Methods for Model Complexity Reduction for the Nonlinear Calibration of Amplifiers Using Volterra Kernels

Volterra models allow modeling nonlinear dynamical systems, even though they require the estimation of a large number of parameters and have, consequently, potentially large computational costs. The pruning of Volterra models is thus of fundamental importance to reduce the computational costs of nonlinear calibration, and improve stability and speed, while preserving accuracy. Several techniques (LASSO, DOMP and OBS) and their variants (WLASSO and OBD) are compared in this paper for the experimental calibration of an IF amplifier. The results show that Volterra models can be simplified, yielding models that are 4–5 times sparser, with a limited impact on accuracy. About 6 dB of improved Error Vector Magnitude (EVM) is obtained, improving the dynamic range of the amplifiers. The Symbol Error Rate (SER) is greatly reduced by calibration at a large input power, and pruning reduces the model complexity without hindering SER. Hence, pruning allows improving the dynamic range of the amplifier, with almost an order of magnitude reduction in model complexity. We propose the OBS technique, used in the neural network field, in conjunction with the better known DOMP technique, to prune the model with the best accuracy. The simulations show, in fact, that the OBS and DOMP techniques outperform the others, and OBD, LASSO and WLASSO are, in turn, less efficient. A methodology for pruning in the complex domain is described, based on the Frisch–Waugh–Lovell (FWL) theorem, to separate the linear and nonlinear sections of the model. This is essential because linear models are used for equalization and cannot be pruned to preserve model generality vis-a-vis channel variations, whereas nonlinear models must be pruned as much as possible to minimize the computational overhead. This methodology can be extended to models other than the Volterra one, as the only conditions we impose on the nonlinear model are that it is feedforward and linear in the parameters

EASILY VERIFIABLE CONTROLLER DESIGN WITH APPLICATION TO AUTOMOTIVE POWERTRAINS

Bridging the gap between designed and implemented model-based controllers is a major challenge in the design cycle of industrial controllers. This gap is mainly created due to (i) digital implementation of controller software that introduces sampling and quantization imprecisions via analog-to-digital conversion (ADC), and (ii) uncertainties in the modeled plant’s dynamics, which directly propagate through the controller structure. The failure to identify and handle these implementation and model uncertainties results in undesirable controller performance and costly iterative loops for completing the controller verification and validation (V&V) process. This PhD dissertation develops a novel theoretical framework to design controllers that are robust to implementation imprecision and uncertainties within the models. The proposed control framework is generic and applicable to a wide range of nonlinear control systems. The final outcome from this study is an uncertainty/imprecisions adaptive, easily verifiable, and robust control theory framework that minimizes V&V iterations in the design of complex nonlinear control systems. The concept of sliding mode controls (SMC) is used in this study as the baseline to construct an easily verifiable model-based controller design framework. SMC is a robust and computationally efficient controller design technique for highly nonlinear systems, in the presence of model and external uncertainties. The SMC structure allows for further modification to improve the controller robustness against implementation imprecisions, and compensate for the uncertainties within the plant model. First, the conventional continuous-time SMC design is improved by: (i) developing a reduced-order controller based on a novel model order reduction technique. The reduced order SMC shows better performance, since it uses a balanced realization form of the plant model and reduces the destructive internal interaction among different states of the system. (ii) developing an uncertainty-adaptive SMC with improved robustness against implementation imprecisions. Second, the continuous-time SMC design is converted to a discrete-time SMC (DSMC). The baseline first order DSMC structure is improved by: (i) inclusion of the ADC imprecisions knowledge via a generic sampling and quantization uncertainty prediction mechanism which enables higher robustness against implementation imprecisions, (ii) deriving the adaptation laws via a Lyapunov stability analysis to overcome uncertainties within the plant model, and (iii) developing a second order adaptive DSMC with predicted ADC imprecisions, which provides faster and more robust performance under modeling and implementation imprecisions, in comparison with the first order DSMC. The developed control theories from this PhD dissertation have been evaluated in real-time for two automotive powertrain case studies, including highly nonlinear combustion engine, and linear DC motor control problems. Moreover, the DSMC with predicted ADC imprecisions is experimentally tested and verified on an electronic air throttle body testbed for model-based position tracking purpose

Analytical and computational methods for the study of rare event probabilities in dispersive and dissipative waves

