Parameter Adaptation Algorithm Research Articles

An Adaptive Convergence-Trajectory Controlled Ant Colony Optimization Algorithm With Application to Water Distribution System Design Problems

Evolutionary algorithms and other meta-heuristics have been employed widely to solve optimization problems in many different fields over the past few decades. Their performance in finding optimal solutions often depends heavily on the parameterization of the algorithm’s search operators, which affect an algorithm’s balance between search diversification and intensification. While many parameter-adaptive algorithms have been developed to improve the searching ability of meta-heuristics, their performance is often unsatisfactory when applied to real-world problems. This is, at least in part, because available computational budgets are often constrained in such settings due to the long simulation times associated with objective function and/or constraint evaluation, thereby preventing convergence of existing parameter-adaptive algorithms. To this end, this paper proposes an innovative parameter-adaptive strategy for ant colony optimization (ACO) algorithms based on controlling the convergence trajectory in decision space to follow any prespecified path, aimed at finding the best possible solution within a given, and limited, computational budget. The utility of the proposed convergence-trajectory controlled ACO (ACO $_{\mathbf{CTC}}$ ) algorithm is demonstrated using six water distribution system design problems (WDSDPs, a difficult type of combinatorial problem in water resources) with varying complexity. The results show that the proposed ACO $_{\mathbf{CTC}}$ successfully enables the specified convergence trajectories to be followed by automatically adjusting the algorithm’s parameter values. Different convergence trajectories significantly affect the algorithm’s final performance (solution quality). The trajectory with a slight bias toward diversification in the first half and more emphasis on intensification during the second half of the search exhibits substantially improved performance compared to the best available ACO variant with the best parameterization (no convergence control) for all WDSDPs and computational scenarios considered. For the two large-scale WDSDPs, new best-known solutions are found by the proposed ACO $_{\mathbf{CTC}}$ .

Adaptive direct torque control using Luenberger-sliding mode observer for online stator resistance estimation for five-phase induction motor drives

Direct torque control is considered as one of the variable structure control techniques which are characterized by fast response, simplicity and provide direct control of both electromagnetic torque and stator flux by adequate selection of the inverter switches in each sampling period. In case of multiphase motor drives, the increase in voltage vectors offers flexibility to optimize the selection of the inverter switching states, thereby achieving more precise control of the torque and flux. Nevertheless, the criterion for the selection of the inverter states becomes more complex. This aspect is not considered an issue in the traditional three-phase motor drives but needs to be considered in designing the switching table of the direct torque control of five-phase induction motor. Due to the auxiliary vector plane, the low-frequency harmonics need to be eliminated and full utilization of the dc link voltage is desired. The effects of parameter variation (particularly, stator resistance) on the performance of the direct torque control. It is necessary the addition of parameter adaptation algorithm to compensate this effects. A novel direct torque control of five-phase induction motor using a new switching table combined with an adaptive variable structure observer to avoid the effects of stator resistance variations is presented in this paper. Simulation results provided show the effectiveness of the proposed control strategy.

Modified Parameters of Harmony Search Algorithm for Better Searching

The scheduling and rostering problems are deliberated as integrated due to they depend on each other whereby the input of rostering problems is a scheduling problems. In this research, the integrated scheduling and rostering bus driver problems are defined as maximising the balance of the assignment of tasks in term of distribution of shifts and routes. It is essential to achieve is fairer among driver because this can bring to increase in driver levels of satisfaction. The latest approaches still unable to address the fairness problem that has emerged, thus this research proposes a strategy to adopt an amendment of a harmony search algorithm in order to address the fairness issue and thus the level of fairness will be escalate. The harmony search algorithm is classified as a meta-heuristics algorithm that is capable of solving hard and combinatorial or discrete optimisation problems. In this respect, the three main operators in HS, namely the Harmony Memory Consideration Rate (HMCR), Pitch Adjustment Rate (PAR) and Bandwidth (BW) play a vital role in balancing local exploitation and global exploration. These parameters influence the overall performance of the HS algorithm, and therefore it is crucial to fine-tune them. The contributions to this research are the HMCR parameter using step function while the fret spacing concept on guitars that is associated with mathematical formulae is also applied in the BW parameter. The model of constant step function is introduced in the alteration of HMCR parameter. The experimental results revealed that our proposed approach is superior than parameter adaptive harmony search algorithm. In conclusion, this proposed approach managed to generate a fairer roster and was thus capable of maximising the balancing distribution of shifts and routes among drivers, which contributed to the lowering of illness, incidents, absenteeism and accidents.

