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Showing papers in "Control and Decision in 2005"


Journal Article
TL;DR: The outstanding advantage of Tent map is discussed to compare with the hybrid algorithms based on chaotic map, and a hybrid optimization algorithm with higher search speed is constructed.
Abstract: The outstanding advantage of Tent map is discussed to compare with the hybrid algorithms based on chaotic map. Combined with the pattern search algorithm, a hybrid optimization algorithm with higher search speed is constructed. Examples show the feasibility of the algorithm, as well as the practicability of Tent map.

97 citations


Journal Article
TL;DR: The advances of unscented Kalman filter (UKF) are firstly reviewed in this article, where the combination of the Kalman linear filtering with the unscenting transformation (UT) is discussed in general sense.
Abstract: The advances of unscented Kalman filter (UKF) are firstly reviewed. The combination of the Kalman linear filtering with the unscented transformation (UT) is discussed in general sense. UKF and its typical sampling strategies are given and analyzed. Finally, the possible future directions of the UKF are also discussed.

63 citations


Journal Article
TL;DR: To the problem of aggregation and expression of bias with linguistic assessment information in the multi-attribute group decision making, the bias expression, bias aggregation and the alternative selection of experts based on cloud model are studied.
Abstract: Abstrcat To the problem of aggregation and expression of bias with linguistic assessment information in the multi-attribute group decision making, the bias expression,the bias aggregation and the alternative selection of experts based on cloud model are studied. Cloud model is used to express the linguistic assessment information given by each decision maker. The power of attribute and decision maker is calculated by the mood arithmetic of cloud. The bias aggregation is executed by means of floating cloud. The order and alternative of selection is determined according to the relative distance of cloud model. The burring and randomicity of assessment is fully expressed in this method.

53 citations


Journal Article
TL;DR: In this article, the generic ideas of particle filter are given, based on the analysis of standard algorithm of sampling-importance-resampling filter, the problems of particle filtering are discussed and some improvement methods are illustrated, from view of probability density function, the comparisons between particle filter and others nonlinear filter algorithms and applicability are introduced, some applications in the developed areas are reviewed, further research directions are pointed out.
Abstract: The Algorithm and applications related to particle filter are surveyed. Aiming at the nonlinear/non-Gaussian filter problem, the generic ideas of particle filter are given, based on the analysis of standard algorithm of sampling-importance-resampling filter, the problems of particle filter are discussed and some improvement methods are illustrated. From view of probability density function, the comparisons between particle filter and others non-linear filter algorithms and applicability are introduced, some applications in the developed areas are reviewed, Finally, further research directions are pointed out.

42 citations


Journal Article
TL;DR: A global path planning approach based on particle swarm optimization (PSO) is presented that has a simple model, low complexity, rapid convergence and no restrict on the shapes of obstacles.
Abstract: A global path planning approach based on particle swarm optimization (PSO) is presented. The first step is to make a new map between starting-point and goal-point through coordinate system transferring. Then the PSO is introduced to get a global optimized path. This algorithm has a simple model, low complexity, rapid convergence and no restrict on the shapes of obstacles. Simulation results are provided to verify the effectiveness and practicability.

35 citations


Journal Article
TL;DR: The ant colony algorithm that is often applied to discrete space optimization problems is introduced into continuous space to solve constrained multiobjective optimization problems and shows that the approach possesses high searching efficiency and can efficiently find multiple Pareto optimal solutions.
Abstract: The ant colony algorithm (ACA) that is often applied to discrete space optimization problems is introduced into continuous space to solve constrained multiobjective optimization problems The new pheromone remaining (process) and walking strategy of ants are described In addition, combined with the searching strategy based on (global) best experience, this approach guides the ants to search better solutions The approach is validated using (several) benchmark cases The simulation results show that the approach possesses high searching efficiency and can efficiently find multiple Pareto optimal solutions

31 citations


Journal Article
TL;DR: By using grey system theories, based on the present theories of buffer operators, some strengthening buffer operators are established, which have the universality and practicability as discussed by the authors, while their characters and their inherent relation among them are studied.
Abstract: By using grey system theories,based on the present theories of buffer operators,some strengthening buffer operators are established,which have the universality and practicability.Meanwhile,their characters and their inherent relation among them are studied.The problem that there are some contradictions between quantitative analysis and qualitative analysis in pretreatment for vibration data sequences is resolved effectively.An example shows its validity and practicability.

