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In robotics, non-linear least squares estimation is a common technique for simultaneous localization and mapping. One of the remaining challenges are measurement outliers leading to inconsistency or even divergence within the optimization process. Recently, several approaches for robust state estimation dealing with outliers inside the optimization back-end were presented, but all of them include...
the optimization algorithm plays an important role in solving the complex problems, and many complex problems can be modeled as a combinatorial optimization problem. The multi-dimensional knapsack problem is a kind of typical combinatorial optimization problem. The pollination algorithm is a kind of natural heuristic algorithm proposed in recent years, which has the characteristics of few parameter...
In this paper, we investigate distributed consensus problems for multiple miniature aerial vehicles (MAVs) with nonlinear dynamics and uncertainty. We develop distributed consensus protocol to solve regulation synchronization problem for leaderless MAVs with directed interaction topology. Adaptive control algorithms are used locally for each vehicle to deal with nonlinear dynamics and uncertainty...
It is known that the Gradient descent bit flipping (GDBF) algorithm is an effective hard-decision decoding algorithm for low-density parity-check (LDPC) codes. However, trapping in a local maximum limits its error-rate performance. This paper presents a modified GDBF scheme that can mitigate the trapping problem and hence can improve the error-rate performance. Compared to the conventional GDBF algorithm,...
Dual methods can handle easily complicated constraints in convex problems, but they have typically slow (sublinear) convergence rate in an average primal point, even when the original problem has smooth strongly convex objective function. Primal projected gradient-based methods achieve linear convergence for constrained, smooth and strongly convex optimization, but it is difficult to implement them,...
The bare bones particle swarm optimization (BBPSO) is a population-based algorithm. The BBPSO is famous for easy coding and fast applying. A Gaussian distribution is used to control the behavior of the particles. However, every particle learning from a same particle may cause the premature convergence. To solve this problem, a new hierarchical bare bones particle swarm optimization algorithm is proposed...
This paper considers the depth control problem of autonomous underwater vehicles (AUVs) in discrete time. A neural-network-based deterministic policy gradient (NNDPG) controller is proposed by combining the deterministic policy gradient theorem with neural networks. Two networks, evaluation network and policy network, are designed to respectively approximate the long-term cost function and policy...
For task completion in distributed environments, a set of resources is required and a group of agents must cooperate in deciding the share each should provide to maximize the system performance. We address the problem from an evolutionary game-theoretic perspective and present a fully distributed algorithm based on local replicator dynamics. By using the optimality condition, we prove the convergence...
Based on the mobile robot path planning problem, on the basis of the improved grid method, this paper proposes an improved ant colony algorithm, the particle swarm optimization algorithm can be incorporated into the ant colony algorithm. Firstly, using the particle swarm optimization algorithm to search for global path roughly. At the same time of search for dynamic pheromone intelligent distribution,...
In this paper, we aim to improve the overall performance of kernel adaptive filters by adaptively combining several component filters with different parameters setting in the practical applications. The convex combination scheme is exploited to incorporate any two parallel diversity branches which could be the component filter or the output of previous combination layer. The proposed convex combination...
Average-consensus filter problem is investigated for a mixed-order multi-agent system, which consists of first-order and second-order agents, and the proportional-integral consensus filter algorithms are proposed for the agents with different constant inputs. Based on generalized Nyquist stability criterion, sufficient convergence conditions are obtained for the multiagent system under a fixed, symmetric...
In this paper, a particle swarm optimization method with a new strategy for inertia weight has been considered. The author abandoned the commonly used linear inertia weight and proposed a new dynamic inertia weight based on fitness of the particles. The new weight is a function of the best and the worst fitness of the particles. The considered NIWPSO algorithm was tested on a set of benchmark functions...
In this paper, an adaptive genetic algorithm based on multi-population elite selection strategy is proposed. The multi-population elite selection strategy is used to preserve the optimal individuals of each group. Finally, these optimal individuals formed a population, and then use the improved adaptive genetic algorithm to finish the solution. By comparing the simulation experiments of TSP problem...
A simple Line-Of-Sight (LOS) based trajectory tracking algorithm intended for underactuated vehicles is presented. Time parametrized trajectories in two dimensions are considered. Local exponential stability for straight trajectories and ultimate boundedness for general trajectories is shown for the guidance law error dynamics, using Lyapunov perturbation analysis. The results are verified in simulations...
LDPC codes have been applied in recent communication standards, such as WiFi, WiGig, and 10GBased-T Ethernet as a forward error correction code. However, LDPC codes require a large number of computational complexity for high performances. To solve this problem, various studies have been continuously performed for reducing computational complexity. In this paper, we propose an adaptive forced convergence...
Distributed and cooperative algorithms are of preponderant importance for the correct operation of multiagent systems. In particular, average consensus algorithms represent an appealing alternative for combining measurements in large-scale networks of low-capable sensors, due to their low computational cost and strong convergence properties. However, the actual performance of average consensus algorithms...
Based on the biological mechanism of immune algorithm, an improved immune genetic algorithm is proposed, in which particle swarm optimization is taken as global searching strategy to improve the global search ability of the immune genetic algorithm, and progressive optimization algorithm is used for evolving operation of control strategy to improve its local search ability. At the same time, because...
It is a research hotspot that the evolutionary algorithm is applied to the nonlinear parameters identification of turbo-generator speed governor system, but the single evolutionary algorithms have varying degrees of defects: the premature convergence and slow convergence speed. A hybrid optimization algorithm (DEPSO) is proposed to overcome single evolutionary limitations based on the combination...
There were many researches about the parameter estimation of canonical dynamic systems recently. Extended Kalman filter (EKF) is a popular parameter estimation method in virtue of its easy applications. This paper focuses on parameter estimation for a class of canonical dynamic systems by EKF. By constructing associated differential equation, the convergence of EKF parameter estimation for the canonical...
In this paper, a multi-agent distributed continuous-time algorithm is proposed to solve a large-scale linear algebraic equation Ax = do. Unlike many existing results assuming each agent knows a few rows of A, the algorithm proposed in this paper assumes each agent knows a few columns of A. To solve the linear algebraic equation, the problem is first converted to an optimization problem with a linear...
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