Adaptive Loop Shaping for Wideband Disturbances Attenuation in Precision Information Storage Systems

Modern hard disk drive (HDD) systems are subjected to various external disturbances. One particular category, defined as wide-band disturbances, can generate vibrations with their energy highly concentrated at several frequency bands. Such vibrations are commonly time-varying and strongly environment/product-dependent, and the wide spectral peaks can occur at frequencies above the servo bandwidth. This paper considers the attenuation of such challenging vibrations in the track-following problem of HDDs. Due to the fundamental limitation imposed by the Bode’s integral theorem, the attenuation of such wide-band disturbances may cause unacceptable amplifications at other frequencies. To achieve a good performance and an optimal tradeoff, an add-on adaptive vibration-compensation scheme is proposed in this paper. Through parameter adaptation algorithms that online identify both the center frequencies and the widths of the spectral peaks, the proposed control scheme automatically allocates the control efforts with respect to the real-time disturbance characteristics. The effect is that the position error signal in HDDs can be minimized with effective vibration cancelation. Evaluation of the proposed algorithm is performed by experiments on a voice-coil-driven flexible positioner system.

Robust Adaptive Sensorless Control for Permanent-Magnet Synchronous Motors

A sensorless algorithm is developed on the basis of a rotor flux observer in the stationary frame. In particular, it involves a parameter adaptive algorithm for an initial rotor flux like the observer, which was recently proposed by Bobtsov et al. (Automatica, vol. 61, Nov. 2015). In the proposed method, the flux observer is linked to the parameter estimator via a compensating term that results from parameter error. This method has a robust property against dc bias errors, i.e., it cures the inherent weakness of the pure integrator (flux observer) to dc offsets that frequently occur in current measurements and voltage estimates. The robust performance is demonstrated through simulations and experimental results.

A robust optimal design for strictly positive realness in recursive parameter adaptation

SummaryThis paper provides an optimization‐based approach to assure the strict positive real (SPR) condition in a set of recursive parameter adaptation algorithms (PAA). The developed algorithms and tools enable a multiobjective formulation of the SPR problem, creating new controls of the stability and parameter convergence in PAAs. In addition to assuring the SPR condition for global stability in PAAs, we provide an algorithmic solution for uniform convergence under performance constraints in PAAs. Several new aspects of parameter convergence were observed with the adoption of the algorithm in a narrow‐band identification problem. The proposed algorithm is verified in simulation and experiments on a precision motion control platform in advanced manufacturing.

An Adaptive Disturbance Observer for Precision Control of Time-Varying Systems

Abstract: In this paper, a new adaptive disturbance observer (ADOB) approach is introduced to overcome the inherent interference problem between a disturbance observer (DOB) and a parameter adaptation algorithm (PAA); the PAA is applied to estimate the parameters of the nominal plant model inverse, such that the disturbance estimated by the DOB is remained small. Namely, the proposed ADOB is equivalent to an adaptive control method that recursively minimizes the l 2 norm of the disturbance estimated by a DOB. Since the bounded l 2 norm of the disturbance estimate can be interpreted as the guaranteed stability of the DOB loop, the stability of the proposed ADOB can be proved theoretically, as well as practically. In addition, the proposed method directly seeks the parameters of a nominal plant model inverse, and thus the model inversion process is no longer necessary. The proposed ADOB is verified by theoretical analyses and experimental results in this paper.

Multi Objective Design Optimization of Helical Gear Pair Using Adaptive Parameter Harmony Search Algorithm

Mechanical design engineers design products by selecting the best possible materials and geometries that satisfies the specific operational requirements of the design. It involves lot of creativity and aesthetics to make better designs. A gear design makes the designer to compromise many design variables so as to arrive the best performance of a gear set. The best possible way for multi variable, Multiobjective gear design is to try design optimization. For many complex engineering optimization problems multi objective design optimization methods are used to simplify the design problem. In this paper, multiobjective design of helical gear pair transmission with objective functions namely volume of the small and large helical gear and opposite number of overlap ratio is taken into account. The design variables considered are normal module, helix angle, gear width coefficient and teeth number of small helical gear. A recent meta-heuristic algorithm namely parameter adaptive harmony search algorithm is applied to solve this problem using the weighted sum approach. It is evident from the results that the proposed approach is performing better than other algorithms.

An adaptive flux observer for the permanent magnet synchronous motor

SummaryA gradient descent‐based nonlinear observer for surface‐mount permanent magnet synchronous motors with remarkable stability properties was recently proposed. A key assumption for the derivation of the observer is the knowledge of the electrical parameters, which are usually uncertain. In the present paper, we propose a robust adaptive flux observer adding immersion and invariance parameter adaptation algorithms to estimate the stator resistance. Global boundedness of all signals and convergence to a residual set of the flux estimation error is guaranteed. The performance of the new adaptive observer is assessed with realistic simulations. Copyright © 2015 John Wiley & Sons, Ltd.

