Digital Filter Design Using Improved Artificial Bee Colony Algorithms

Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems.There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the-art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm is used to design IIR lowpass, highpass and bandpass filters, and the results are compared to three state-of-the-art design methods. Based on the obtained design results, the four design algorithms are shown to be competitive as compared to the state-of-the-art design methods

A Novel Fractional Order Fuzzy PID Controller and Its Optimal Time Domain Tuning Based on Integral Performance Indices

A novel fractional order (FO) fuzzy Proportional-Integral-Derivative (PID) controller has been proposed in this paper which works on the closed loop error and its fractional derivative as the input and has a fractional integrator in its output. The fractional order differ-integrations in the proposed fuzzy logic controller (FLC) are kept as design variables along with the input-output scaling factors (SF) and are optimized with Genetic Algorithm (GA) while minimizing several integral error indices along with the control signal as the objective function. Simulations studies are carried out to control a delayed nonlinear process and an open loop unstable process with time delay. The closed loop performances and controller efforts in each case are compared with conventional PID, fuzzy PID and PI{\lambda}D{\mu} controller subjected to different integral performance indices. Simulation results show that the proposed fractional order fuzzy PID controller outperforms the others in most cases.Comment: 30 pages, 20 figure

Controller Design for Fractional-Order Systems

In recent time, the application of fractional derivatives has become quite apparent in modeling mechanical and electrical properties of real materials. Fractional integrals and derivatives has found wide application in the control of dynamical systems, when the controlled system or/and the controller is described by a set of fractional order differential equations. In the present work a fractional order system has been represented by a higher integer order system, which is further approximated by second order plus time delay (SOPTD) model. The approximation to a SOPTD model is carried out by the minimization of the two norm of the actual and approximated system. Further, the effectiveness of a fractional order controller in meeting a set of frequency domain specifications is determined based on the frequency response of an integer order PID and a fractional order PID (FOPID) controller, designed for the approximated SOPTD model. The advent of fuzzy logic has led to greater flexibility in designing controllers for systems with time varying and nonlinear characteristics by exploiting the system observations in a linguistic manner. In this regard, a fractional order fuzzy PID controller has been developed based on the minimization different optimal control based integral performance indices. The indices have been minimized using genetic algorithms. Simulation results show that the fuzzy fractional order PID controller is able to outperform the classical PID, fuzzy PID and FOPID controllers

Acoustic Measurement of Snow

Instrumentation commonly used to measure snowpack stratigraphy, snow density, Snow Water Equivalent (SWE), temperature and liquid water content is usually invasive and requires disruption of the snowpack. Most measurement techniques modify the snow medium and more than one sample cannot be taken at the same location. This does not permit continuous monitoring of these parameters using a single measurement instrument. An acoustic wave sent into the snowpack was used to measure snow. To provide the theory required to make acoustic measurements, the Biot-Stoll model of sound wave propagation in porous media was modified using a mixture theory so that it was applicable to a multiphase porous medium. The combined model is called the Unified Thermoacoustic Model (UTAM) for snow. An acoustic measurement device, the System for the Acoustic Sensing of Snow (SAS2), was designed to send sound waves into snow and to receive the reflected sound waves using a loudspeaker and a microphone array. A stationary version of the SAS2 was deployed on a met station and a portable version of the SAS2 was placed on a roving ski-based platform. The systems were deployed at field sites in the Canadian Rocky Mountains, Alberta. The results showed that the SAS2 was able to measure snow density, temperature, and liquid water content and serve as a replacement technology for snowtube and snowpit measurements. Snow density was estimated more accurately by the SAS2 than from commonly-used snow tube techniques

Design and Analysis of Electric Powertrains for Offshore Drilling Applications

Doktorgradsavhandling ved Institutt for ingeniørvitenskap, Universitetet i Agder, 2016The global energy market is challenged with an ever increasing need for resources to meet the growing demands for electric power, transportation fuels, etc. Although we witness the expansion of the renewable energy industry, it is still the fossil fuels, with oil and gas dominating the scene of global energy supply sector, that provide majority of worldwide power generation.However, many of the easily accessible hydrocarbon reserves are depleted which requires from the producers of drilling equipment to focus on cost-effective operations and technology to compete in a challenging market. Particularly high level of activity is observed in both industry and academia in the field of electrical actuation systems of drilling machines, as control methods of alternating current (AC) motor drives have become an industrially mature technology over the past few decades. In addition, state-of-the-art AC motors manufacturing processes allow to conform to the strict requirements for safe operation of electrical equipment in explosive atmospheres. These two main reasons made electric actuation systems a tough competitor to hydraulic powertrains used traditionally by the industry. However, optimal design of induction motor drives and systematic analysis of factors associated with operation in harsh offshore conditions are still considered as a major challenge. In this thesis, effective methods for design and analysis of induction motor drives are proposed, including aspects of optimization and simulation-based engineering. The first part of the thesis is devoted to studying methods for modeling, control, and identification of induction machines operating in offshore drilling equipment with the focus to improve their reliability, extend lifetime, and avoid faults and damage, whereas the second part introduces more general approaches to the optimal selection of components of electric drivetrains and to the improvement of the existing dimensioning guidelines. A multidisciplinary approach to design of actuation systems is explored in this thesis by studying the areas of motion control, condition monitoring, and thermal modeling of electric powertrains with an aspiration to reach the level of design sophistication which goes beyond what is currently considered an industrial standard. We present a technique to reproduce operation of a full-scale offshore drilling machine on a scaled-down experimental setup to estimate the mechanical load that the designed powertrain must overcome to meet the specification requirements. The same laboratory setup is used to verify the accuracy of the estimation and control method of an induction motor drive based on the extended Kalman filter (EKF) to confirm that the sensorless control techniques can reduce the number of data acquisition devices in offshore machines, and thus decrease their failure rate without negatively affecting their functionality. To address the challenge of condition monitoring of induction motor drives, we propose a technique to assess the expected lifetime of electric drivetrain components when subjected to the desired duty cycles by comparing the effects of a few popular motion control signals on the cumulative damage and vibrations. As a result, the information about the influence of a given control strategy on drivetrain lifecycle is made available early in the design stage which can significantly affect the choice of the optimal powertrain components. The results show that some of the techniques that have a well-proven track record in other industries can be successfully applied to solve challenges associated with operation of offshore drilling machines. One of the most essential contributions of this thesis, optimal selection of drivetrain components, is based on formulating the drivetrain dimensioning problem as a mixed integer optimization program. The components of powertrain that satisfy the design constraints and are as cost-effective as possible are found to be the global optimum, contrary to the functionality offered by some commercially available drivetrain sizing software products. Another important drawback of the dimensioning procedures recommended by the motor drives manufacturers is the inability to assess if the permissible temperature limits given in the standards do not become violated when the actuation system experiences overloads different than these tabulated in the catalogs. Hence, the second most significant contribution is to propose a method to monitor thermal performance of induction motor drives that is based exclusively on publicly available catalog data and allows for evaluating whether the standard thermal performance limits are violated or not under arbitrary load conditions and at any ambient temperature. Both these solutions can effectively enrich the industrially accepted dimensioning procedures to satisfy the level of conservatism that is demanded by the offshore drilling business but, at the same time, provide improved efficiency and flexibility of the product design process and guarantee optimality (quantitatively, not qualitatively, measurable) of the final solution. An attractive direction for additional development is to further integrate knowledge from different fields relevant to electric powertrains to enable design of tailored solutions without compromising on their cost and performance