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In this paper, an algorithm for dynamic path generation in urban environments is presented, taking into account structural and sudden changes in straight and bend segments (e.g. roundabouts and intersections). The results present some improvements in path generation (previously hand plotted) considering parametric equations and continuous-curvature algorithms, which guarantees a comfortable lateral...
Existing mainstream indoor localization technologies mainly rely on RF signatures and thus incur significant and recurring labor cost to measure the time-varying signature map. We have proposed a smartphone localization system using the embedded gyroscope for triangulation from nearby physical features (e.g., store logos) recognized from photo-taking. It requires a much reduced and one-time measurement,...
In the past decade, adaptive dynamic programming (ADP) has been widely used to realize online learning tracking control of dynamical systems, where neural networks with manually designed features are commonly used. In order to improve the generalization capability and learning efficiency of ADP, this paper presents a novel framework of ADP with sparse kernel machines by integrating kernel methods...
This paper proposes two consensus algorithms for second-order nonlinear multi-agent dynamics. The objective of the algorithms is to guarantee two states of each agent reach consensus. Consensus is achieved asymptotically based on two “virtual states” in each agent. Both of the consensus algorithms use backstepping control to deal with the nonlinear section in each agent. By the first algorithm, the...
An electronic reverse auction system with one buyer and multiple suppliers is considered in this paper. The buyer procures multi-items from potential suppliers with unconstrained capacity and the suppliers bid competitively on combinations of items in the system. As an important decision problem from the buyer's perspective, a winner determination problem (WDP) of multi-items single-unit combinatorial...
Considering the features of coupling, multivariable, and nonlinearity in thickness and tension system for cold-rolling, this paper proposes a dynamic coupling model. Furthermore, a new compound decoupling control algorithm is designed based on diagonal recurrent neural network combined with PID(DRNN-PID) for decoupling control. Simulation results show the proposed algorithm has stronger adaptive tracking,...
This paper reviews the integrated perturbation analysis - sequential quadratic programming (IPA-SQP) approach. The IPA-SQP approach has been proposed to address computational challenges in nonlinear model predictive control (MPC) problems. This approach combines the complementary features of perturbation analysis and sequential quadratic programming in a unified framework. An overview of the IPA-SQP...
In this paper, we establish a decentralized control law to stabilize a class of nonlinear interconnected large-scale systems using a neural-network-based online model-free integral policy iteration algorithm. The model-free approach can solve the decentralized control problem for the interconnected system which has unknown dynamics. The stabilizing decentralized control strategy is derived based on...
The simulation methods based on stochastic realizations of state vector evolutions derived from the time convolutionless master equation are investigated. The quantum jumps for non-Markovian systems and their waiting time distribution is mainly focused on. With the help of an exact analytical waiting time distribution, the non-Markovian quantum trajectory for a stochastic process of a driven two-level...
This paper uses the integral reinforcement learning (IRL) technique to develop an online learning algorithm for finding suboptimal static output-feedback controllers for partially-unknown continuous-time (CT) linear systems. To our knowledge, this is the first static output-feedback control design method based on reinforcement learning for CT systems. In the proposed method, an online policy iteration...
This paper presents the linear quadratic tracking (LQT) control of a quadrotor UAV by solving discrete time matrix difference Riccati Equation. First, the nonlinear dynamic model of the quadrotor is obtained by using Newton's equations of motion. Then, the nonlinear dynamic model is linearized around hover condition. The linearized dynamic model is used to solve the optimal control problem. A trade...
In this paper, a High Precise Optimization Algorithm for manipulating multi-layered feed-forward neural network is studied. Its basic principle is: defining neural network average error as objective function, weights and thresholds as design variables, through design variables rationally sorted, objective function is dynamically formed. Compared the new method with BP, the optimum step-length can...
In this paper, we apply statistical mechanics methods to the problem of detection of multiple primary wireless sources by a wireless sensor network. We assume that the location of the primary sources is known, but that the channel connecting them to the sensors is random. The sensor network tries to detect which sources are emitting by employing a belief propagation algorithm. We use the Replica approach...
The particle swarm optimization (PSO) is an algorithm that attempts to search for better solution in the solution space by attracting particles to converge toward a particle with the best fitness. PSO is typically troubled with the problems of trapping in local optimum and premature convergence. In order to overcome both problems, we propose an improved PSO algorithm that is applied mutation operator...
The power demand over the electrical power system and smart grid is a random function in the time domain which is affected by a larger number of stochastic factors, for example weather, date and economy as well as a series of unpredictable human factors. Therefore, the most convenient and efficient methodology to forecast the power demand is a stochastic model based on statistics and fuzzy mathematics,...
Abstract-We present in this paper a new algorithm based on Particle Swarm Optimization (PSO) for solving Dynamic Single Objective Constrained Optimization (DCOP) problems. We have modified several different parameters of the original particle swarm optimization algorithm by introducing new types of particles for local search and to detect changes in the search space. The algorithm is tested with a...
OFDMA has been selected as the multiple access scheme for emerging broadband wireless communication systems. However, designing efficient resource allocation algorithms for OFDMA systems is a challenging task, especially in the uplink, due to the combinatorial nature of subcarrier assignment and the distributed power budget for different users. Inspired by Glauber dynamics, in this paper, we propose...
Grid computing involves sharing data storage and coordinating network resources. The complexity of scheduling increases with heterogeneous nature of grid and is highly difficult to schedule effectively. The goal of grid job scheduling is to achieve high system performance and match the job to the appropriate available resource. Due to dynamic nature of grid, the traditional job scheduling algorithms...
The Enhance Neuro-fuzzy system for classification using dynamic clustering presents in this paper is an extension of the original Neuro-fuzzy method for linguistic feature selection and rule-based classification. The new algorithm resolves the limitations of the original algorithm that uses only 3 membership functions for all features to fine the appropriate function for each feature. Each feature...
This paper presents the dynamic modeling of floating systems with application for three-dimensional swimming eel-like robot and rowing-like system. To obtain the Cartesian evolution during the design or control of these systems the dynamic models must be used. Owing to the complexity of such systems efficient and simple tools are needed to obtain their model. For this goal we propose an efficient...
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