Volume 15, Number 1, 2018
Display Method:
2018, vol. 15, no. 1,
pp. 1-12, doi: 10.1007/s11633-017-1101-z
Abstract:
In this paper, a vibration motion control is proposed and implemented on a foamed polystyrene machining robot to suppress the generation of undesirable cusp marks, and the basic performance of the controller is verified through machining experiments of foamed polystyrene. Then, a 3 dimensional (3D) printer-like data interface is proposed for the machining robot. The 3D data interface enables to control the machining robot directly using stereolithography (STL) data without conducting any computer-aided manufacturing (CAM) process. This is done by developing a robotic preprocessor that helps to remove the need for the conventional CAM process by directly converting the STL data into cutter location source data called cutter location (CL) or cutter location source (CLS) data. The STL is a file format proposed by 3D systems, and recently is supported by many computer aided design (CAD)/CAM softwares. The STL is widely used for rapid prototyping with a 3D printer which is a typical additive manufacturing system. The STL deals with a triangular representation of a curved surface geometry. The developed 3D printer-like data interface allows to directly control the machining robot through a zigzag path, rectangular spiral path and circular spiral path generated according to the information included in STL data. The effectiveness and usefulness of the developed system are demonstrated through actual machining experiments.
In this paper, a vibration motion control is proposed and implemented on a foamed polystyrene machining robot to suppress the generation of undesirable cusp marks, and the basic performance of the controller is verified through machining experiments of foamed polystyrene. Then, a 3 dimensional (3D) printer-like data interface is proposed for the machining robot. The 3D data interface enables to control the machining robot directly using stereolithography (STL) data without conducting any computer-aided manufacturing (CAM) process. This is done by developing a robotic preprocessor that helps to remove the need for the conventional CAM process by directly converting the STL data into cutter location source data called cutter location (CL) or cutter location source (CLS) data. The STL is a file format proposed by 3D systems, and recently is supported by many computer aided design (CAD)/CAM softwares. The STL is widely used for rapid prototyping with a 3D printer which is a typical additive manufacturing system. The STL deals with a triangular representation of a curved surface geometry. The developed 3D printer-like data interface allows to directly control the machining robot through a zigzag path, rectangular spiral path and circular spiral path generated according to the information included in STL data. The effectiveness and usefulness of the developed system are demonstrated through actual machining experiments.
2018, vol. 15, no. 1,
pp. 13-24, doi: 10.1007/s11633-017-1095-6
Abstract:
This paper brings out a structured methodology for identifying intervals of communication time-delay where consensus in directed networks of multiple agents with high-order integrator dynamics is achieved. It is built upon the stability analysis of a transformed consensus problem which preserves all the nonzero eigenvalues of the Laplacian matrix of the associated communication topology graph. It is shown that networks of agents with first-order integrator dynamics can be brought to consensus independently of communication delay, on the other hand, for agents with second-order integrator dynamics, the consensus is achieved independently of communication delay only if certain conditions are satisfied. Conversely, if such conditions are not satisfied, it is shown how to compute the intervals of communication delay where multiple agents with second-order or higher-order can be brought to consensus. The paper is ended by showing an interesting example of a network of agents with second-order integrator dynamics which is consensable on the first time-delay interval, but as the time-delay increases, it loses consensability on the second time-delay interval, then it becomes consensable again on the third time-delay interval, and finally it does not achieve consensus any more on the fourth time-delay interval. This example shows the importance of analyzing consensus with time-delay in different intervals.
This paper brings out a structured methodology for identifying intervals of communication time-delay where consensus in directed networks of multiple agents with high-order integrator dynamics is achieved. It is built upon the stability analysis of a transformed consensus problem which preserves all the nonzero eigenvalues of the Laplacian matrix of the associated communication topology graph. It is shown that networks of agents with first-order integrator dynamics can be brought to consensus independently of communication delay, on the other hand, for agents with second-order integrator dynamics, the consensus is achieved independently of communication delay only if certain conditions are satisfied. Conversely, if such conditions are not satisfied, it is shown how to compute the intervals of communication delay where multiple agents with second-order or higher-order can be brought to consensus. The paper is ended by showing an interesting example of a network of agents with second-order integrator dynamics which is consensable on the first time-delay interval, but as the time-delay increases, it loses consensability on the second time-delay interval, then it becomes consensable again on the third time-delay interval, and finally it does not achieve consensus any more on the fourth time-delay interval. This example shows the importance of analyzing consensus with time-delay in different intervals.
