Bijan Sayyarrodsari

Estimation-based synthesis of H/sub /spl infin//-optimal adaptive equalizers over wireless channels

This paper presents a systematic synthesis procedure for H/sub /spl infin//-optimal adaptive FIR equalizers over a time-varying wireless channel. The channel is assumed to be frequency selective with Rayleigh fading. The proposed equalizer structure consists of the series connection of an adaptive FIR filter and a fixed equalizer (designed for the nominal channel). Adaptation of the weight vector of the adaptive FIR filter is achieved using the H/sub /spl infin//-optimal solution of an estimation-based interpretation of the channel equalization problem. Due to its H/sub /spl infin//-optimality, the proposed solution is robust to exogenous disturbances, and enables fast adaptation (i.e., a short training period) without compromising the steady-state performance of the equalization. Preliminary simulation are presented to support the above claims.

Fuzzy genetic controllers for the autonomous rendezvous and docking problem

Autonomous rendezvous and docking has been dlefined as one of the primary goals in today’s space technology. Autonomous operation of an unmanned space vehicle in a real world environment poses a series of problems. The kno,wledge about the environment is in general incomplete, uncertain and approximate. Perceptually acquired information is not precise, sensor’s noise introduces uncertainty and imprecision, sensor’s limited range and visibility introduces incompleteness. in this study, fuzzy logic and genetic algorithm (GA) have been applied to this problem in order to perform better in the case of all these problems. Fuzzy and GA combination imitates the role of human in the decision process. Background Information On Autonomous Rendezvous And Docking Technology The methodology presented in this research can be applied to any transportation problem. Some of the problems include decision making and evaluation of transportation system, transportation network design and traffic scheduling. Decision making and evaluation of a transportation system comprise of achieving multiple objectives using one of the alternate methods. All of these methods cannot satisfy all the objectives. Human decision making is required to weigh all the methods and choose the right alternative. Fuzzy logic with its role of human expert and GA being a strong search and optimization algorithm can mimic the human expert’s role. The problem can be formulated and GAfuzzy method can be applied to find the method that satisfies all the objectives optimally. In the transportation network design, GA-fuzzy method can be applied to find the shortest path or routes to be traversed between the different nodes (for example, bus terminus). GA has been used to solve the traveling salesman problem (TSP), gas pipeline and other combinatorial problems. GA and fuzzy have also been used to find the optimal solution for a lot of scheduling problems. “Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commerical advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is b) permission of the Association for Computing Machinery. To cop) otherwise, or to republish, requires a fee and/or specific permission.”

Genetic algorithm based gain scheduling

We designed a feedforward control law that greatly decreases the load sway of a shipboard crane due to ship rolling. This feedforward control uses measurements of ship rolling angle at each instant. At different operating points the optimal feedforward gain changes while is numerically computable. Here, we propose to use a genetic algorithm (GA) based approach to optimize the mapping of feedforward gain in four dimensional space. The process is based on the numerical calculation of the optimal feedforward gain for any rolling angle (/spl rho/), length of the rope (L), and luffing angle (/spl delta//sub 0/). The optimal gain is calculated for a group of points in the working space and then fit a function of order n to these points in a four dimensional space. Our choice for this problem includes real value GA with a combination of different crossover methods. The cost function is the sum of squared errors at selected points and we aim to minimize it. Since moving the load to another location also changes the optimal gain, the new improved gain scheduling further reduces the swinging within the whole working space. GA is a directed search method and is capable of searching for variables of functions with any desired structure. The major advantages of using GA for function mappings is that the function does not have to be linear or in any specific form.

