Control Parameters Of Algorithm Research Articles

Optimal Control of the Angular Momentum for a Solid (Spacecraft) Performing a Spatial Turn

A quaternion solution of the dynamic problem on the optimal turn of a solid (for example, a spacecraft) from a known initial to a given final angular position is presented. Optimization of the control program is carried out by using a combined indicator that combines a quadratic performance criterion and turn time; the minimized functional combines energy costs and maneuver duration in a given proportion. Based on the maximum principle, quaternion models, and methods for studying the controlled motion of a solid (spacecraft), a solution of the problem has been obtained. The construction of the optimal rotation is based on a differential equation relating the angular momentum and the orientation quaternion of a solid. The conditions of optimality are written in analytical form and the properties of the optimal motion are studied. Analytical equations and calculation formulas for finding the optimal control are presented. The control law is formulated as an explicit dependence of the control variables on the phase coordinates. Key relations that determine the optimal values of the parameters of the angular momentum control algorithm are given. In the case of a dynamically symmetric body, a complete solution of the turn problem in a closed form is obtained: analytical dependences as explicit functions of time for control variables and relations for calculating the parameters of the control law are given. A numerical example and the results of numeric simulation of the rotation of a spacecraft as a solid under optimal control that demonstrate the practical feasibility of the proposed control method are given.

РЕШЕНИЕ ЗАДАЧИ РАЗМЕЩЕНИЯ ЭЛЕМЕНТОВ ЭВА НА ОСНОВЕ ИНТЕГРАЦИИ МЕТОДОВ ЭВОЛЮЦИОННОГО ПОИСКА И НЕЧЕТКОГО УПРАВЛЕНИЯ

The problem of elements placement in digital computing equipment are consider in the article.The analysis of the current state of research on this topic is carried out, the relevance of theproblem under consideration is noted. The importance of developing new effective methods forsolving engineering design problems is emphasized. The prospects of developing and using hybrid approaches and models for solving complex semi-formalized design and optimization problems arenoted. The statement of the problem of placement of circuit elements of digital computing equipmentis given. The importance of a qualitative solution of the placement problem from the point of view ofthe successful implementation of the subsequent stages of design is noted. The analysis of variousapproaches and algorithms for solving the placement problem is carried out. Options for choosingvarious criteria for assessing the quality of placement are given. A complex additive criterion forassessing the quality of placement is proposed. The objective function and limitations of the consideredplacement problem as an optimization problem are given. A hybrid approach to solving theplacement problem is proposed. To increase the efficiency and reduce the running time of the algorithm,a model of a parallel multipopulation genetic algorithm is proposed. To synchronize evolutionaryprocesses in a multipopulation genetic algorithm, a modified migration operator has beendeveloped. An analysis of the efficiency of the proposed migration operator has been carried out andrecommendations for its use have been formulated. In order to increase the speed of the algorithm forsolving the placement problem, a model for organizing parallel evolutionary computations throughthe use of multithreading at the local level is proposed. The principles of operation of the fuzzy controlmodule are described. The procedure of logical inference using the rule base is described.The structure of a multilayer neural network that implements the Gaussian function is proposed.A model of a fuzzy logic controller for dynamically changing the values of control parameters of agenetic algorithm is proposed. The control parameters of the fuzzy logic controller are determined.The proposed hybrid algorithm is implemented as an application program. A series of computationalexperiments were carried out to determine the efficiency of the developed algorithm and to select theoptimal values of the control parameters.

A Novel Approach for Polyphase Filter Bank Design Using ABC Algorithm

Polyphase filter banks (PFBs) are the most preferred multirate structures for subband coding in Digital Signal Processing (DSP) and communication. For PFB design, there are many important design parameters such as filter length and frequency selectivity. Also, to realize the desired frequency response in designs, stopband and passband attenuation are of considerable importance. In PFB design, researchers and practitioners frequently use iterative and meta-heuristic optimization methods. Heuristic techniques have a significant problem-solving ability in continuous and discrete solution space. Therefore, they give better results than other suggested methods, and their performance depends on the control parameters. In this study, Artificial Bee Colony (ABC) algorithm was employed for suggested design problem of PFB. In the first stage, the control parameters of the ABC algorithm were examined to improve the performance of the proposed PFB problem. In the second stage, the analysis was carried out by changing filter lengths (8-256) and filter band frequencies (0.3-0.7/0.4-0.6). All results obtained were also compared with the Particle Swarm Optimization algorithm (PSO) and the Genetic algorithm (GA). Finally, a DSP application of PFB was carried out according to best results achieved by the ABC algorithm for filter lengths and frequencies.

