactured for experimental verifications. Pull-in voltage effect in the MEMS Lateral Comb Resonators are investigated and controlled by variable structure controllers. Reliability of operation is enhanced by active control of the device under fault conditions.;The second part of this research is focused on the fault diagnosis of the MEMS devices. Fault is introduced and investigated for better understanding of the system behavioral changes. Modeling of the device in different operating conditions suggests for the multiple-model adaptive estimation (MMAE) fault diagnosis technique. Application of Kalman filters in MMAE is investigated and the performance of the fault diagnosis is compared with other techniques such as self-tuning and auto self-tuning techniques. According to the varying parameters of the system, online parameter identification systems are required to monitor the parameter variations and model the system accurately. Self-tuning banks are applied and combined with MMAE to provide accurate fault diagnosis systems. Different parameter identification techniques result in different system performances. In this regard, this research investigates the application of Recursive Least Square with Forgetting Factor. Different techniques for tuning of forgetting factor value are introduced and their results are compared for better performance. The organization of this dissertation is as follows:;Chapter I introduces the structure of the MEMS Lateral Comb Resonator; Chapter II introduces the application of control techniques and displacement feedback approach. Chapter III investigates the control approach and experimental results. In chapter IV, a new controller is introduced and designed for the MEMS trajectory controls. Chapter V is about the fault and different techniques of fault diagnosis in MEMS LCRs. Chapter 6 is the future work suggested through the current results and observations. Each chapter contains a section to summarize the concluding remarks

An Unsupervised Neural Network for Real-Time Low-Level Control of a Mobile Robot: Noise Resistance, Stability, and Hardware Implementation

We have recently introduced a neural network mobile robot controller (NETMORC). The controller is based on earlier neural network models of biological sensory-motor control. We have shown that NETMORC is able to guide a differential drive mobile robot to an arbitrary stationary or moving target while compensating for noise and other forms of disturbance, such as wheel slippage or changes in the robot's plant. Furthermore, NETMORC is able to adapt in response to long-term changes in the robot's plant, such as a change in the radius of the wheels. In this article we first review the NETMORC architecture, and then we prove that NETMORC is asymptotically stable. After presenting a series of simulations results showing robustness to disturbances, we compare NETMORC performance on a trajectory-following task with the performance of an alternative controller. Finally, we describe preliminary results on the hardware implementation of NETMORC with the mobile robot ROBUTER.Sloan Fellowship (BR-3122), Air Force Office of Scientific Research (F49620-92-J-0499

Digital adaptive controllers for VTOL vehicles. Volume 1: Concept evaluation

A digital self-adaptive flight control system was developed for flight test in the VTOL approach and landing technology (VALT) research aircraft (a modified CH-47 helicopter). The control laws accept commands from an automatic on-board guidance system. The primary objective of the control laws is to provide good command-following with a minimum cross-axis response. Three attitudes and vertical velocity are separately commanded. Adaptation of the control laws is based on information from rate and attitude gyros and a vertical velocity measurement. The final design resulted from a comparison of two different adaptive concepts--one based on explicit parameter estimates from a real-time maximum-likelihood estimation algorithm, the other based on an implicit model reference adaptive system. The two designs were compared on the basis of performance and complexity

Adaptive Stochastic Systems: Estimation, Filtering, And Noise Attenuation

This dissertation investigates problems arising in identification and control of stochastic systems. When the parameters determining the underlying systems are unknown and/or time varying, estimation and adaptive filter- ing are invoked to to identify parameters or to track time-varying systems. We begin by considering linear systems whose coefficients evolve as a slowly- varying Markov Chain. We propose three families of constant step-size (or gain size) algorithms for estimating and tracking the coefficient parameter: Least-Mean Squares (LMS), Sign-Regressor (SR), and Sign-Error (SE) algorithms. The analysis is carried out in a multi-scale framework considering the relative size of the gain (rate of adaptation) to the transition rate of the Markovian system parameter. Mean-square error bounds are established, and weak convergence methods are employed to show the convergence of suitably interpolated sequences of estimates to solutions of systems of ordinary and stochastic differential equations with regime switching. Next we consider problems in noise attenuation in systems with unmodeled dynamics and stochastic signal errors. A robust two-phase design procedure is developed which first estimates the signal in a simplified form, and then applies a control to tune out the noise. Worst-case error bounds are derived in terms of the unmodeled dynamics and variances of the disturbance and measurement errors