26 citations


Journal Article
TL;DR: Simulation results show that the proposed algorithm improves the quality of the solution while lowering the computational cost of PSO algorithm, which is an NP-hard problem.
Abstract: Layout optimization is an NP-hard problem It also belongs to complex nonlinear constrained optimization problem In view of this problem, a new methodology based on particle swarm optimization (PSO) is developed to optimize layout parameters A constraint handling strategy suit for PSO is proposed Furthermore, improvement is made by using direct search to intensify local search ability of PSO algorithm Simulation results show that the proposed algorithm improves the quality of the solution while lowering the computational cost

25 citations


Journal Article
TL;DR: A heuristic algorithm for knowledge reduction is designed and an efficient algorithm for computing conditional information entropy is proposed and the result shows that when used as heuristic information, the proposed significance of the attribute is better than the other two.
Abstract: The disadvantages of the current conditional information entropy are analyzed. A new conditional information entropy is proposed. Based on this entropy the new significance of an attribute is defined and compared with two significances of this attribute based on the positive region and the current conditional information entropy respectively. The result shows that when used as heuristic information, the proposed significance of the attribute is better than the other two. Finally, a heuristic algorithm for knowledge reduction is designed and an efficient algorithm for computing conditional information entropy is proposed. Theoretical analysis and experimental results show that time complexity of this reduction algorithm is less than that of the algorithm based on the current conditional information entropy. Also, this reduction algorithm is more capable of finding the minimal or optimal reducts.

24 citations


Journal Article
TL;DR: The simulation results of the New England 39-bus system show that the proposed control scheme and coordination method are very effective in managing global voltage profile of power system as well as settling local voltage violation problems in system contingencies.
Abstract: Within the framework of the multi-agent based secondary voltage control system,the communication methods,collaboration protocol and optimal control strategy are investigated for enhancing the ability of fast and coordinated voltage control in power system.The performance in system contingencies is mainly discussed.The concept of virtual control agency is proposed to adapt the emergent dynamic control environment.Contract net protocol,which is widely used in coordination of multi-agent system(MAS),is introduced to realize optimal coordination and cooperation among control agents.The simulation results of the classic New England system equipped with several static var compensators(SVC) show the effectiveness and flexibility of the control scheme.

24 citations


Journal Article
TL;DR: Numerical experiment results show that the multiclass SVM methods based on binary tree are suitable for practical use and can resolve the unclassifiable region problems in the conventional multiclassSVM methods.
Abstract: The multiclass SVM methods based on binary tree are proposed. The new methods can resolve the unclassifiable region problems in the conventional multiclass SVM methods. To maintain high generalization ability, the most widespread class should be separated at the upper nodes of a binary tree. Hypercuboid and hypersphere class least covers are used to be rules of constructing binary tree. Numerical experiment results show that the multiclass SVM methods are suitable for practical use.

Journal Article
TL;DR: In this article, the problem of exponential stability for networked control systems with time-delay is discussed when the plant state can′t be measured directly, and the open-loop control method of computing control signal based on plant model at intervals of state signal through network is proposed for reducing the usage of the network.
Abstract: The plant state observer is designed and the problem of exponential stability for networked control systems with time-delay is discussed when the plant state can′t measured directly. The open-loop control method of computing control signal based on plant model at intervals of state signal through network is proposed for reducing the usage of the network. Furthermore, necessary and sufficient conditions for system globally exponentially stability are derived for both continuous and discrete plants. These conditions are affected by the network signal update time and the plant model error and network-induced delay. The example illustrates the effectiveness of the stability conditions.

Journal Article
TL;DR: Based on the tuning law between the control and error, and (formulating) the non linear function of each gain parameter, the nonlinear PID controller could be optimized and (constructed) by adopting genetic algorithm.
Abstract: The relationship between the error signal and gain parameters of PID controller is nonlinear , fitting and constructing the nonlinear function of each parameter can be used respectively for the individual tuning each part of PID controller. In this article, it is described that based on the tuning law between the control and error, and (formulating) the nonlinear function of each gain parameter, the nonlinear PID controller could be optimized and (constructed) by adopting genetic algorithm. The simulate results of typical system shows that the dynamic and static (performances) of the system can be both attended to at a certain degree.

