Standard genetic algorithms （SGAs） are investigated to optimise discrete-time proportional-integral-derivative （PID） con- troller parameters, by three tuning approaches, for a multivariable glass furnace process w...Standard genetic algorithms （SGAs） are investigated to optimise discrete-time proportional-integral-derivative （PID） con- troller parameters, by three tuning approaches, for a multivariable glass furnace process with loop interaction. Initially, standard genetic algorithms （SGAs） are used to identify control oriented models of the plant which are subsequently used for controller optimisa- tion. An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria. The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. While, the third tuning approach utilises a modified cost function which includes the total effect of both controlled variables, glass temperature and excess oxygen. This modified cost function is shown to exhibit improved control robustness and disturbance rejection under loop interaction.展开更多
It is well known that many real-world systems can be described by complex networks with the nodes and the edges representing the individuals and their communications,respectively.Based on recent advances in complex ne...It is well known that many real-world systems can be described by complex networks with the nodes and the edges representing the individuals and their communications,respectively.Based on recent advances in complex networks,this paper aims to provide some new methodologies to study some fundamental problems in smart grids.In particular,it summarises some results for network properties,distributed control and optimisation,and pinning control in complex networks and tries to reveal how these new technologies can be applied in smart grids.展开更多
In this paper,we explore the relationship between dual decomposition and the consensusbased method for distributed optimisation.The relationship is developed by examining the similarities between the two approaches an...In this paper,we explore the relationship between dual decomposition and the consensusbased method for distributed optimisation.The relationship is developed by examining the similarities between the two approaches and their relationship to gradient-based constrained optimisation.By formulating each algorithm in continuous-time,it is seen that both approaches use a gradient method for optimisation with one using a proportional control term and the other using an integral control term to drive the system to the constraint set.Therefore,a significant contribution of this paper is to combine these methods to develop a continuous-time proportional-integral distributed optimisation method.Furthermore,we establish convergence using Lyapunov stability techniques and utilising properties from the network structure of the multi-agent system.展开更多
In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a ＂global＂ nonlinear long-range pre...In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a ＂global＂ nonlinear long-range prediction model through the fuzzy conjunction of a number of ＂local＂ linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.展开更多
This paper presentsa voltage control strategy for power distribution systems with interconnected renewable energy based distributed generators (DGs). The control strategy coordinating conventional voltage control devi...This paper presentsa voltage control strategy for power distribution systems with interconnected renewable energy based distributed generators (DGs). The control strategy coordinating conventional voltage control devices and reactive power from DG.A mixed-integer nonlinear programming problem was formulated and solved by particle swarm optimization (PSO). The code is written using DigSILENT programming language (DPL) and implemented inside DigSILENT power factory simulation software. All system constraints and operating limits are considered. The optimal power flow based approach can incorporate various uncertainties such as intermittent power characteristics and varying load demand. The proposed method is tested using real distribution network to demonstrate its effectiveness. The merits of the proposed method over the classical local-based control are presented in the simulation results. It is demonstrated that the proposed method is capable of keeping the system voltage within operating limit. Power losses is at the same time is minimized in comparison to the losses using conventional method.展开更多
文摘Standard genetic algorithms （SGAs） are investigated to optimise discrete-time proportional-integral-derivative （PID） con- troller parameters, by three tuning approaches, for a multivariable glass furnace process with loop interaction. Initially, standard genetic algorithms （SGAs） are used to identify control oriented models of the plant which are subsequently used for controller optimisa- tion. An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria. The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. While, the third tuning approach utilises a modified cost function which includes the total effect of both controlled variables, glass temperature and excess oxygen. This modified cost function is shown to exhibit improved control robustness and disturbance rejection under loop interaction.
基金This work was supported by the National Science Fund for Excellent Young Scholars[grant number 61322302]the National Science Fund for Distinguished Young Scholars[grant number 61025017]+3 种基金the National Natural Science Foundation of China[grant number 61104145],[grant number 61304168]the Natural Science Foundation of Jiangsu Province of China[grant number BK2011581],[grant number BK20130595]the Research Fund for the Doctoral Program of Higher Education of China[grant number 20110092120024]the Fundamental Research Funds for the Central Universities of China,and the Discovery Scheme under[grant number DP140100544].
文摘It is well known that many real-world systems can be described by complex networks with the nodes and the edges representing the individuals and their communications,respectively.Based on recent advances in complex networks,this paper aims to provide some new methodologies to study some fundamental problems in smart grids.In particular,it summarises some results for network properties,distributed control and optimisation,and pinning control in complex networks and tries to reveal how these new technologies can be applied in smart grids.
基金The work by M.Egerstedt was funded by The Air Force Office of Scientific Research through[grant number 2012-00305-01].
文摘In this paper,we explore the relationship between dual decomposition and the consensusbased method for distributed optimisation.The relationship is developed by examining the similarities between the two approaches and their relationship to gradient-based constrained optimisation.By formulating each algorithm in continuous-time,it is seen that both approaches use a gradient method for optimisation with one using a proportional control term and the other using an integral control term to drive the system to the constraint set.Therefore,a significant contribution of this paper is to combine these methods to develop a continuous-time proportional-integral distributed optimisation method.Furthermore,we establish convergence using Lyapunov stability techniques and utilising properties from the network structure of the multi-agent system.
基金This work was supported by the UK EPSRC (GR/N13319, GR/R10875).
文摘In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a ＂global＂ nonlinear long-range prediction model through the fuzzy conjunction of a number of ＂local＂ linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.
文摘This paper presentsa voltage control strategy for power distribution systems with interconnected renewable energy based distributed generators (DGs). The control strategy coordinating conventional voltage control devices and reactive power from DG.A mixed-integer nonlinear programming problem was formulated and solved by particle swarm optimization (PSO). The code is written using DigSILENT programming language (DPL) and implemented inside DigSILENT power factory simulation software. All system constraints and operating limits are considered. The optimal power flow based approach can incorporate various uncertainties such as intermittent power characteristics and varying load demand. The proposed method is tested using real distribution network to demonstrate its effectiveness. The merits of the proposed method over the classical local-based control are presented in the simulation results. It is demonstrated that the proposed method is capable of keeping the system voltage within operating limit. Power losses is at the same time is minimized in comparison to the losses using conventional method.
