Parameter Adjustment Mechanism Research Articles

A Novel Adaptive Multi-Objective Particle Swarm Optimization Based on Decomposition and Dominance for Long-term Generation Scheduling of Cascade Hydropower System

Multi-objective long-term generation scheduling (MLGS) considering ecological flow demands is important for comprehensive utilization of water resources in cascade hydropower system (CHS). A novel adaptive multi-objective particle swarm optimization based on decomposition and dominance (D2AMOPSO) is developed in this paper to solve the MLGS problem. In D2AMOPSO, a constraint handling method based on repair strategy and individualconstraints and group constraints (ICGC) technique is embedded to address various constraints. An improved logistic map is adopted to initialize the population. During the evolutionary process, an improved Tchebycheff decomposition is introduced to select personal best and global best for each particle, and the non-dominated solutions found so far are stored in an external archive where crowding distance and elitist learning strategy are performed to improve its diversity. Meanwhile, an adaptive flight parameter adjustment mechanism based on Pareto entropy is adopted to balance the global exploration and local exploitation abilities of the population. A normal cloud mutation operator is used to keep the population diversity and escape local minima. In the case study of the Three Gorges Cascade hydropower system (TGC) under three typical years, the results of the proposed method and other four competitors show that D2AMOPSO can obtain better diversity and faster convergence solutions for the MLGS problem in less time.

Study of tunable and enhanced transition radiation from graphene in terahertz band

Terahertz (THz) radiation has many special properties, such as low quantum energy, high signal-to-noise ratio and wide frequency, so that it possesses important application prospects in various fields. With the development of THz technology and its application fields, it is an urgent need to develop all types of terahertz source. It is as well-known that the radiation generated by traditional electronics methods has high power with low frequency, while by traditional photonics method has relatively high frequency with low power. Fortunately, the method of generating radiation based on the combination of photonics and electronics can overcome the above disadvantages, so it can solve the problem encountered by the terahertz source. In this paper, the integrated method by using electron beam to excite surface plasmon waves and generate transition radiation is adopted to obtain terahertz waves. Although the output power by this method is not remarkably high, the method is flexible and easy to implement. Due to the merit of graphene plasmon in THz band, a physical model of a moving electron incidence on a monolayer graphene at arbitrary angle is established in this paper to generate transition radiation in THz band. The field of the transition radiation is fully deduced by Maxwell’s equations combined with the special boundary conditions due to graphene’s conductivity. Then the expression of spectral-angular distribution is also derived in order to show the radiation energy characteristic more intensively in three-dimensional space. The characteristic of the radiation is numerically calculated by MATLAB. We focus on the transition radiation generated by a charged particle with a moderate energy and explain some contributions to the radiation, such as the incident angle, the conductivity or the Fermi level in graphene and the substrate dielectric characteristic. The pattern and intensity of the radiation field are numerically analyzed in the upper and lower half-spaces. We plot the figures of field distribution and the spectral angular distribution related to these variables mentioned above to analyze their influences on the radiation. In the case of the normal incidence, the transition radiation exhibits strong symmetry and considerably more intensive than that at grazing and oblique incidence. The results of the spectral angular distribution display that the radiation intensity increases strongly with the incident angle, and that the radiation energy focuses in backward and forward directions in the case of normal incidence. This study summarizes that the parameter adjustment mechanism is favorable for the performance of terahertz source. Since the conductivity of graphene can be adjusted by applying a bias voltage and doping, the radiation frequency and intensity can be tuned by the conductivity of graphene. Meanwhile, the substrate, electron energy and incident angle are conveniently controlled, therefore a more flexible way is obtained to develop tunable terahertz radiation sources.

A Hybrid Control Strategy for Real-Time Control of the Iron Removal Process of the Zinc Hydrometallurgy Plants

As part of the zinc hydrometallurgy plant, the iron removal process is a complex system with four cascaded reactors. Tighter process-index control is difficult to achieve due to the complicated, long, and time-varying removal process. The control performance is also affected by the quality of the ore source and external disturbances. Little research is documented in the literature to address these difficulties and manual control is widely used. An innovative hybrid control strategy is developed to control the iron removal process’ indices within narrow ranges with minimum cost of additive regents, including oxygen and zinc oxide. This strategy is composed of an optimal setting model, a model-based optimal controller, an integrated prediction model, a fuzzy-logic-based feedforward compensator, and a model feedback adjustor. The optimal setting model automatically optimizes the set-points of the process indices under different production conditions. To achieve the process requirements with minimal cost, the model-based optimal controller is designed. The integrated prediction model is established to provide a more accurate on-line prediction of the process indices by integrating the mechanism prediction model and an error compensation model based on the least-square support vector machine. Based on the predicted process indices, the compensator is developed for the optimal controller. The adjustor provides a parameter adjustment mechanism. Four-week-long industrial experiments in the largest zinc hydrometallurgy plant in China show that the control strategy can not only improve the process-indexes control performance, but also save 6.55% oxygen and 4.61% zinc oxide consumptions, which translates to 222 858 m3 oxygen and 1236 t zinc oxide per year (a saving of about $570 000). The hybrid control strategy can be extended to cover other similar processes in the zinc hydrometallurgy and other industries.