A Hybrid Learning Particle Swarm Optimization With Fuzzy Logic for Sentiment Classification Problems

Methods based on deep learning have great utility in the current field of sentiment classification. To better optimize the setting of hyper-parameters in deep learning, a hybrid learning particle swarm optimization with fuzzy logic (HLPSO-FL) is proposed in this paper. Hybrid learning strategies are divided into mainstream learning strategies and random learning strategies. The mainstream learning strategy is to define the mainstream particles in the cluster and build a scale-free network through the mainstream particles. The random learning strategy makes full use of historical information and speeds up the convergence of the algorithm. Furthermore, fuzzy logic is used to control algorithm parameters to balance algorithm exploration and exploration performance. HLPSO-FL has completed comparison experiments on benchmark functions and real sentiment classification problems respectively. The experimental results show that HLPSO-FL can effectively complete the hyperparameter optimization of sentiment classification problem in deep learning and has strong convergence.

Using genetic algorithms for automating automated lane‐keeping system testing

AbstractIn this paper, we outline an approach for automatically generating challenging road networks for virtual testing of an automated lane‐keeping system. Based on a set of control points, we construct a parametric curve representing a road network, defining the dynamic driving task an automated lane‐keeping system‐equipped vehicle must perform. Changing control points has a global influence on the resulting road geometry. Our approach uses search to find control‐point sets that result in a challenging road, eventually forcing the vehicle to leave the intended path. We apply our approach in different search variants to evaluate their performance regarding test efficiency and the diversity of failing tests. In addition, we evaluate different genetic algorithm control parameter configurations to investigate the most influential parameters and if specific configurations can be seen asoptimal, leading to better results than others. For both studies, we consider another search‐based test method and two different random test generators as a baseline for comparison. The empirical results indicate that specific control parameter settings increase the overall performance for each search variant. While the population size is the most influential control parameter for all methods, the performance improvement when usingoptimalsettings is only significant for one method.

Application of Fine-Tuned Krill Herd Algorithm in Design of Water Distribution Networks

Stochastic population-based metaheuristic algorithms have been widely used to design water distribution networks (WDNs). The present study introduces one of its kind, the krill herd algorithm (KHA), a novel swarm intelligence-based metaheuristic. Demonstrating KHA application to WDN design, the study proposes its enhanced version to improve its convergence precision. Consequently, combining the fine-tuned mechanism of KHA with the EPANET 2.2 simulation software, the fine-tuned KHA (FIT-KHA) model is formulated and checked for its efficiency in handling the WDN design problem. Because three benchmark problems (new and rehabilitation WDNs) of different dimensions are considered, the significance of the algorithm control parameters concerning the network size is studied. From sensitivity analysis, the time step factor, number of krill, and maximum iteration size are found to influence the algorithm performance. Their optimal values followed an increasing trend with the network size. The computational results reinforce a superior search exploitation ability of the FIT-KHA. Importantly, the results exhibit a better computational efficiency of KHA over most of the reported metaheuristic algorithms. Then, the performance state of optimally designed WDN under hydraulic (demand, roughness coefficient variations) and mechanical (single- and two-pipe failure) uncertain scenarios is studied. Besides reliability and surrogate measures, multiaspect metrics based on adequacy and equity are evaluated to assess the network’s performance. The WDNs are observed to be more susceptible to demand uncertainties over roughness coefficient variation. At higher states of mechanical (pipe) failure scenarios, especially, the equity of the WDN is disturbed compared to the adequacy, except for rehabilitated WDN. Certainly, the performance study manifests that the reliability measure that truly presents the WDNs performance is the function of network type. While proposing KHA as an effective alternative optimization tool, the study suggests a prior performance study to choose an appropriate metric to formulate a reliability-based multiobjective framework for the robust design of WDNs.