Synthesis and optimization of utility system using parameter adaptive differential evolution algorithm

Synthesis and optimization of utility system usually involve grassroots design, retrofitting and operation optimization, which should be considered in modeling process. This paper presents a general method for synthesis and optimization of a utility system. In this method, superstructure based mathematical model is established, in which different modeling methods are chosen based on the application. A binary code based parameter adaptive differential evolution algorithm is used to obtain the optimal configuration and operation conditions of the system. The evolution algorithm and models are interactively used in the calculation, which ensures the feasibility of configuration and improves computational efficiency. The capability and effectiveness of the proposed approach are demonstrated by three typical case studies.

Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.

Parametric detection in non-stationary correlated clutter using a sequential approach

Sequential detection provides a powerful solution to minimize the required number of observations for a given performance. Due to the non-stationary nature of clutter, this problem is recurrent in radar applications. In this paper, we develop a sequential parametric adaptive detection algorithm based on the approximation of clutter as an autoregressive process. Stationary segments are considered where both space and time windows are minimized, respectively, by using one secondary cell on each side of the cell under test and by applying a sequential test. We derive the distributions of the considered test statistic and give a closed form expression for the upper threshold whereas, the lower one is given as a simply numerical solution of a proper equation, rather than use the commonly Monte Carlo method based ones. The proposed approach is compared to an existing method based on a fixed sample size. Results obtained using synthetic and real data show that the proposed scheme reduces substantially the required sample size with detection performance close to that of the fixed sample size method.

Combining Structure and Parameter Adaptation of HMMs for Printed Text Recognition.

We present two algorithms that extend existing HMM parameter adaptation algorithms (MAP and MLLR) by adapting the HMM structure. This improvement relies on a smart combination of MAP and MLLR with a structure optimization procedure. Our algorithms are semi-supervised: to adapt a given HMM model on new data, they require little labeled data for parameter adaptation and a moderate amount of unlabeled data to estimate the criteria used for HMM structure optimization. Structure optimization is based on state splitting and state merging operations and proceeds so as to optimize either the likelihood or a heuristic criterion. Our algorithms are successfully applied to the recognition of printed characters by adapting the HMM character models of a polyfont printed text recognizer to new fonts. Our experiments involve a total of 1,120,000 real and 3,100,000 synthetic character images and concern a set of 89 HMM models. A comparison of our results with those of state-of-the-art adaptation algorithms (MAP and MLLR) shows a significant increase in the accuracy of character recognition.

An Adaptive Cauchy Differential Evolution Algorithm with Bias Strategy Adaptation Mechanism for Global Numerical Optimization

Appropriately adapting mutation strategies is a challengeable problem of the literature of the Differential Evolution (DE). The Strategy adaptation Mechanism (SaM) can convert a control parameter adaptation algorithm to a strategy adaptation algorithm. To improve the quality of optimization result, the exploration property is important in the early stage of optimization process and the exploitation property is significant in the late stage of optimization process. To ensure these, we modified the SaM for strictly controlling a balance between the exploration and the exploitation properties, which called the bias SaM (bSaM). We extended the Adaptive Cauchy Differential Evolution (ACDE) by attaching the bSaM. We compared the bSaM with SaM and the bSaM extended ACDE with the state-of-the-art DE algorithms in various benchmark problems. The result of the performance evaluation showed that the bSaM and the bSaM extended ACDE performs better than SaM and the state-of-the-art DE algorithms not only unimodal but also multimodal benchmark problems.

Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems

This paper presents a parameter adaptive harmony search algorithm (PAHS) for solving optimization problems. The two important parameters of harmony search algorithm namely Harmony Memory Consideration Rate (HMCR) and Pitch Adjusting Rate (PAR), which were either kept constant or the PAR value was dynamically changed while still keeping HMCR fixed, as observed from literature, are both being allowed to change dynamically in this proposed PAHS. This change in the parameters has been done to get the global optimal solution. Four different cases of linear and exponential changes have been explored. The change has been allowed during the process of improvization. The proposed algorithm is evaluated on 15 standard benchmark functions of various characteristics. Its performance is investigated and compared with three existing harmony search algorithms. Experimental results reveal that proposed algorithm outperforms the existing approaches when applied to 15 benchmark functions. The effects of scalability, noise, and harmony memory size have also been investigated on four approaches of HS. The proposed algorithm is also employed for data clustering. Five real life datasets selected from UCI machine learning repository are used. The results show that, for data clustering, the proposed algorithm achieved results better than other algorithms.

