Search results

Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.

This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.

Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.

A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

This paper utilizes the improved particle swarm optimization (IPSO) with bounded constraints technique on velocity and positioning for adjusting the gains of a proportional-integral-derivative (PID) and iterative learning control (ILC) controllers. The purpose of this paper is to achieve precision motion through bettering control by this technique.

Actual platform positioning must avoid the occurrence of a large control action signal, undesirable overshooting, and preventing out of the maximum position limit. Several in-house experiments observation, the PSO mechanism is sometimes out of the optimal solution in updating velocity and updating position of particles, the system may become unstable in real-time applications. The proposed IPSO with new bounded constraints technique shows a great ability to stabilize nonminimum phase and heavily oscillatory systems based on new bounded constraints on velocity and positioning in PSO algorithm is evaluated on one axis of linear synchronous motor with a PC-based real-time ILC.

Simulations and experiment results show that the proposed controller can reduce the error significantly after two learning iterations. The developed method using bounded constraints technique provides valuable programming tools to practicing engineers.

The proposed IPSO-ILC-PID controller overcomes the shortcomings of conventional ILC-PID controller with fixed gains. Simulation and experimental results show that the proposed IPSO-ILC-PID algorithm exhibits great speed convergence and robustness. Experimental results confirm that the proposed IPSO-ILC-PID algorithm is effective and achieves better control in real-time precision positioning.

Parameter identification of photovoltaic (PV) modules plays a vital role in modeling PV systems. This study aims to propose a novel hybrid approach to identify the seven parameters of the two-diode model of PV modules with high accuracy.

The proposed hybrid approach combines an improved particle swarm optimization (IPSO) algorithm with an analytical approach. Three parameters are optimized using IPSO, whereas the other four are analytically determined. To improve the performance of IPSO, three improvements are adopted, that is, evaluating the particles with two evaluation functions, adaptive evolutionary learning and adaptive mutation.

The performance of proposed approach is first verified by comparing with several well-established algorithms for two case studies. Then, the proposed method is applied to extract the seven parameters of CSUN340-72M under different operating conditions. The comprehensively experimental results and comparison with other methods verify the effectiveness and precision of the proposed method. Furthermore, the performance of IPSO is evaluated against that of several popular intelligent algorithms. The results indicate that IPSO obtains the best performance in terms of the accuracy and robustness.

An improved hybrid approach for parameter identification of the two-diode model of PV modules is proposed. The proposed approach considers the recombination saturation current of the p–n junction in the depletion region and makes no assumptions or ignores certain parameters, which results in higher precision. The proposed method can be applied to the modeling and simulation for research and development of PV systems.

The purpose of this paper is to search for the critical-scenarios of autonomous vehicles (AVs) quickly and comprehensively, which is essential for verification and validation (V&V).

The author adopted the index F1 to quantitative critical-scenarios' coverage of the search space and proposed the improved particle swarm optimization (IPSO) to enhance exploration ability for higher coverage. Compared with the particle swarm optimization (PSO), there were three improvements. In the initial phase, the Latin hypercube sampling method was introduced for a uniform distribution of particles. In the iteration phase, the neighborhood operator was adapted to explore more modals with the particles divided into groups. In the convergence phase, the convergence judgment and restart strategy were used to explore the search space by avoiding local convergence. Compared with the Monte Carlo method (MC) and PSO, experiments on the artificial function and critical-scenarios search were carried out to verify the efficiency and the application effect of the method.

Results show that IPSO can search for multimodal critical-scenarios comprehensively, with a stricter threshold and fewer samples in the experiment on critical-scenario search, the coverage of IPSO is 14% higher than PSO and 40% higher than MC.

The critical-scenarios' coverage of the search space is firstly quantified by the index F1, and the proposed method has higher search efficiency and coverage for the critical-scenarios search of AVs, which shows application potential for V&V.

The purpose of this paper is to establish the optimization model and solve the short‐term economic dispatch of cascaded hydro‐plants.

An improved particle swarm optimization (IPSO) approach is proposed to solve the short‐term economic dispatch of cascaded hydroelectric plants. The water transport delay time between connected reservoirs is taken into account and it is easy in dealing with the difficult hydraulic and power coupling constraints using the proposed method in practical cascaded hydroelectric plants operation. The feasibility of the proposed method is demonstrated for actual cascaded hydroelectric plant.

The simulation results show that this approach can prevent premature convergence to a high degree and keep a rapid convergence speed.

The optimal values of parameters in the proposed method are the main limitations where the method will be applied to the economic operation of the hydro‐plant.

The paper presents useful advice for short‐term economic operations of the hydro‐plant. A new optimization method to solve the short‐term optimal generation scheduling is proposed. The optimal generation power and water discharge during the whole dispatching time for hydro‐plant operation can be obtained.

The IPSO method is realized by maintaining high diversity of the swarm during the optimization process and preventing premature convergence.

The weighted evaluation function method with normalized objective functions is used to transform the proposed multi-objective model into a single objective one, which reflects the investors' preference for returns, risks and social responsibility by adjusting the weights. Finally, an example is given to illustrate the solution steps of the model and the effectiveness of the algorithm.