2018, vol. 15, no. 1,
pp. 25-38, doi: 10.1007/s11633-016-1046-7
Abstract:
The goal of this paper is to propose a unique control method that permits the evolution of both timed continuous Petri net (TCPN) and T-timed discrete Petri net (T-TDPN) from an initial state to a desired one. Model predictive control (MPC) is a robust control scheme against perturbation and a consistent real-time constraints method. Hence, the proposed approach is studied using the MPC. However, the computational complexity may prevent the use of the MPC for large systems and for large prediction horizons. Then, the proposed approach provides some new techniques in order to reduce the high computational complexity; among them one is taking constant control actions during the prediction.
The goal of this paper is to propose a unique control method that permits the evolution of both timed continuous Petri net (TCPN) and T-timed discrete Petri net (T-TDPN) from an initial state to a desired one. Model predictive control (MPC) is a robust control scheme against perturbation and a consistent real-time constraints method. Hence, the proposed approach is studied using the MPC. However, the computational complexity may prevent the use of the MPC for large systems and for large prediction horizons. Then, the proposed approach provides some new techniques in order to reduce the high computational complexity; among them one is taking constant control actions during the prediction.
2018, vol. 15, no. 1,
pp. 39-51, doi: 10.1007/s11633-017-1099-2
Abstract:
In this paper, some issues related to design and analysis of real networked control systems (NCS) under the focus of the most likely region of stability are addressed. Such a system is cumbersome due to its inherent variable time delays, ranging from microseconds to hours. To show the influence of such huge variations in the control performance, a laboratory-scale luminosity system has been setup using the Internet as part of the control loop with dominant time constant in the order of milliseconds. Proportional and integral (PI) control strategies with and without explicit compensation for the time-delay variations were implemented using an event-driven controller. Using the well-known Monte Carlo method and subsequent analyses of time responses, it has been possible to identify the most likely region of stability. Some experimental results show the influence of the statistical parameters of the delays on the determination of the most likely regions of stability of the NCS and how these can be used in assessment and redesign of the control system. The experiments show that much larger delays than one sample period can be supported by real NCSs without becoming unstable.
In this paper, some issues related to design and analysis of real networked control systems (NCS) under the focus of the most likely region of stability are addressed. Such a system is cumbersome due to its inherent variable time delays, ranging from microseconds to hours. To show the influence of such huge variations in the control performance, a laboratory-scale luminosity system has been setup using the Internet as part of the control loop with dominant time constant in the order of milliseconds. Proportional and integral (PI) control strategies with and without explicit compensation for the time-delay variations were implemented using an event-driven controller. Using the well-known Monte Carlo method and subsequent analyses of time responses, it has been possible to identify the most likely region of stability. Some experimental results show the influence of the statistical parameters of the delays on the determination of the most likely regions of stability of the NCS and how these can be used in assessment and redesign of the control system. The experiments show that much larger delays than one sample period can be supported by real NCSs without becoming unstable.
A Time/Space Separation Based 3D Fuzzy Modeling Approach for Nonlinear Spatially Distributed Systems
2018, vol. 15, no. 1,
pp. 52-65, doi: 10.1007/s11633-017-1080-0
Abstract:
Spatially distributed systems (SDSs) are usually infinite-dimensional spatio-temporal systems with unknown nonlinearities. Therefore, to model such systems is difficult. In real applications, a low-dimensional model is required. In this paper, a time/space separation based 3D fuzzy modeling approach is proposed for unknown nonlinear SDSs using input-output data measurement. The main characteristics of this approach is that time/space separation and time/space reconstruction are fused into a novel 3D fuzzy system. The modeling methodology includes two stages. The first stage is 3D fuzzy structure modeling which is based on Mamdani fuzzy rules. The consequent sets of 3D fuzzy rules consist of spatial basis functions estimated by Karhunen-Love decomposition. The antecedent sets of 3D fuzzy rules are used to construct temporal coefficients. Going through 3D fuzzy rule inference, each rule realizes time/space synthesis. The second stage is parameter identification of 3D fuzzy system using particle swarm optimization algorithm. After an operation of defuzzification, the output of the 3D fuzzy system can reconstruct the spatio-temporal dynamics of the system. The model is suitable for the prediction and control design of the SDS since it is of low-dimension and simple nonlinear structure. The simulation and experiment are presented to show the effectiveness of the proposed modeling approach.