An Active Disturbance Rejection Based Approach to Vibration Suppression in Two‐Inertia Systems

AbstractThis study concerns the resonance problems found in motion control, typically described in a two‐inertia system model as compliance between the motor and the load. We reformulate the problem in the framework of active disturbance rejection control (ADRC), where the resonance is assumed to be unknown and treated as disturbance, estimated and mitigated. This allows the closed‐loop bandwidth to go well beyond the resonant frequency, which is quite difficult using existing methods. In addition, such level of performance is achieved with minimum complexity in the controller design and tuning: no parameter estimation or adaptive algorithm is needed, and the controller is tuned by adjusting one parameter, namely, the bandwidth of the control loop. It is also shown that the proposed solution applies to both the velocity and position control problems, and the fact that ADRC offers an effective and practical motion control solution, in the presence of unknown resonant frequency within the bandwidth of the control system. Finally, frequency response analysis is performed where stability margin is obtained before the simulation results are verified in the hardware experiments.

Dynamic Parameter Adaptation for M-LWDF/M-LWWF Scheduling

M-LWWF/M-LWDF scheduling schemes have attracted much interest due to their ability to both stabilize queues whenever possible and control delay through parameter selection. However, a good implementation of these schedulers would require a mechanism to minimize the required fraction of the bandwidth while satisfying its stability and delay requirements. To the best of our knowledge, previous works on these scheduling policies did not address the problem of minimizing the bandwidth utilization while satisfying delay constraints. In this paper, we explore the solution of this problem using a joint bandwidth and weight adaptation approach. We characterize the problem solution space for M-LWWF and M-LWDF scheduling, assuming time-varying traffic. We also show that, starting from any point in the solution space, simple dynamic bandwidth and weight updates can surely lead to the convergence to the optimal operation point in this space. Based on these characteristics, we propose a dynamic parameter adaptation algorithm that is able to track the time-varying optimal operation points for dynamic traffic and channel conditions. Simulation results show the efficiency of our proposed algorithm in tracking the optimal operation points in dynamic traffic and channel settings.

Improving playability of Blu-ray disc drives by using adaptive suppression of repetitive disturbances

This paper deals with the problem of adaptive suppression of disturbances on Blu-ray disc drives servo-mechanisms. The proposed methodology is intended for enhancing playability of low quality media at higher rotational speeds. One of the main problems in controlling an optical disc drive concerns the control of the radial lens position which assures the disc playback. However, due to the rotary nature of the Blu-ray disc the most important sources of disturbances concern those that present periodic behaviour. The frequency of such disturbances is proportional to the disc speed and inversely depends on the radial lens position, making more difficult the task of the tracking-controller when a high speed playback is required. Here, a new parametric adaptation algorithm is proposed to reject the main narrow-band disturbances without increasing the controller bandwidth. The proposed scheme makes use of the internal model principle and the Youla–Kucera parameterization. The parametric adaptation algorithm together with the robust stability and performance analysis are part of the main contribution of this paper. An experimental-data study illustrates the behaviour of the high speed tracking control.

Adaptive Internal Model Control of a DC Motor Drive System Using Dynamic Neural Network

This work concerns the study of problems relating to the adaptive internal model control of DC motor in both cases conventional and neural. The most important aspects of design building blocks of adaptive internal model control are the choice of architectures, learning algorithms, and examples of learning. The choice of parametric adaptation algorithm for updating elements of the conventional adaptive internal model control shows limitations. To overcome these limitations, we chose the architectures of neural networks deduced from the conventional models and the Levenberg-marquardt during the adjustment of system parameters of the adaptive neural internal model control. The results of this latest control showed compensation for disturbance, good trajectory tracking performance and system stability.

PABM-EDCF: parameter adaptive bi-directional mapping mechanism for video transmission over WSNs

To support and keep high quality of video transmission over wireless sensor networks, this paper proposes a parameter adaptive bi-directional cross-layer mapping algorithm on the basis of the operation mechanism of IEEE 802.11e Enhanced Distributed Coordination Function (EDCF) supporting video service differentiation, named PABM-EDCF. Instead of classifying video data to a specific access category in 802.11e network, our proposed adaptive cross-layer scheme makes use of the hierarchy characteristic of video stream, dynamically maps video data to the appropriate access categories according to both the significance of the different video frames and the network traffic load. The significance passes from the application layer to the media access layer through a cross-layer architecture. In order to prevent the network congestion and keep the high transmission quality, the proposed algorithm adopts bi-directional floating mapping algorithm and congestion awareness mechanism based on the queue length and frame types. The mapping parameters are updated according to the network condition in time. Our simulation results indicate: the proposed method (a) improves the video transmission quality; (b) optimizes the management and utilization of queue resources; and (c) yields superior performance (under different loads) over 802.11e, static mapping and adaptive mapping schemes.