The main focus of this dissertation is the application of importance sampling (IS) to calculate the probabilities associated with rare events in nonlinear, large-dimensional lightwave systems that are driven by noise, including models for fiber-based optical communication system and mode-locked lasers. Throughout the last decade, IS has emerged as a valuable tool for improving the efficiency of simulating rare events in such systems. In particular, it has shown great success in simulating various sources of transmission impairments found in optical communication systems, with examples ranging from large polarization fluctuations resulting from randomly varying fiber birefringence to large pulse-width fluctuations resulting from imperfections in the optical fiber. In many cases, the application of IS is guided by a low-dimensional reduction of the system dynamics. Combining the low-dimensional reduction with Monte Carlo simulations of the original system has been shown to be an extremely effective scheme for computing, for example, the probability with which a pulse deviates significantly from its initial form due to a random forcing. In the context of nonlinear optics, this might represent a transmission error where the propagation model is the nonlinear Schr¨odinger equation (NLSE) with additive or multiplicative noise. A shortcoming of this method is that the efficiency of the IS technique depends strongly on the accuracy of the low-dimensional reduction used to guide the simulations. These low-dimensional reductions are often derived from a formal perturbation theory, referred to as soliton perturbation theory (SPT) for the case of soliton propagation under the forced NLSE. As demonstrated here, such reduction methodsare often inadequate in their description of the pulse\u27s dynamics. In particular, the interaction between a propagating pulse and dispersive radiation leads to a radiation-induced drift in a pulse\u27s phase, which is largely unaccounted for in the reduced systems currently in use. The first part of this dissertation is devoted to understanding the interaction between a pulse and dispersive radiation, leading to the derivation of an improved reduced system based on a variational approach. Once this system is derived and verified numerically, it serves as the basis for an improved IS method that incorporates the dynamics of the radiation, which is subsequently extended to more realistic propagation models. Of particular interest is the case of the NLSE with a periodic modulation of the dispersion constant, referred to as dispersion management (DM), and a related model where this modulation is averaged to give an autonomous, nonlocal equation. Following the nomenclature commonly use in literature, the former (nonautonomous) equation will be referred to as the NLSE+DM and the latter (autonomous) equation as the DMNLSE. A complicating aspect of these more realistic models is that, unlike the NLSE, exact solutions only exist as numerical objects rather than as closed-form solutions, which introduces an addition source of error in the derivation of a reduced system for the pulse dynamics. In the second part of this dissertation, the IS method is extended to the calculation of phase-slip probabilities in mode-locked lasers (MLL). Realistic models for pulse propagation in MLL include the dissipative effects of gain and loss, in addition to nonlocal saturation effects. As a result most of the reduced systems derived for pulse dynamics are extremely complicated, which diminishes their applicability as guides for IS simulations. Therefore, a MLL operating in the soliton propagation regime is considered, where the effects of gain, loss and saturation are treated perturbatively. A simple reduced system for the pulse dynamics is derived for this MLL model, allowing the IS technique to be effectively applied

An efficient nonlinear circuit simulation technique

This paper proposes a novel method for the analysis and simulation of integrated circuits (ICs) with the potential to greatly shorten the IC design cycle. The circuits are assumed to be subjected to input signals that have widely separated rates of variation, e.g., in communication systems, an RF carrier modulated by a low-frequency information signal. The proposed technique involves two stages. Initially, a particular order result for the circuit response is obtained using a multiresolution collocation scheme involving cubic spline wavelet decomposition. A more accurate solution is then obtained by adding another layer to the wavelet series approximation. However, the novel technique presented here enables the reuse of results acquired in the first stage to obtain the second-stage result. Therefore, vast gains in efficiency are obtained. Furthermore, a nonlinear model-order reduction technique can readily be used in both stages making the calculations even more efficient. Results will highlight the efficacy of the proposed approac