Based on the possibility theory, assuming that the future returns of each asset are trapezoidal fuzzy numbers, a mean-variance-Yager entropy-social responsibility model is constructed including piecewise linear transaction costs and risk-free assets. The model proposed in this paper includes six constraints, the investment proportion sum, the non-negativity proportion, the ceiling and floor, the pre-assignment, the cardinality and the round lot constraints. In addition, considering the special round lot constraint, the proposed model is transformed into an integer programming problem.

The effects of different constraints and transaction costs on the effective frontier of the portfolio are analyzed, which not only assists investors to make decisions close to their expectations by setting appropriate parameters but also provides constructive suggestions through the overall performance of each asset.

There are two improvements in the improved particle swarm optimization algorithm: one is that the complex constraints are specifically satisfied by using a renewable 0–1 random constraint matrix and random scaling factors instead of fixed ones; the other is eliminating the particles with poor fitness and randomly adding some new particles that satisfy all the constraints to achieve the goal of global search as much as possible.

The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem.

A modified PSO algorithm is designed.

The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm.

Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

To obtain a high-quality finished product model, three-dimensional (3D) printing needs to be optimized.

Based on back-propagation neural network (BPNN), the particle swarm optimization (PSO) algorithm was improved for optimizing the parameters of BPNN, and then the model precision was predicted with the improved PSO-BPNN (IPSO-BPNN) taking nozzle temperature, etc. as the influencing factors.

It was found from the experimental results that the prediction results of IPSO-BPNN were closer to the actual values than BPNN and PSO-BPNN, and the prediction error was smaller; the average error of dimensional precision and surface precision was 6.03% and 6.54%, respectively, which suggested that it could provide a reliable guidance for 3D printing optimization.

The experimental results verify the validity of IPSO-BPNN in 3D printing precision prediction and make some contributions to the improvement of the precision of finished products and the realization of 3D printing optimization.

The purpose of this paper is to present a new constrained optimization algorithm based on a particle swarm optimization (PSO) algorithm approach.

This paper introduces a hybrid approach based on a modified ring neighborhood with two new perturbation operators designed to keep diversity. A constraint handling technique based on feasibility and sum of constraints violation is adopted. Also, a special technique to handle equality constraints is proposed.

The paper shows that it is possible to improve PSO and keeping the advantages of its social interaction through a simple idea: perturbing the PSO memory.

The proposed algorithm shows a competitive performance against the state‐of‐the‐art constrained optimization algorithms.

The proposed algorithm can be used to solve single objective problems with linear or non‐linear functions, and subject to both equality and inequality constraints which can be linear and non‐linear. In this paper, it is applied to various engineering design problems, and for the solution of state‐of‐the‐art benchmark problems.

A new neighborhood structure for PSO algorithm is presented. Two perturbation operators to improve PSO algorithm are proposed. A special technique to handle equality constraints is proposed.

This study aims to analyze the inertial weight factor value in the (PSO) algorithm and propose non‐linear weights with decreasing strategy to implement the improved PSO (IPSO) algorithm. Using various types of sensors, combined with ZigBee wireless sensor networks and the TCP/IP network. The GPRS/SMS long‐range wireless network will sense the measured data analysis and evaluation to create more effective monitoring and observation in a regional environment to achieve an Internet of Things with automated information exchange between persons and things.

This study proposes a wireless sensor network system using ZigBee (PSoC‐1605A) chip, sensor and circuit boards to constitute the IOT system. The IOT system consists of a main coordinator (PSoC‐1605A), smart grid monitoring system, robotic arm detection warning system and temperature and humidity sensor network. The hardware components communicate with each other through wireless transmission. Each node collects data and sends messages to other objects in the network.

This study employed IPSO to perform information fusion in a multi‐sensor network. The paper shows that IPSO improved the measurement preciseness via weight factors estimated via experimental simulations. The experimental results show that the IPSO algorithm optimally integrates the weight factors, information source fusion reliability, information redundancy and hierarchical structure integration in uncertain fusion cases. The sensor data approximates the optimal way to extract useful information from each fusion data and successfully eliminates noise interference, producing excellent fusion results.

Robotic arm to tilt detection warning system: Several geographic areas are susceptible to severe tectonic plate movement, often generating earthquakes. Earthquakes cause great harm to public infrastructure, and a great threat to high‐tech, high‐precision machinery and production lines. To minimize the extent of earthquake disasters and allow managers to deal with power failures, vibration monitoring system construction can enhance manufacturing process quality and stability. Smart grid monitoring system: The greenhouse effect, global energy shortage and rising cost of traditional energy are related energy efficiency topics that have attracted much attention. The aim of this paper is that real‐time data rendering and analysis can be more effective in understanding electrical energy usage, resulting in a reduction in unnecessary consumption and waste. Temperature and humidity sensor network system: Environmental temperature and humidity monitoring and application of a wide range of precision industrial production lines, laboratories, antique works of art that have a higher standard of environmental temperature and humidity requirements. The environment has a considerable influence on biological lifeforms. The relative importance of environmental management and monitoring is acute.

This paper improves the fixed inertial weight of the original particle swarm optimization (PSO) algorithm. An illustration in the paper indicates that IPSO applies the Internet of Things (IOT) system in monitoring a system via adjusted weight factors better than other existing PSO methods in computing a precise convergence rate for excellent fusion results.