An effective and robust genetic algorithm for urban freight transport scheduling using passenger rail network

This paper considers freight transport in urban areas using passenger rail networks as an eco-friendly alternative to current predominantly fossil-fueled trucks. It addresses one of the essential problems, called freight rail transport scheduling problem (FRTSP), where the objective is to minimize the total waiting time of the parcels at their departure stations which is equivalent to minimizing the space occupancy at the stations and maximizing the turnover of the freight transport. In this paper, we focus on designing an effective and robust method to solve the FRTSP. For this purpose, a genetic algorithm is proposed whose performances, as it is widely known, highly depend on the length of chromosomes and control parameters. In this genetic algorithm, the length of chromosomes is reduced owing to a mathematical formulation with fewer integer decision variables representing a parcel-to-train assignment. We prove that as long as a parcel-to-train assignment is known, it takes a polynomial time to check whether this assignment can lead to a feasible solution and, if yes, solve the remaining problem by transforming it into computing the longest distances in a digraph. This result makes it possible to only consider parcel-to-train assignment variables in chromosomes, while efficiently determining the values of the other variables in fitness evaluation. In order to achieve robustness regarding all instances, the control parameters of the genetic algorithm are set with Taguchi method. Computational results show that the method developed in this way is very effective. For small-size instances, it yields optimal solutions within considerably shorter computation times than a commonplace optimization solver does. For large-size instances, it outperforms such a solver, not only providing much higher-quality solutions but also consuming much shorter computation times. This method thus meets very well operational requirements where high-quality solutions are expected while computation time is very limited.

A constrained multi-item EOQ inventory model for reusable items: Reinforcement learning-based differential evolution and particle swarm optimization

The growing environmental concerns, governmental regulations, and significant cost savings are the primary motivations for companies to consider the reuse and recovery of products in their inventory system. The previous research ignored several realistic features of reusable items inventory systems, such as the presence of multiple products and operational constraints. For the first time, this paper presents a new multiproduct economic order quantity inventory model for an inventory system of reusable products. The goal of the model is to determine the optimal replenishment quantity and reuse quantity of each item so that the system's total cost is minimized. Several operational constraints are considered to provide a more realistic framework, such as the total available budget, warehouse space, and holding cost. Due to the nonlinearity of the presented model, differential evolution (DE) and particle swarm optimization (PSO) algorithms are utilized as two solution approaches. However, these algorithms' performance is highly dependent on their control parameters. Therefore, for the first time, two new variants of these algorithms, called DEQL and PSOQL, are presented in this study, where the control parameters of algorithms are not pre-determined. A powerful reinforcement learning algorithm, Q-learning, adapts these values intelligently. In other words, as a research contribution, this research aims at introducing a new variant of hybrid the DE and PSO algorithms in which a machine learning algorithm controls the value of metaheuristic parameters. The other parameters of the proposed algorithms are tunned employing the Taguchi method. Extensive numerical examples are established in different sizes, and the outputs are discussed in terms of several criteria. Statistical analysis of the results is performed, demonstrating that the proposed reinforcement learning-based parameter adaption has significantly improved algorithms' performance.

Synthetic Lyapunov stabilization technique for designing actuation-constrained multi-input multi-output control systems

The main objective of this paper is to present a non-predictive method in the design of nonlinear multi-input multi-output (MIMO) control systems with the presence of constraints that are determinant in practical conditions, namely, the frequency bandwidth limitation of the actuation system and saturation boundaries in control commands. If these constraints are applied in the non-predictive control design problem, it is not possible to simultaneously satisfy Lyapunov stability and actuation constraints, analytically. Instead of model-predictive-based algorithms, which in most cases are computationally expensive, this paper proposes an algorithm based on synthetic Lyapunov stability. In this technique, by defining an intelligent filter applied to the system desired trajectories, defining intelligent proximity coefficients in decoupled inequalities resulting from Lyapunov stability, and determining the admissible boundaries of control commands, a space of regulatory parameters is generated. By appropriately adjusting these parameters based on statistical analysis conducted on the overall dynamics of the system, the Lyapunov stability is guaranteed, and the mentioned control constraints are not violated. In summary, the proposed control algorithm includes the formulation of discrete-time dynamics of sliding functions, the presentation of the procedure of defining and adjusting the control algorithm parameters with the proposed synthetic stability criterion, and the calculation of control inputs based on constraints imposed on the problem. Finally, the algorithm is applied to a cart moving in the X-Y plane, including two rigid cooperative arms that are carrying a load. The most important features of synthetic Lyapunov stability compared to the model predictive-based method are its small computational load and its acceptable performance in satisfying both the Lyapunov stability conditions and determinant control constraints in more realistic situations.