Journal Article
TL;DR: Numerical simulation on typical problems show that the performances of RQGA are the best among all testing algorithms and it is much robust on parameters and initial conditions.
Abstract: Quantum algorithm (QA) with updating of quantum gates and catastrophe of population is compared with quantum genetic algorithm (QGA) by including crossover and mutation for quantum bits. Furthermore, a framework of hybrid quantum GA is proposed which combines the quantum based search and classic genetic search, and hybrid QGA with binary encoding (BQGA) and hybrid QGA with real encoding (RQGA) are presented. Numerical simulation on typical problems show that the performances of RQGA are the best among all testing algorithms and it is much robust on parameters and initial conditions.

Journal Article
TL;DR: A model of large-scale swarm based on individual local information and an individual motion control equation and good adaptability, robustness and scalability emerge in the proposed model and control algorithm.
Abstract: A model of large-scale swarm based on individual local information and an individual motion control equation are proposed. The stability of aggregating behavior of large-scale intelligent swarm is studied with Lyapunov stability theory. The model only rely on the local information between the mutual observable individuals. Combined with the proposed individual local control algorithm, the stable global aggregating behavior can be achieved easily. Good adaptability, robustness and scalability emerge in the proposed model and control algorithm.

Journal Article
TL;DR: In this article, particle swarm optimization (PSO) is combined with simulated annealing (SA) as a local search algorithm to avoid becoming trapped in a local optimum, and an easily implemented (hybrid) algorithm for the multi-objective (Flexible) job-shop scheduling problem (FJSP) is presented.
Abstract: Particle swarm optimization (PSO) is discussed, which combines local search and global search, (possessing) high search efficiency. Simulated annealing (SA) as a local search algorithm employs certain probability to avoid becoming trapped in a local optimum. By reasonably hybridizing these two methodologies, an easily (implemented) (hybrid) algorithm for the multi-objective (Flexible) job-shop scheduling problem (FJSP) is presented. The computational results show that the proposed algorithm is a viable and effective approach for the multi-objective FJSP.

Journal Article
TL;DR: By using linear matrix inequalities (LMIs), a sufficient and necessary condition that guarantees the admissibility and strict dissipativeness of linear singular systems is presented in this paper, and sufficient conditions are derived for the existence of state feedback and dynamic output feedback strictly dissipative controllers.
Abstract: By using linear matrix inequalities (LMIs), a sufficient and necessary condition that guarantees the (admissibility) and strict dissipativeness of linear singular systems is presented. And sufficient conditions are derived for the existence of state feedback and dynamic output feedback strictly dissipative controllers. Furthermore, a (sufficient) condition is obtained such that an uncertain singular system is both generalized quadratically stable and strictly dissipative. The state feedback and dynamic output feedback controllers are designed.

Journal Article
TL;DR: An identification model of RBF neural network, based on SA and parallel PSO, is presented, which is used to optimize RBF kernel number-to resolve the problem of random selection.
Abstract: To improve performance of original particle swarm optimization (PSO) algorithm and avoid trapping to local minima, the paraller PSO based on simulated annealing (SA) is proposed. The proposed algorithm combines the fast search optimum ability of parallel PSO with probability jump property of SA. It can maintain the individual diversity and restrain the degenerate phenomenon. Converter vanadium recover is a complicated nonlinear reaction, which is difficult to build up end-point control model. An identification model of RBF neural network, based on SA and parallel PSO, is presented, which is used to optimize RBF kernel number-to resolve the problem of random selection. The model is applied to the prediction of oxygen blast time and simulation indicate the errore of prediction do not surpass twenty percent of true value.