Convex optimization methods for model reduction

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 153-161).Model reduction and convex optimization are prevalent in science and engineering applications. In this thesis, convex optimization solution techniques to three different model reduction problems are studied.Parameterized reduced order modeling is important for rapid design and optimization of systems containing parameter dependent reducible sub-circuits such as interconnects and RF inductors. The first part of the thesis presents a quasi-convex optimization approach to solve the parameterized model order reduction problem for linear time-invariant systems. Formulation of the model reduction problem as a quasi-convex program allows the flexibility to enforce constraints such as stability and passivity in both non-parameterized and parameterized cases. Numerical results including the parameterized reduced modeling of a large RF inductor are given to demonstrate the practical value of the proposed algorithm.A majority of nonlinear model reduction techniques can be regarded as a two step procedure as follows. First the state dimension is reduced through a projection, and then the vector field of the reduced state is approximated for improved computation efficiency. Neither of the above steps has been thoroughly studied. The second part of this thesis presents a solution to a particular problem in the second step above, namely, finding an upper bound of the system input/output error due to nonlinear vector field approximation. The system error upper bounding problem is formulated as an L2 gain upper bounding problem of some feedback interconnection, to which the small gain theorem can be applied. A numerical procedure based on integral quadratic constraint analysis and a theoretical statement based on L2 gain analysis are given to provide the solution to the error bounding problem. The numerical procedure is applied to analyze the vector field approximation quality of a transmission line with diodes.(Cont) The application of Volterra series to the reduced modeling of nonlinear systems is hampered by the rapidly increasing computation cost with respect to the degrees of the polynomials used. On the other hand, while it is less general than the Volterra series model, the Wiener-Hammerstein model has been shown to be useful for accurate and compact modeling of certain nonlinear sub-circuits such as power amplifiers. The third part of the thesis presents a convex optimization solution technique to the reduction/identification of the Wiener-Hammerstein system. The identification problem is formulated as a non-convex quadratic program, which is solved by a semidefinite programming relaxation technique. It is demonstrated in the thesis that the formulation is robust with respect to noisy measurement, and the relaxation technique is oftentimes sufficient to provide good solutions. Simple examples are provided to demonstrate the use of the proposed identification algorithm.by Kin Cheong Sou.Ph.D

An efficient wavelet-based nonlinear circuit simulation technique with model order reduction

This paper proposes further improvement to a novel method for the analysis and simulation of ICs recently proposed by the authors. The circuits are assumed to be subjected to input signals that have widely separated rates of variation, e.g. in communication systems an RF carrier modulated by a low-frequency information signal. The previously proposed technique enables the reuse of the results obtained using a lower-order accuracy model to calculate a response of higher-order accuracy model. In this paper, the efficiency of this method is further improved by using a nonlinear model order reduction technique. Results highlight the efficiency of the proposed approach

On the Selection of Tuning Methodology of FOPID Controllers for the Control of Higher Order Processes

In this paper, a comparative study is done on the time and frequency domain tuning strategies for fractional order (FO) PID controllers to handle higher order processes. A new fractional order template for reduced parameter modeling of stable minimum/non-minimum phase higher order processes is introduced and its advantage in frequency domain tuning of FOPID controllers is also presented. The time domain optimal tuning of FOPID controllers have also been carried out to handle these higher order processes by performing optimization with various integral performance indices. The paper highlights on the practical control system implementation issues like flexibility of online autotuning, reduced control signal and actuator size, capability of measurement noise filtration, load disturbance suppression, robustness against parameter uncertainties etc. in light of the above tuning methodologies.Comment: 27 pages, 10 figure

Balanced truncation of perturbative representations of nonlinear systems

The paper presents a novel approach for a balanced truncation style of model reduction of a perturbative representation of a nonlinear system. Empirical controllability and observability gramians for nonlinear systems are employed to define a projection matrix. However, the projection matrix is applied to the perturbative representation of the system rather than directly to the exact nonlinear system. This is to achieve the required increase in efficiency desired of a reduced-order model. Application of the new method is illustrated through a sample test-system. The technique will be compared to the standard approach for reducing a perturbative representation of a nonlinear system

'Constant in gain Lead in phase' element - Application in precision motion control

This work presents a novel 'Constant in gain Lead in phase' (CgLp) element using nonlinear reset technique. PID is the industrial workhorse even to this day in high-tech precision positioning applications. However, Bode's gain phase relationship and waterbed effect fundamentally limit performance of PID and other linear controllers. This paper presents CgLp as a controlled nonlinear element which can be introduced within the framework of PID allowing for wide applicability and overcoming linear control limitations. Design of CgLp with generalized first order reset element (GFORE) and generalized second order reset element (GSORE) (introduced in this work) is presented using describing function analysis. A more detailed analysis of reset elements in frequency domain compared to existing literature is first carried out for this purpose. Finally, CgLp is integrated with PID and tested on one of the DOFs of a planar precision positioning stage. Performance improvement is shown in terms of tracking, steady-state precision and bandwidth