Effects of Concrete Grades and Column Spacings on the Optimal Design of Reinforced Concrete Buildings.

This paper investigates the effects of concrete grades and column spacings on the optimal design of reinforced concrete (RC) buildings. To this end, cost design optimization was performed for buildings with three different floor systems: flat plates (FS), flat slabs with drop panels (FSDP), and solid slabs (SS). The evolutionary method provided by the Excel solver was used as the optimization algorithm because it can deal with the complex nature of structural design problems. The objective function was the total construction cost of the building, including the costs of concrete, reinforcement bars, labor, and formwork, while still fulfilling the constraints of the Egyptian code of practice (ECP-18). The applicability of the presented algorithm was investigated in a design example, where the tuning of the evolutionary algorithm control parameters was sought, and the best parameters were investigated. Two case studies were employed to study the impacts of changing the column spacing and concrete grades on the optimal cost for each floor system. The results showed that low concrete grades, (i.e., characteristic strength up to 40 MPa) and column spacings up to 5 m are preferred in terms of direct construction costs for low-rise RC residential buildings.

Parameters Optimization of Taguchi Method Integrated Hybrid Harmony Search Algorithm for Engineering Design Problems

Performance of convergence to the optimum value is not completely a known process due to characteristics of the considered design problem and floating values of optimization algorithm control parameters. However, increasing robustness and effectiveness of an optimization algorithm may be possible statistically by estimating proper algorithm parameters values. Not only the algorithm which utilizes these estimated-proper algorithm parameter values may enable to find the best fitness in a shorter time, but also it may supply the optimum searching process with a pragmatical manner. This study focuses on the statistical investigation of the optimum values for the control parameters of the harmony search algorithm and their effects on the best solution. For this purpose, the Taguchi method integrated hybrid harmony search algorithm has been presented as an alternative method for optimization analyses instead of sensitivity analyses which are generally used for the investigation of the proper algorithm parameters. The harmony memory size, the harmony memory considering rate, the pitch adjustment rate, the maximum iteration number, and the independent run number of entire iterations have been debated as the algorithm control parameters of the harmony search algorithm. To observe the effects of design problem characteristics on control parameters, the new hybrid method has been applied to different engineering optimization problems including several engineering-optimization examples and a real-size engineering optimization design. End of extensive optimization and statistical analyses to achieve optimum values of control parameters providing rapid convergence to optimum fitness value and handling constraints have been estimated with reasonable relative errors. Employing the Taguchi method integrated hybrid harmony search algorithm in parameter optimization has been demonstrated as it is a reliable and efficient manner to obtain the optimum results with fewer numbers of run and iteration.

A Semi-Active Control Technique through MR Fluid Dampers for Seismic Protection of Single-Story RC Precast Buildings.

The work proposes an innovative solution for the reduction of seismic effects on precast reinforced concrete (RC) structures. It is a semi-active control system based on the use of magnetorheological dampers. The special base restraint is remotely and automatically controlled according to a control algorithm, which modifies the dissipative capability of the structure as a function of an instantaneous dynamic response. The aim is that of reducing the base bending moment demand without a significant increase in the top displacement response. A procedure for the optimal calibration of the parameters involved in the control logic is also proposed. Non-linear modelling of a case-study structure has been performed in the OpenSees environment, also involving the specific detailing of a novel variable base restraint. Non-linear time history analyses against natural earthquakes allowed testing of the optimization procedure for the control algorithm parameters, finally the capability of the proposed technology to mitigate seismic risk of new or existing one-story precast RC structures is highlighted.