Spatially distributed systems (SDSs) are usually infinite-dimensional spatio-temporal systems with unknown nonlinearities. Therefore, to model such systems is difficult. In real applications, a low-dimensional model is required. In this paper, a time/space separation based 3D fuzzy modeling approach is proposed for unknown nonlinear SDSs using input-output data measurement. The main characteristics of this approach is that time/space separation and time/space reconstruction are fused into a novel 3D fuzzy system. The modeling methodology includes two stages. The first stage is 3D fuzzy structure modeling which is based on Mamdani fuzzy rules. The consequent sets of 3D fuzzy rules consist of spatial basis functions estimated by Karhunen-Love decomposition. The antecedent sets of 3D fuzzy rules are used to construct temporal coefficients. Going through 3D fuzzy rule inference, each rule realizes time/space synthesis. The second stage is parameter identification of 3D fuzzy system using particle swarm optimization algorithm. After an operation of defuzzification, the output of the 3D fuzzy system can reconstruct the spatio-temporal dynamics of the system. The model is suitable for the prediction and control design of the SDS since it is of low-dimension and simple nonlinear structure. The simulation and experiment are presented to show the effectiveness of the proposed modeling approach.
2018, vol. 15, no. 1,
pp. 66-83, doi: 10.1007/s11633-017-1086-7
Abstract:
An accurate short-term wind speed prediction algorithm based on the efficient kernel ridge pseudo inverse neural network (KRPINN) variants is proposed in this paper. The use of nonlinear kernel functions in pseudo inverse neural networks eliminates the trial and error approach of choosing the number of hidden layer neurons and their activation functions. The robustness of the proposed method has been validated in comparison with other models such as pseudo inverse radial basis function (PIRBF) and Legendre tanh activation function based neural network, i.e., PILNNT, whose input weights to the hidden layer weights are optimized using an adaptive firefly algorithm, i.e., FFA. However, since the individual kernel functions based KRPINN may not be able to produce accurate forecasts under chaotically varying wind speed conditions, a linear combination of individual kernel functions is used to build the multi kernel ridge pseudo inverse neural network (MK-RPINN) for providing improved forecasting accuracy, generalization, and stability of the wind speed prediction model. Several case studies have been presented to validate the accuracy of the short-term wind speed prediction models using the real world wind speed data from a wind farm in the Wyoming State of USA over time horizons varying from 10 minutes to 5 hours.
An accurate short-term wind speed prediction algorithm based on the efficient kernel ridge pseudo inverse neural network (KRPINN) variants is proposed in this paper. The use of nonlinear kernel functions in pseudo inverse neural networks eliminates the trial and error approach of choosing the number of hidden layer neurons and their activation functions. The robustness of the proposed method has been validated in comparison with other models such as pseudo inverse radial basis function (PIRBF) and Legendre tanh activation function based neural network, i.e., PILNNT, whose input weights to the hidden layer weights are optimized using an adaptive firefly algorithm, i.e., FFA. However, since the individual kernel functions based KRPINN may not be able to produce accurate forecasts under chaotically varying wind speed conditions, a linear combination of individual kernel functions is used to build the multi kernel ridge pseudo inverse neural network (MK-RPINN) for providing improved forecasting accuracy, generalization, and stability of the wind speed prediction model. Several case studies have been presented to validate the accuracy of the short-term wind speed prediction models using the real world wind speed data from a wind farm in the Wyoming State of USA over time horizons varying from 10 minutes to 5 hours.
2018, vol. 15, no. 1,
pp. 84-93, doi: 10.1007/s11633-016-1036-9
Abstract:
In this paper, a fractional order proportional integral derivative (FOPID) controller for multiarea automatic generation control (AGC) scheme has been designed. FOPID controller has five parameters and provides two additional degrees of flexibility in comparison to a proportional integral derivative (PID) controller. The optimal values of parameters of FOPID controller have been determined using Big Bang Big Crunch (BBBC) search algorithm. The designed controller regulates real power output of generators to achieve the best dynamic response of frequency and tie-line power on a load perturbation. The complete scheme for designing of the controllers has been developed and demonstrated on multiarea deregulated power system. The performance of the designed FOPID controllers has been compared with the optimally tuned PID controllers. It is observed from the results that the FOPID controller shows a considerable improvement in the performance as compared to the conventional PID controller.