Estimation-based synthesis of H/spl infin/-optimal adaptive FIR filters for filtered-LMS problems

This paper presents a systematic synthesis procedure for H/spl infin/-optimal adaptive FIR filters in the context of an active noise cancellation (ANC) problem. An estimation interpretation of the adaptive control problem is introduced first. Based on this interpretation, an H/spl infin/ estimation problem is formulated, and its finite horizon prediction (filtering) solution is discussed. The solution minimizes the maximum energy gain from the disturbances to the predicted (filtered) estimation error and serves as the adaptation criterion for the weight vector in the adaptive FIR filter. We refer to this adaptation scheme as estimation-based adaptive filtering (EBAF). We show that the steady-state gain vector in the EBAF algorithm approaches that of the classical (normalized) filtered-X LMS algorithm. The error terms, however, are shown to be different. Thus, these classical algorithms can be considered to be approximations of our algorithm. We examine the performance of the proposed EBAF algorithm (both experimentally and in simulation) in an active noise cancellation problem of a one-dimensional (1-D) acoustic duct for both narrowband and broadband cases. Comparisons to the results from a conventional filtered-LMS (FxLMS) algorithm show faster convergence without compromising steady-state performance and/or robustness of the algorithm to feedback contamination of the reference signal.

An estimation-based approach to the design of adaptive IIR filters

We present an estimation-based approach to the design of adaptive IIR filters. We also use this approach to design adaptive filters when a feedback signal from the output of the adaptive filter contaminates the reference signal. We use an H/sub /spl infin// criterion to cast the problem as a nonlinear H/sub /spl infin// filtering problem, and present an approximate linear H/sub /spl infin// filtering solution. This linear filtering solution is then used to adapt the adaptive IIR Filter. The presentation of the proposed adaptive algorithm is done in the context of an adaptive active noise cancellation problem. Simulations are used to examine the performance of the proposed estimation-based adaptive algorithm.

A theoretical justification for nonlinear control property of a class of fuzzy logic controllers

In Sayyarrdsari, Homaifar, and Hogans (1993), a hybrid implementation of fuzzy and conventional PID controller was introduced and its application to a 2 degree of freedom robot manipulator arm was examined. A theoretical justification for that approach, based on the fact that “fuzzy systems are universal approximators” is presented in this paper.

Fuzzy controller for robot arm trajectory

Abstract Reports on successful applications of fuzzy logic controllers (FLCs) are no longer rare. Regardless of the application domain, the main idea is to convert a linguistic control scenario into an automatic control strategy. The experts knowledge is the backbone of this linguistic control strategy. FLCs have their most successful implementations where the process under control is too complex for analysis by conventional quantitative methods and, therefore, conventional controllers face serious shortcomings. This paper proposes a “hybrid” implementation of FLCs and conventional PID controllers that can be helpful in some applications. The proposed method is applied to a 2 degree of freedom robot arm with promising results.

Industrial application of nonlinear model predictive control technology for fuel ethanol fermentation process

There are currently 134 ethanol biorefineries in the United States with a production capacity of nearly 7.2 billion gallons per year, with an additional 6.2 billion gals per year capacity under the construction [1]. Approximately two thirds of these are dry-mill production facilities.

Extrapolating gain-constrained neural networks - effective modeling for nonlinear control

Nonlinear model predictive control (NLMPC) is now a widely accepted control technology in many industrial applications. Since the quality of the model of a physical non-linear process plays a critical role in the successful development, deployment, and maintenance of a NLMPC application, the mathematical representation of such models has been the subject of significant research in both academia and industry. In this paper, extrapolating gain-constrained neural networks (EGCN) is described as a key component of a NLMPC technology that has been in use in more than 100 industrial applications over the past 7 years. Simulation results are presented which compare EGCN models to traditional neural network training methods as well as to the recently proposed bounded-derivative network (BDN). These results highlight the critical advantages of EGCN in nonlinear process modeling for optimization and control applications and underscore the effectiveness of EGCN models in providing guarantees on global gain-bounds without compromising accurate representation of available process data.

Hierarchical Learning-Based Design of a Hybrid Fuzzy Pid Controller

ABSTRACTThe development of a systematic design procedure has been of interest since the introduction of fuzzy logic controllers (FLCs). As the dimensions of the systems input and output spaces become larger, the design problem becomes exponentially more difficult. Partitioning the input space into smaller, more manageable regions simplifies the design procedure. The problem then becomes one of integrating the regional controllers designed for each input space partition. This article demonstrates a systematic and hierarchical approach to the design of a hybrid fuzzy-PID controller through the application of a learning-based algorithm. The proposed method is applied to a two-link robotic arm. Favorable simulation results are obtained when comparing our approach to fixed PID Control and Variable Structure Control.