Initialisation Approaches for Population-Based Metaheuristic Algorithms: A Comprehensive Review

A situation where the set of initial solutions lies near the position of the true optimality (most favourable or desirable solution) by chance can increase the probability of finding the true optimality and significantly reduce the search efforts. In optimisation problems, the location of the global optimum solution is unknown a priori, and initialisation is a stochastic process. In addition, the population size is equally important; if there are problems with high dimensions, a small population size may lie sparsely in unpromising regions, and may return suboptimal solutions with bias. In addition, the different distributions used as position vectors for the initial population may have different sampling emphasis; hence, different degrees of diversity. The initialisation control parameters of population-based metaheuristic algorithms play a significant role in improving the performance of the algorithms. Researchers have identified this significance, and they have put much effort into finding various distribution schemes that will enhance the diversity of the initial populations of the algorithms, and obtain the correct balance of the population size and number of iterations which will guarantee optimal solutions for a given problem set. Despite the affirmation of the role initialisation plays, to our knowledge few studies or surveys have been conducted on this subject area. Therefore, this paper presents a comprehensive survey of different initialisation schemes to improve the quality of solutions obtained by most metaheuristic optimisers for a given problem set. Popular schemes used to improve the diversity of the population can be categorised into random numbers, quasirandom sequences, chaos theory, probability distributions, hybrids of other heuristic or metaheuristic algorithms, Lévy, and others. We discuss the different levels of success of these schemes and identify their limitations. Similarly, we identify gaps and present useful insights for future research directions. Finally, we present a comparison of the effect of population size, the maximum number of iterations, and ten (10) different initialisation methods on the performance of three (3) population-based metaheuristic optimizers: bat algorithm (BA), Grey Wolf Optimizer (GWO), and butterfly optimization algorithm (BOA).

In-Vehicle Stereo Vision Systems with Improved Ant Colony Optimization Based Lane Detection: A Solution to Accidents Involving Large Goods Vehicles Due to Blind Spots

This paper presents an in-vehicle stereo vision system as a solution to accidents involving large good vehicle due to blind spots using Nigeria as a case study. In this paper, a stereo-vision system was attached to the front of Large Good Vehicles (LGVs) with a view to presenting live feeds of vehicles close to the LGV vehicles and their distance away. The captured road images using the stereo vision system were optimized for effectiveness and optimal vehicle maneuvering using a modified metaheuristics algorithm called the simulated annealing Ant Colony Optimization (saACO) algorithm. The concept of simulated annealing is strategies used to automatically select the control parameters of the ACO algorithm. This helps to stabilize the performance of the ACO algorithm irrespective of the quality of the lane images captured in the in-vehicle vision system. The system is capable of notifying drivers through lane detection techniques of blind spots. This technique enables the driver to be more aware of what surrounds the vehicle and make decisions early. In order to test the system, the stereo-vision device was mounted on a Large good vehicle, driven in Zaria (a city in Kaduna state in Nigeria), and data were in the record. Out of 180 events, 42.22% of potential accident events were caused by Passenger Cars, while 27.22%, 18.33% and 12.22% were caused by two-wheelers, Large Good Vehicles and road users, respectively. In the same vein, the in-vehicle lane detection system shows a good performance of the saACO-based lane detection system and gives a better performance in comparison with the standard ACO method.

Effects of Particle Swarm Optimization and Genetic Algorithm Control Parameters on Overcurrent Relay Selectivity and Speed