Search point ranking‐based adaptive cuckoo search

In this letter, an adaptive Cuckoo Search with a parameter adjustment mechanism of each search point by using parameters of the search points variously and ranking of search points is proposed. The neighborhood area of the search point of the proposed method differs depending on its ranking. The search performance of the proposed method is verified through numerical simulations for typical benchmark problems. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism

Firefly algorithm (FA) is a new swarm intelligence optimization algorithm, which has shown an effective performance on many optimization problems. However, it may suffer from premature convergence when solving complex optimization problems. In this paper, we propose a new FA variant, called NSRaFA, which employs a random attraction model and three neighborhood search strategies to obtain a trade-off between exploration and exploitation abilities. Moreover, a dynamic parameter adjustment mechanism is used to automatically adjust the control parameters. Experiments are conducted on a set of well-known benchmark functions. Results show that our approach achieves much better solutions than the standard FA and five other recently proposed FA variants.

A Neural Network Approach for Tracking Control of Uncertain Switched Nonlinear Systems with Unknown Dead-Zone Input

This paper is concerned with adaptive neural tracking control problem for uncertain switched nonlinear systems with unknown dead-zone input. Multilayer neural networks (MNNs) are employed to approximate unknown nonlinear functions, and an adaptive neural network controller is introduced to enhance system robustness. With the proposed control scheme, boundedness of all the signals of the closed-loop system is established regardless of the parameter adjustment mechanism, and better tracking control performance can eventually be achieved in view of the universal approximation capability of MNNs. Also, a switching signal is suitably defined using average dwell-time technique. By using a switching control scheme, it is demonstrated that the transient performance and stability can be simultaneously obtained. Finally, a simulation example is given to illustrate the effectiveness and validity of this approach.

Adaptive Control for Brushless DC Motor Based on Fuzzy Inference

Due to the nonlinearity of brushless direct current (BLDC) motor, it is difficult to obtain satisfied control characteristics using proportional integral derivative (PID) controller. A novel adaptive fuzzy control method based on high performance speed control is proposed in this paper, which combines an adaptive parameter adjustment mechanism with fuzzy controller to solve the problems of non-linearity, parameter variations and load excursions that occur in the BLDC motor drive system. The adaptive parameter adjustment mechanism can give better quantization and proportion factors of the fuzzy controller when there are variations in motor parameters or load, hence the fuzzy control rules are changed. The adaptive fuzzy control system is simulated in matlab with the changes of motor parameters and load, the control performance of the traditional PID controller is compared with the adaptive fuzzy controller. The comparison results indicate that the adaptive fuzzy control system has stronger robust and self-adaptive ability, faster response time, and zero overshoot and steady state error, which can satisfy the request of the BLDC motor control system. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4950

Wireless sensor networks for indoor air quality monitoring

The purpose of this study is to build an indoor air quality monitoring system based on wireless sensor networks (WSNs) technology. The main functions of the system include (1) remote parameter adjustment and firmware update mechanism for the sensors to enhance the flexibility and convenience of the system, (2) sensor nodes are designed by referring to the IEEE 1451.4 standard. This way, sensor nodes can automatically adjust and be plug and play, and (3) calibration method to strength the measurement value's sensitivity and accuracy. The experimental results show that transmission speed improves 30% than Trickle, transmission volume reduced to 42% of the original volume, updating task in 5*5 network topology can be executed 1.79 times and power consumption reduced to 30%. When baseline drifts, we can use the firmware update mechanism to adjust the reference value. The way can reduce error percentage from 15% to 7%.

Adaptive model predictive control for constrained nonlinear systems

In this paper, a method is proposed for the adaptive model predictive control of constrained nonlinear system. Rather than relying on the inherent robustness properties of standard NMPC, the developed technique explicitly account for the transient effect of parametric estimation error by combining a parameter adjustment mechanism with robust MPC algorithms. The parameter estimation routine employed guarantees non-increase of the estimation error vector. This means that the controller employs a process model which approaches that of the true system over time. These estimates are used to update the parameter uncertainty set at every time step, resulting in a gradual reduction in the conservative and/or computational effects of the incorporated robust features.

Model Reference Adaptive Tension Control in a Papermaking Machine

Presented is an adaptive tension control system design study. Model reference adaptive control designs using MIT rule and PI methods to yield the control parameter adjustment mechanism. Lyapunov techniques are used to develop a model-reference control system that stabilizes the tension. A novel model of changing the length of spans by employing magnetic bearing is developed to allow fast and accurate adjustment of tension using mechatronics concepts. Simulation results compare the tension setpoint regulation provided by the proposed control strategy with PID tension feedback control.

On the parameter convergence properties of a combined VS/adaptive control scheme during sliding motion

The effects of the use of a variable structure control component within an adaptive control scheme, previously proposed by the authors (1992, 1995), are investigated in this paper. The discontinuous control action does not allow the explicit identification of the coefficients of the filters, but only the identification of the so-called equivalent control coinciding with the ideal control. By coupling the variable structure control strategy with a continuous parameter adjustment mechanism it is instead possible to obtain an equivalent control which, during the sliding motion, turns out to be equal to a suitably constructed prediction error. As long as the parameter adaptation is driven by both the tracking error (which vanishes in finite time) and the prediction error, then the controller parameters prove to be exponentially convergent to the ideal values, provided that the reference signal is sufficiently persistently exciting.