DISCRETE-TIME VARIABLE STRUCTURE CONTROLLER FOR AIRCRAFT FLIGHT ANGLE TRACKING

The paper presents the longitudinal, short-period aircraft dynamics and its application on the climb angle tracking. For the aircraft flight angle tracking the stable system centre technique is developed for controlling the output in a discrete-time non-minimum phase causal system using the sliding mode control. The developed discrete-time stable system centre technique transforms the output tracking problem to a corresponding state variable tracking problem by asymptotically identifying the ideal internal dynamics for the unstable internal states of a discrete-time system. A numerical simulation example is given to show the effectiveness of the method

Nonlinear continuous-time generalised predictive control

The development of the nonlinear version of the Continuous-time Generalised Predictive Control (NCGPC) is presented. Unlike the linear version, the nonlinear version is developed in state-space form and shown to include Nonlinear Generalised Minimum Variance (NGMV), and a new algorithm, Nonlinear Predictive Generalised Minimum Variance (NPGMV), as special cases. Through simulations, it is demonstrated that NCGPC can deal with nonlinear systems whose relative degree is not well defined and nonlinear systems with unstable zero dynamics. Geometric approaches, such as exact linearisation, are shown to be included in the NCGPC as special cases

Nonlinear Tracking Control Using a Robust Differential-Algebraic Approach.

This dissertation presents the development and application of an inherently robust nonlinear trajectory tracking control design methodology which is based on linearization along a nominal trajectory. The problem of trajectory tracking is reduced to two separate control problems. The first is to compute the nominal control signal that is needed to place a nonlinear system on a desired trajectory. The second problem is one of stabilizing the nominal trajectory. The primary development of this work is the development of practical methods for designing error regulators for Linear Time Varying systems, which allows for the application of trajectory linearization to time varying trajectories for nonlinear systems. This development is based on a new Differential Algebraic Spectral Theory. The problem of robust tracking for nonlinear systems with parametric uncertainty is studied in relation to the Linear Time Varying spectrum. The control method presented herein constitutes a rather general control strategy for nonlinear dynamic systems. Design and simulation case studies for some challenging nonlinear tracking problems are considered. These control problems include: two academic problems, a pitch autopilot design for a skid-to-turn missile, a two link robot controller, a four degree of freedom roll-yaw autopilot, and a complete six degree of freedom Bank-to-turn planar missile autopilot. The simulation results for these designs show significant improvements in performance and robustness compared to other current control strategies

Analysis and structural design of on-line process optimization systems.

Imperial Users onl

Physics-guided neural networks for feedforward control with input-to-state stability guarantees

Currently, there is an increasing interest in merging physics-based methods and artificial intelligence to push performance of feedforward controllers for high-precision mechatronics beyond what is achievable with linear feedforward control. In this paper, we develop a systematic design procedure for feedforward control using physics-guided neural networks (PGNNs) that can handle nonlinear and unknown dynamics. PGNNs effectively merge physics-based and NN-based models, and thereby result in nonlinear feedforward controllers with higher performance and the same reliability as classical, linear feedforward controllers. In particular, conditions are presented to validate (after training) and impose (before training) input-to-state stability (ISS) of PGNN feedforward controllers. The developed PGNN feedforward control framework is validated on a real-life, high-precision industrial linear motor used in lithography machines, where it reaches a factor 2 improvement with respect to conventional mass-friction feedforward

Inversion-based feedforward-feedback control: theory and implementation to high-speed atomic force microscope imaging