Distribution systems continue to grow and becoming more complex with increasing operational challenges such as protection miscoordination. Initially, conventional methods were favoured to solve overcurrent relay coordination problems; however, the implementation of these methods is time-consuming. Therefore, recent studies have adopted the utilisation of particle swarm optimization and genetic algorithms to solve overcurrent relay coordination problems and maximise system selectivity and operational speed. Particle swarm optimization and genetic algorithms are evolutionary algorithms that at times suffer from premature convergence due to poor selection of control parameters. Consequently, this paper presents a comprehensive sensitivity analysis to evaluate the effect of the discrete control parameters on particle swarm optimizer and genetic algorithms performance, alternatively on the behaviour of overcurrent relays. Optimization algorithms aim to minimise overcurrent relay time multiplier settings and accomplish optimal protection coordination. The findings indicate that particle swarm optimization is more sensitive to inertia weight and swarm size while the number of iterations has minimal effect. The results also depict that 30% crossover, 2% mutation, and smaller population size yield faster convergence rate and optimise fitness function, which improves genetic algorithms performance. Sensitivity analysis results are verified by comparing the performance of particle swarm optimization with the genetic algorithms which show the former parameter setting outperforms the latter. The relay operational speed is reduced by 15% for particle swarm optimization and system selectivity is maximised. The optimal protection coordination achieved using particle swarm optimization showed superiority of the algorithm, its ability to circumvent premature convergence, consistency, and efficiency.

Evaluation of several initialization methods on arithmetic optimization algorithm performance

Abstract Arithmetic optimization algorithm (AOA) is one of the recently proposed population-based metaheuristic algorithms. The algorithmic design concept of the AOA is based on the distributive behavior of arithmetic operators, namely, multiplication (M), division (D), subtraction (S), and addition (A). Being a new metaheuristic algorithm, the need for a performance evaluation of AOA is significant to the global optimization research community and specifically to nature-inspired metaheuristic enthusiasts. This article aims to evaluate the influence of the algorithm control parameters, namely, population size and the number of iterations, on the performance of the newly proposed AOA. In addition, we also investigated and validated the influence of different initialization schemes available in the literature on the performance of the AOA. Experiments were conducted using different initialization scenarios and the first is where the population size is large and the number of iterations is low. The second scenario is when the number of iterations is high, and the population size is small. Finally, when the population size and the number of iterations are similar. The numerical results from the conducted experiments showed that AOA is sensitive to the population size and requires a large population size for optimal performance. Afterward, we initialized AOA with six initialization schemes, and their performances were tested on the classical functions and the functions defined in the CEC 2020 suite. The results were presented, and their implications were discussed. Our results showed that the performance of AOA could be influenced when the solution is initialized with schemes other than default random numbers. The Beta distribution outperformed the random number distribution in all cases for both the classical and CEC 2020 functions. The performance of uniform distribution, Rayleigh distribution, Latin hypercube sampling, and Sobol low discrepancy sequence are relatively competitive with the Random number. On the basis of our experiments’ results, we recommend that a solution size of 6,000, the number of iterations of 100, and initializing the solutions with Beta distribution will lead to AOA performing optimally for scenarios considered in our experiments.

Robust formation tracking of multiple autonomous surface vessels with individual objectives: A noncooperative game-based approach

Distributed robust formation tracking of multiple autonomous surface vessels (ASVs) with uncertain dynamics and individual objectives is investigated in this paper by developing a noncooperative game-based approach. Compared with the multiple ASVs model commonly studied in existing literature, two distinct features of the present multiple ASVs model are: (i) the ASVs are allowed to have individual objectives, and (ii) the evolution of each ASV is subjected to unmodeled dynamics and time-varying external disturbances. Specifically, the ASVs are required to cooperatively achieve the group objective of tracking a time-varying reference trajectory in a specific formation pattern, while each ASV also needs to complete its own individual objective of maintaining desired displacement with respect to the reference trajectory during the formation tracking process according to its own benefit. A noncooperative game-based approach is developed for the ASVs to achieve the goal of formation tracking in the presence of individual objectives where the formation tracking problem is cast into the Nash equilibrium seeking problem of a noncooperative game among ASVs subject to unmodeled dynamics and external disturbances. To make each ASV seek the Nash equilibrium efficiently, adaptive neural network (NN) is adopted to approximate the unmodeled dynamics and nonsmooth feedback is utilized to deal with the effect of bounded external disturbances. It is theoretically shown that the states of ASVs including the position and heading angle states could asymptotically converge to the Nash equilibrium if the underlying communication graph is strongly connected and the control parameters of the Nash equilibrium seeking algorithms are suitably selected. Finally, an experimental study is performed to verify the validity of the theoretical results.