Journal Article
TL;DR: In this paper, a continuous state feedback control law for trajectory tracking is proposed for a nonholonomic mobile robot, which can track the desired trajectory in the global sense as well as in finite time when the desired rotate velocity is a nonzero constant.
Abstract: The finite-time trajectory tracking control problem of a nonholonomic mobile robot is discussed. In contract to traditional finite time control algorithms based on non-continuous feedbacks, the finite time control algorithms based on continuous feedbacks are more suitable for application to control engineering. Using finite time control techniques for continuous systems, a continuous state feedback control law for trajectory tracking is developed. The proposed control law can guarantee that the mobile robot will track the desired trajectory in the global sense as well as in finite time when the desired rotate velocity is a nonzero constant. Simulation results show the effectiveness of the method.

Journal Article
TL;DR: In this article, a successive approximation algorithm of designing feed-forward and feedback optimal controllers is developed, where the original optimal control problem is transformed into a sequence of nonhomogeneous linear two-point boundary value (TPBV) problems.
Abstract: The optimal control problem is considered for nonlinear systems affected by external persistent (disturbances.) A successive (approximation) algorithm of designing feedforward and feedback optimal controllers is (developed.) By using the successive approximation approach, the original optimal control problem is transformed into a sequence of nonhomogeneous linear two-point boundary value (TPBV) problems. The obtained optimal control law consists of analytical linear feedforward and feedback terms and a nonlinear compensation term which is the limit of the adjoint vector sequence. By using the finite-step iteration of nonlinear compensation sequence, a feedforward and feedback suboptimal control law is obtained. Simulations show the result is more robust with respect to external persistent disturbances than the classical feedback optimal control.

Journal Article
TL;DR: A novel AIA based on Euclidean distance and king crossover strategy (DKBAIA) is derived, with results showing that the convergence performance of MDKBAIA is improved greatly, its running speed is enhanced greatly, which is nearly close to the speed of GA.
Abstract: To the drawback that artificial immune algorithm (AIA) usually runs slowly and its convergent speed is also slower than genetic algorithm (GA), a king-crossover strategy is proposed Combining the king-crossover strategy with Euclidean distance based AIA, a novel AIA based on Euclidean distance and king crossover strategy (DKBAIA) is derived The concept of similar antibody matrix and an improving measure are proposed to improve the DKBAIA, thus the improved DKBAIA (MDKBAIA) is obtained Simulation results show that the convergence performance of MDKBAIA is improved greatly, its running speed is also enhanced greatly, which is nearly close to the speed of GA

Journal Article
LU Gui-zhang1
TL;DR: In this article, an introduction in the field of multi-robot formation control is provided with a focus on the various methods that have been put forward by researchers and the directions of further research are proposed.
Abstract: Abstrcat An introduction in the field of multi-robot formation control is provided with a focus on the various methods that have been put forward by researchers. The works had been done in the field are reviewed. The various methods are described and compared. The something that is missing in current research and the factors that must be considered in designing a multi-robot formation system are pointed out. At last, the directions of further research are proposed.

Journal Article
TL;DR: Experiment results show that the algorithm not only acquirs the same precision with that of the classical algorithms, but also is faster than that ofThe classical algorithms.
Abstract: A kind of algorithm for support vector machine (SVM) is proposed,which can train SVM fast and incrementally.The new algorithm selects border vectors which may be support vectors,so as to reduce training samples and advance training speed.Then an incremental algorithm is presented to train SVM by using the selected border vectors.Experiment results show that the algorithm not only acquirs the same precision with that of the classical algorithms,but also is faster than that of the classical algorithms.

Journal Article
TL;DR: A genetic algorithm with table working is developed that can find the optimal or nearly optimal solution efficiently to this mixed 0-1 integer programming model for optimizing the location of distribution centers.
Abstract: Based on the supply cost of commodity and the distribution system characteristics of business to customer E-commerce companies,a mixed 0-1 integer programming model is built for optimizing the location of distribution centers.The model is in fact a special type of classic location-allocation models and has NP-hard complexity.To solve the model,a genetic algorithm with table working is developed.The algorithm can find the optimal or nearly optimal solution efficiently to this model.Examples show that it is a good method to solve the complicated optimization problems of the location of logistics distribution centers.

