A Novel Approach to Control of Piezo-Transducer in Microelectronics Packaging: PSO-PID and Editing Trajectory Optimization

Long, Zhili; Jiang, Zhaotian; Wang, Chao; Jin, Yan; Cao, Zhonghua; Li, Yangmin

doi:10.1109/tcpmt.2020.2984701

Cited by 16 publications

(8 citation statements)

References 25 publications

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“…They just evaluate the objective function at given decision variables and consider the optimization problem as a black box. Such algorithms mainly include genetic algorithm (GA) [25,26], PSO [27][28][29][30][31][32], fruit fly optimization algorithm (FOA) [33], queuing search algorithm (QSA) [34], atom search optimization (ASO) [35], equilibrium optimizer (EO) [36][37][38], evolutionary programming (EP) [39], differential evolution (DE) [40][41][42], etc. Many researchers extended these algorithms to solve the multi-objective optimization problems [43][44][45] or integrated them into multi-population methods to improve the optimization performance [46][47][48] 。 Among these algorithms, PSO is a considerably popular one which has some attractive features, such as simplicity, less parameters, low computational complexity, and ease to implement.…”

Section: A Nature-inspired Algorithmsmentioning

confidence: 99%

Novel Feedrate Optimization Method for NURBS Tool Paths Under Various Constraints

Chen

2022

IEEE Access

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A good feedrate profile for NURBS tool paths should be able to consider both machining efficiency and motion limits. In other words, the feedrate profile can lead to the machining time as small as possible and can satisfy various path and axis motion constraints. However, owing to the complicated relationship between trajectory motion and axes, it is difficult and time-consuming to check axis motion constraints (e.g., axis velocity, axis acceleration, axis jerk). In addition, the path length is not easy to calculate, so the machining time cannot be accurately estimated. This paper presents a feedrate profile planning method that can easily include, delete or organize the constraints and minimize the machining time for NURBS tool paths. The key idea is to use the same parameter u to represent both NURBS tool path and feedrate profile. When designing the feedrate profile with u, an analytical form of machining time can be obtained, and the constraints are converted into functions of u (including feedrate, tangential acceleration, tangential jerk, axis velocity, axis acceleration, axis jerk, chord error, and centripetal acceleration), which can be quickly checked. Particle swam optimization algorithm is employed to eliminate the solutions with long machining time or do not meet the constraints, and gradually optimize the feedrate profile. In the simulation, a star-shaped NURBS curve is selected to demonstrate the effectiveness of the proposed method. The results show that the proposed method can achieve not only smooth and high-speed machining under various constraints, but also high accuracy with the minimization of position error at the final interpolation point.INDEX TERMS CNC machine tool, feedrate profile optimization, non-uniform rational B-spline (NURBS) interpolation, particle swarm optimization ''

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Section: A Nature-inspired Algorithmsmentioning

confidence: 99%

Novel Feedrate Optimization Method for NURBS Tool Paths Under Various Constraints

Chen

2022

IEEE Access

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“…PID and its variant controllers are prone to some shortcoming such as parameter tuning and uncertainty about it [23]. There are some works devoted to the tuning of the PID controllers.…”

Section: Related Workmentioning

confidence: 99%

“…Then, velocity and positions are calculated according to Eqs. (22)(23). Algorithm continues until the pre-defined conditions are satisfied.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A Comparative Study on The Tuning of The PID Flight Controllers Using Swarm Intelligence

Kaba

2020

IJAST

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QUAVs have some shortcomings in terms of nonlinearities, coupled dynamics, unstable open–loop characteristics, and they are prone to internal and external disturbances. Therefore, control problem of the QUAVs is still an open issue. Designed controllers based on the linear dynamics have limited operating ranges. Therefore, nonlinear dynamics of the QUAVs must be derived and used in the control problem. Although some advanced controllers are presented for QUAV control, PID controllers are the most employed, well–known controllers with the simple structure, ease of implementation, solid functionality and robustness amongst the variations up to a degree. In this paper, PID based controllers are proposed for the nonlinear attitude dynamics to overcome the control problem of the QUAVs. However, since optimality and tuning of the PID controllers are fuzzy because of trial and error approaches, swarm intelligence based meta-heuristic algorithms (ABC, ACO and PSO) are employed to optimize the PID coefficients. Results are compared in terms of transient analysis and MC analysis to cover the rise time, settling time, percentage overshoot, steady–state error for the former and stochastic fitness evaluation for the latter, respectively.

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“…PSO is widely applied to autotune the PID parameter because of its effectiveness and efficiency in handling nonlinear and easy implementation [35], [36]. However, it still has the limitation of falling to the local minima and converging [37], [38].…”

Section: Introductionmentioning

confidence: 99%

Proportional-Integral-Derivative Parametric Autotuning by Novel Stable Particle Swarm Optimization (NSPSO)

2022

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To improve the performance, robustness and stability of autotuning the proportional integral and derivative (PID) parameter, the novel stable particle swarm optimization (NSPSO) is proposed in this paper. The NSPSO is the combination of the particle swarm and optimization algorithm with the new stable rule to reconsider the survival of the remaining particle in the search space for handling the instability of the system. The new rule is proposed based on proving the stability according to the Lypunov stability theorem. Additionally, to show the method's superiority in performance and robustness, the proposed method is compared with the results of simulations with the particle swarm optimization (PSO), the hybrid particle swarm optimization-grey wolf optimization (PSO-GWO), the whale optimization algorithm (WOA) and the social spider optimization algorithm (SSO) based on a direct current (DC) motor control system. In the comparative performance, the various fitness functions are applied, while the comparative robustness and the changed operation point of the DC motor are applied. After comparing the methods, the proposed method obtains better results than the PSO, PSO-GWO, WOA and SSO in both performance and robustness.INDEX TERMS DC motor, Lypunov stability, optimization, particle swarm and optimization (PSO), Proportional integral and derivative (PID).

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A Novel Approach to Control of Piezo-Transducer in Microelectronics Packaging: PSO-PID and Editing Trajectory Optimization

Cited by 16 publications

References 25 publications

Novel Feedrate Optimization Method for NURBS Tool Paths Under Various Constraints

Novel Feedrate Optimization Method for NURBS Tool Paths Under Various Constraints

A Comparative Study on The Tuning of The PID Flight Controllers Using Swarm Intelligence

Proportional-Integral-Derivative Parametric Autotuning by Novel Stable Particle Swarm Optimization (NSPSO)

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