In this paper, a fractional order proportional integral derivative (FOPID) controller for multiarea automatic generation control (AGC) scheme has been designed. FOPID controller has five parameters and provides two additional degrees of flexibility in comparison to a proportional integral derivative (PID) controller. The optimal values of parameters of FOPID controller have been determined using Big Bang Big Crunch (BBBC) search algorithm. The designed controller regulates real power output of generators to achieve the best dynamic response of frequency and tie-line power on a load perturbation. The complete scheme for designing of the controllers has been developed and demonstrated on multiarea deregulated power system. The performance of the designed FOPID controllers has been compared with the optimally tuned PID controllers. It is observed from the results that the FOPID controller shows a considerable improvement in the performance as compared to the conventional PID controller.
2018, vol. 15, no. 1,
pp. 94-102, doi: 10.1007/s11633-016-1012-4
Abstract:
Boundary control for a class of partial integro-differential systems with space and time dependent coefficients is considered. A control law is derived via the partial differential equation (PDE) backstepping. The existence of kernel equations is proved. Exponential stability of the closed-loop system is achieved. Simulation results are presented through figures.
Boundary control for a class of partial integro-differential systems with space and time dependent coefficients is considered. A control law is derived via the partial differential equation (PDE) backstepping. The existence of kernel equations is proved. Exponential stability of the closed-loop system is achieved. Simulation results are presented through figures.
2018, vol. 15, no. 1,
pp. 103-114, doi: 10.1007/s11633-016-0990-6
Abstract:
In this paper, a hybrid particle swarm optimization (PSO) algorithm with differential evolution (DE) is proposed for numerical benchmark problems and optimization of active disturbance rejection controller (ADRC) parameters. A chaotic map with greater Lyapunov exponent is introduced into PSO for balancing the exploration and exploitation abilities of the proposed algorithm. A DE operator is used to help PSO jump out of stagnation. Twelve benchmark function tests from CEC2005 and eight real world optimization problems from CEC2011 are used to evaluate the performance of the proposed algorithm. The results show that statistically, the proposed hybrid algorithm has performed consistently well compared to other hybrid variants. Moreover, the simulation results on ADRC parameter optimization show that the optimized ADRC has better robustness and adaptability for nonlinear discrete-time systems with time delays.
In this paper, a hybrid particle swarm optimization (PSO) algorithm with differential evolution (DE) is proposed for numerical benchmark problems and optimization of active disturbance rejection controller (ADRC) parameters. A chaotic map with greater Lyapunov exponent is introduced into PSO for balancing the exploration and exploitation abilities of the proposed algorithm. A DE operator is used to help PSO jump out of stagnation. Twelve benchmark function tests from CEC2005 and eight real world optimization problems from CEC2011 are used to evaluate the performance of the proposed algorithm. The results show that statistically, the proposed hybrid algorithm has performed consistently well compared to other hybrid variants. Moreover, the simulation results on ADRC parameter optimization show that the optimized ADRC has better robustness and adaptability for nonlinear discrete-time systems with time delays.
2018, vol. 15, no. 1,
pp. 115-124, doi: 10.1007/s11633-017-1092-9
Abstract:
Activity is now playing a vital role in software processes. To ensure the high-level efficiency of software processes, a key point is to locate those activities that own bigger resource occupation probabilities with respect to average execution time, called delayed activities, and then improve them. To this end, we firstly propose an approach to locating delayed activities in software processes. Furthermore, we present a case study, which exhibits the high-level efficiency of the approach, to concretely illustrate this new solution. Some beneficial analysis and reasonable modification are developed in the end.
Activity is now playing a vital role in software processes. To ensure the high-level efficiency of software processes, a key point is to locate those activities that own bigger resource occupation probabilities with respect to average execution time, called delayed activities, and then improve them. To this end, we firstly propose an approach to locating delayed activities in software processes. Furthermore, we present a case study, which exhibits the high-level efficiency of the approach, to concretely illustrate this new solution. Some beneficial analysis and reasonable modification are developed in the end.