Journal Article
TL;DR: Simulation shows that the proposed RCQGA not only avoids the shortcoming of binary system coding based QGA prematurity but it also reduces the optimizing complexity with faster convergence speed, more stability, and more powerful optimizing ability.
Abstract: This paper proposed a real-coded chaotic quantum-inspired genetic algorithm(RCQGA) based on the chaotic and coherent characters of Q-bits.In this algorithm,real chromosomes are inversely mapped to Qbits in the solution space.Q-bits probability guided real cross and chaos mutation are used to real chromosomes evolution and searching;Simulation shows that the proposed RCQGA not only avoids the shortcoming of binary system coding based QGA prematurity but it also reduces the optimizing complexity with faster convergence speed,more stability,more powerful optimizing ability.

Journal Article
TL;DR: The immune genetic algorithm based on selection probability of similarity and vector distance based on the mechanism of antibodies' diversity in the immune system is introduced into the genetic algorithm to overcome the shortage of the basic genetic algorithm.
Abstract: To overcome the shortage of the basic genetic algorithm,the mechanism of antibodies' diversity in the immune system is introduced into the genetic algorithm.In the case of keeping individual diversity and improving the level of adaptability of the individual diversity in the population,the immune genetic algorithm based on selection probability of similarity and vector distance is proposed.Meanwhile the general expressing form of the kind of selection probability is given.Secondly,the immune vaccine is introduced into immune genetic algorithm on selection probability of similarity and vector distance to prevent the algorithm degenerative during the process of optimization.The immune genetic algorithm is applied to the 20-city traveling salesman problem,and advanced coding strategy is proposed.Comparing the algorithm with other six algorithms,the results show that the convergent speed of the algorithm is faster than others.

Journal Article
TL;DR: In this article, the robust H ∞ control problem for a class of nonlinear systems with parameter uncertainties is considered, where the uncertain parameter belongs to a known compact set and enters the system nonlinearly, and the system is assumed to admit partially feedback linearization and satisfy disturbance strict triangularity conditions.
Abstract: The problem of robust H_∞ control is considered for a class of nonlinear systems with parameter uncertainties. The uncertain parameter belongs to a known compact set and enters the system nonlinearly. The systems are assumed to admit partially feedback linearization and satisfy disturbance strict triangularity conditions. In the framework of the input-to-state stable theory, based on Lyapunov argument and backstepping design technique a state feedback controller is constructed which renders the closed-loop system internally stable with bounded L_2-gains from exogenous input to output for all admissible parameter uncertainties. No Hamilton-Jacobi equation is needed for the controller design. A simulation example shows the feasibility and effectiveness of the conclusion.

Journal Article
TL;DR: To the inconsistency of decision making in multi-person and multi- attribute decision problems, a multi-attribute group decision problem is described and the composition of the rule set is analyzed to explain the conflicts of the different agents.
Abstract: To the inconsistency of decision making in multi-person and multi-attribute decision problems, a multi-attribute group decision problem is described. Combination analysis is put forward in order to generate more explicit rules. And, the composition of the rule set is analyzed to explain the conflicts of the different agents.

Journal Article
TL;DR: To the problem of human fatigue in interactive genetic algorithm, a neural network based phase estimation of individual fitness is proposed and turning strategy between neural network estimate based individual fitness and (human) evaluation based individual Fitness is given.
Abstract: To the problem of human fatigue in interactive genetic algorithm, a neural network based phase estimation of individual fitness is proposed. Turning strategy between neural network estimate based individual fitness and (human) evaluation based individual fitness is given. The performance index on learning effect of neural network is (also) presented. The complexity of the algorithm is analyzed. The instance results show its validity.

Journal Article
TL;DR: In this paper, the hierarchical identification of state space models for mutivariable systems is studied for systems whose states are measurable, the parameter matrices of state spaces models are directly identified using the least squares principle.
Abstract: The combined state and parameter identification algorithm for scalar systems is extended and the hierarchical identification of state space models for mutivariable systems is studied For systems whose states are measurable, the parameter matrices of state space models are directly identified using the least squares principle For systems whose states are unmeasurable, according to the hierarchical identification principle, a hierarchical state-space model identification method is presented to estimate unknown parameters and states based on input-output data The hierarchical state space model identification is divided into two steps: the system states are assumed to be known (that is, unknown states in parameter estimation algorithm are replaced with their estimates), the parameter estimates are recursively computed based on the state estimates and input-output data; and then the state estimates are recursively computed based on the input-output data and parameter estimates