Optimisation of an Algorithm for Automatic Control of Transmission in a Wheeled Tractor

The paper considers a digital model of a wheeled agricultural tractor, as well as algorithms for automatic control of the engine and transmission. Simulation of the working conditions was performed in MATLAB and its applications, Simulink, Sim-scape. The tractor model is based on a systemic approach and includes such subsystems as the engine, transmission, undercarriage, hook load, and control actions from the operator, as well as functions of logic and optimisation for gear shifting. With the help of fundamental blocks of the Matlab package, models of tractor physical components are created. The fundamental blocks of the package serve to model the physical components of the tractor: engine, transmission control system, the transmission that includes friction clutches, gears, shafts ties with the necessary characteristics. Simulation of the working processes of a tractor unit is performed with optimisation of the laws of engine and transmission control according to the criterion of fuel efficiency and maximum power usage. The control system parameters were optimised in the transport mode, as well as in the maximum power mode in the traction range. As an illustration of the use of this analysis method, a solution to the problem of optimisation of an agricultural wheeled tractor operating modes is given. The results of this mathematical simulation are presented in this paper. Parameters of tractor operation control algorithm, which provide minimum fuel consumption in tractor operating cycles, are given. The research results showed the effectiveness of the proposed algorithms and the digital model. The presented digital model allows to debug the operating tractor control algorithms and offers an engineering practice for optimising automatic control of the agricultural tractor engine and transmission.

Study of seismic-responses and semi-active control of Taizhou Bridge under multi-support excitation

PurposeIn order to make sure of the safety of a long-span suspension bridge under earthquake action, this paper aims to study the traveling wave effect of the bridge under multi-support excitation and optimize the semi-active control schemes based on magneto-rheological (MR) dampers considering reference index as well as economical efficiency.Design/methodology/approachThe finite element model of the long-span suspension bridge is established in MATLAB and ANSYS software, which includes different input currents and semi-active control conditions. Six apparent wave velocities are used to conduct non-linear time history analysis in order to consider the seismic response influence in primary members under traveling wave effect. The parameters α and β, which are key parameters of classical linear optimal control algorithm, are optimized and analyzed taking into account five different combinations to obtain the optimal control scheme.FindingsWhen the apparent wave velocity is relatively small, the influence on the structural response is oscillatory. Along with the increase of the apparent wave velocity, the structural response is gradually approaching the response under uniform excitation. Semi-active control strategy based on MR dampers not only restrains the top displacement of main towers and relative displacement between towers and girders, but also affects the control effect of internal forces. For classical linear optimal control algorithm, the values of two parameters (α and β) are 100 and 8 × 10–6 considering the optimal control effect and economical efficiency.Originality/valueThe emphasis of this study is the traveling wave effect of the triple-tower suspension bridge under multi-support excitation. Meanwhile, the optimized parameters of semi-active control schemes using MR dampers have been obtained, providing relevant references in improving the seismic performance of three-tower suspension bridge.

Recent advances and future research directions in semiactive control isolator

<p indent="0mm">Sensitive equipment could be disturbed by vibration or shock motion. Hence, an isolator should be installed to protect such equipment. A passive isolator does not adapt to the complicated environment of vibration and shock, and an active isolator always has a large energy consumption and high cost. Compared with such isolators, the semiactive control isolator with slightly extra energy consumption has excellent vibration isolation abilities; hence, it is more stable than active isolators. Moreover, it is more flexible than passive isolators owing to its wider isolation frequency. However, most research pertaining to the isolation performance of semiactive isolators considers a single mode, such as the vibration isolation mode or antishock mode, and few studies have focused on hybrid mode, i.e., both vibration isolation and antishock modes. To investigate the semiactive control isolator with hybrid mode, first, research on the theoretical study and application of semiactive control isolators in the recent decade is overviewed. Further, the isolator performance by considering the aspects of the control algorithm and structure parameters is evaluated. Finally, a guideline for a new semiactive control hybrid isolator system is proposed. The guideline can help improve the control algorithm, optimize structure parameters, create evaluative criteria, highlight unsolved problems, and provide recommendations for future research directions.