In this dissertation, a suite of inversion-based feedforward-feedback control techniques are developed and applied to achieve high speed AFM imaging. In the last decade, great efforts have been made in developing the inversion-based feedforward control as an effective approach for precision output tracking. Such efforts are facilitated by the fruitful results obtained in the stable-inversion theory, including, mainly, the bounded inverse of nonminimum-phase systems, the preview-based inversion method that quantified the effect of the future desired trajectory on the inverse input, the consideration of the model uncertainties in the system inverse, and the integration of inversion with feedback and iterative control. However, challenges still exist in those inversion-based approaches. For example, although it has been shown that the inversion-based iterative control (IIC) technique can effectively compensate for the vibrational dynamics during the output tracking in the repetitive applications, however, compensating for both the hysteresis effect and the dynamics effect simultaneously using the IIC approach has not been established yet. Moreover, the current design of the inversion-based feedforward feedback two-degree-of-freedom (2DOF) controller is ad-hoc, and the minimization of the model uncertainty effects on the feedforward control has not been addressed. Furthermore, although it is possible to combine system inversion with both iterative learning and feedback control in the so-called current cycle feedback iterative learning control (CCF-ILC) approach, the current controller design is limited to be casual and the use of such CCF-ILC approach for rejecting slowly varying periodic disturbance has not been explored. These challenges, as magnified in applications such as high-speed AFM imaging, motivate the research of this dissertation. Particularly, it is shown that the IIC approach can effectively compensate for both the hysteresis and vibrational dynamics effects of smart actuators. The convergence of the IIC algorithm is investigated by capturing the input-output behavior of piezo actuators with a cascade model consisting of a rate-independent hysteresis at the input followed by the dynamics part of the system. The size of the hysteresis and the vibrational dynamics variations that can be compensated for (by using the IIC method) has been quantified. Secondly, a novel robust-inversion has been developed for single-input-single-output (SISO) LTI systems, which minimized the dynamics uncertainty effect and obtained a guaranteed tracking performance for bounded dynamics uncertainties. Based on the robust-inversion approach, a systematic design of inversion-based two-degree-of-freedom (2DOF)-control was developed. Finally, the robust inversion- based current cycle feedback iterative learning control approach was developed for the rejection of slow varying periodic disturbances. The proposed CCF-ILC controller design utilizes the recently-developed robust-inversion technique to minimize the model uncertainty effect on the feedforward control, as well as to remove the causality constraints in other CCFILC approaches. It is shown that the iterative law converges, and attains a bounded tracking error upon noise and disturbances. In this dissertation, these techniques have been successfully implemented to achieve high-speed AFM imaging of large-size samples. Specifically, it is shown that precision positioning of the probe in the AFM lateral (x-y) scanning can be successfully achieved by using the inversion-based iterative-control (IIC) techniques and robust-inversion based 2DOF control design approach. The AFM imaging speed as well as the sample estimation can be substantially improved by using the CCF-ILC approach for the precision positioning of the probe in the vertical direction

Resource-aware motion control:feedforward, learning, and feedback

Controllers with new sampling schemes improve motion systems’ performanc

DISCRETE-TIME VARIABLE STRUCTURE CONTROLLER FOR AIRCRAFT FLIGHT ANGLE TRACKING

The paper presents the longitudinal, short-period aircraft dynamics and its application on the climb angle tracking. For the aircraft flight angle tracking the stable system centre technique is developed for controlling the output in a discrete-time non-minimum phase causal system using the sliding mode control. The developed discrete-time stable system centre technique transforms the output tracking problem to a corresponding state variable tracking problem by asymptotically identifying the ideal internal dynamics for the unstable internal states of a discrete-time system. A numerical simulation example is given to show the effectiveness of the method

Adaptive Input Reconstruction with Application to Model Refinement, State Estimation, and Adaptive Control.

Input reconstruction is the process of using the output of a system to estimate its input. In some cases, input reconstruction can be accomplished by determining the output of the inverse of a model of the system whose input is the output of the original system. Inversion, however, requires an exact and fully known analytical model, and is limited by instabilities arising from nonminimum-phase zeros. The main contribution of this work is a novel technique for input reconstruction that does not require model inversion. This technique is based on a retrospective cost, which requires a limited number of Markov parameters. Retrospective cost input reconstruction (RCIR) does not require knowledge of nonminimum-phase zero locations or an analytical model of the system. RCIR provides a technique that can be used for model refinement, state estimation, and adaptive control. In the model refinement application, data are used to refine or improve a model of a system. It is assumed that the difference between the model output and the data is due to an unmodeled subsystem whose interconnection with the modeled system is inaccessible, that is, the interconnection signals cannot be measured and thus standard system identification techniques cannot be used. Using input reconstruction, these inaccessible signals can be estimated, and the inaccessible subsystem can be fitted. We demonstrate input reconstruction in a model refinement framework by identifying unknown physics in a space weather model and by estimating an unknown film growth in a lithium ion battery. The same technique can be used to obtain estimates of states that cannot be directly measured. Adaptive control can be formulated as a model-refinement problem, where the unknown subsystem is the idealized controller that minimizes a measured performance variable. Minimal modeling input reconstruction for adaptive control is useful for applications where modeling information may be difficult to obtain. We demonstrate adaptive control of a seeker-guided missile with unknown aerodynamics.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91520/1/amdamato_1.pd