Distributed Adaptive Consensus Control of High Order Unknown Nonlinear Networked Systems with Guaranteed Performance

Adaptive cooperative tracking control with prescribed performance function (PPF) is proposed for high-order nonlinear multi-agent systems. The tracking error originally within a known large set is confined to a smaller predefined set using this approach. Using output error transformation, the constrained system is relaxed and mapped to an unconstrained one. The controller is conceived under the assumption that the agents' nonlinear dynamics are unknown and the perceived network is structured and strongly connected. Under the proposed controller, all agents track the trajectory of the leader node with guaranteed uniform ultimately bounded transformed error and bounded adaptive estimate of unknown parameters and dynamics. In addition, the proposed controllers with PPF are distributed such that each follower agent requires information between its own state relative to connected neighbors. Proposed controller is validated for robustness and smoothness using highly nonlinear heterogeneous networked system with uncertain time-varying parameters and external disturbances. Index Terms: Prescribed performance, neuro-adaptive, high order, Transformed error, Multi-agents, Distributed control, Consensus, Synchronization, Transient, Steady-state error, MIMO, SISO

Systematic Convergence of Nonlinear Stochastic Estimators on the Special Orthogonal Group SO(3)

This paper introduces two novel nonlinear stochastic attitude estimators developed on the Special Orthogonal Group \mathbb{SO}\left(3\right) with the tracking error of the normalized Euclidean distance meeting predefined transient and steady-state characteristics. The tracking error is confined to initially start within a predetermined large set such that the transient performance is guaranteed to obey dynamically reducing boundaries and decrease smoothly and asymptotically to the origin in probability from almost any initial condition. The proposed estimators produce accurate attitude estimates with remarkable convergence properties using measurements obtained from low-cost inertial measurement units. Unit-quaternion representation of the proposed filters are presented. The estimators proposed in continuous form are complemented by their discrete versions for the implementation purposes. The simulation results illustrate the effectiveness and robustness of the proposed estimators against uncertain measurements and large initialization error, whether in continuous or discrete form. Keywods: Attitude estimates, transient, steady-state error, nonlinear filter, special orthogonal group, SO(3), stochastic system, stochastic differential equations, Ito, Stratonovich, asymptotic stability, Wong-Zakai, inertial measurment unit, IMU, prescribed performance function, Euler Angles, roll, bitch, yaw, color noise, white noise, Nonlinear attitude filter, Nonlinear attitude observer, Orientation, nonlinear stochastic attitude filter on SO(3), unit-quaternion based nonlinear stochastic attitude filter, discrete stochastic attitude filter.Comment: International Journal of Robust and Nonlinear Contro

Location Management in LTE Networks using Multi-Objective Particle Swarm Optimization

Long-term evolution (LTE) and LTE-advance (LTE-A) are widely used efficient network technologies serving billions of users, since they are featured with high spectrum efficiency, less latency, and higher bandwidth. Despite remarkable advantages offered by these technologies, signaling overhead remains a major issue in accessing the network. In particular, the load of signaling is mainly attributed to location management. This paper proposes an efficient approach for minimizing the total signaling overhead of location management in LTE networks using multi-objective particle swarm optimization (MOPSO). Tracking area update (TAU) and paging are considered to be the main elements of the signaling overhead of optimal location management in LTE. In addition, the total inter-list handover contributes significantly to the total signaling overhead. However, the total signaling cost of TAU and paging is adversely related to the total inter-list handover. Two cost functions should be minimized, the first is the total signaling cost of TAU and paging and the second is the total signaling overhead. The trade-off between these two objectives can be circumvented by MOPSO, which alleviates the total signaling overhead. A set of non-dominated solutions on the Pareto-optimal front is defined and the best compromise solution. The proposed algorithm results feasible compromise solution, minimizing the signaling overhead and the consumption of the power battery of a user. The efficacy and the robustness of the proposed algorithm have been proven using large scale environment problem illustrative example. The location management in LTE networks using MOPSO best compromise solution has been compared to a mixed integer non-linear programming (MINLP) algorithm. Location management mobility management entity MME pooling clustering SON Distributed Centralized pooling scheme fuzzy implementation setup LP-CPLEXComment: Computer Network

Energy-efficient Deployment of Relay Nodes in Wireless Sensor Networks using Evolutionary Techniques

Random deployment of sensor nodes is susceptible to initial communication hole, even when the network is densely populated. However, eliminating holes using structural deployment poses its difficulties. In either case, the resulting coverage holes can degrade overall network performance and lifetime. Many solutions utilizing Relay Nodes (RNs) have been proposed to alleviate this problem. In this regard, one of the recent solutions proposed using Artificial Bee Colony (ABC) to deploy RNs. This paper proposes RN deployment using two other evolutionary techniques - Gravitational Search Algorithm (GSA) and Differential Evolution (DE) and compares them with existing solution that uses ABC. These popular optimization tools are deployed to optimize the positions of relay nodes for lifetime maximization. Proposed algorithms guarantee satisfactory RNs utilization while maintaining desired connectivity level. It is shown that DE-based deployment improves the network lifetime better than other optimization heuristics considered.Comment: Int J Wireless Inf Networks (2018

Attitude Determination and Estimation using Vector Observations: Review, Challenges and Comparative Results

This paper concerns the problem of attitude determination and estimation. The early applications considered algebraic methods of attitude determination. Attitude determination algorithms were supplanted by the Gaussian attitude estimation filters (which continue to be widely used in commercial applications). However, the sensitivity of the Gaussian attitude filter to the measurement noise prompted the introduction of the nonlinear attitude filters which account for the nonlinear nature of the attitude dynamics problem and allow for a simpler filter derivation. This paper presents a survey of several types of attitude determination and estimation algorithms. Each category is detailed and illustrated with literature examples in both continuous and discrete form. A comparison between these algorithms is demonstrated in terms of transient and steady-state error through simulation results. The comparison is supplemented by statistical analysis of the error-related mean, infinity norm, and standard deviation of each algorithm in the steady-state. Keywords: Comparative Study, Attitude, Determination, Estimation, Filter, Adaptive Filter, Gaussian Filter, Nonlinear Filter, Overview, Review, Rodrigues Vector, Special Orthogonal Group, Unit-quaternion, Angle-axis, Determinstic, Stochastic, Continuous, Discrete, Multiplicative extended kalman filter, KF, EKF, MEKF, white noise, colored noise

Special Orthogonal Group SO(3), Euler Angles, Angle-axis, Rodriguez Vector and Unit-Quaternion: Overview, Mapping and Challenges

The attitude of a rigid-body in the three dimensional space has a unique and global definition on the Special Orthogonal Group SO (3). This paper gives an overview of the rotation matrix, attitude kinematics and parameterization. The four most frequently used methods of attitude representations are discussed with detailed derivations, namely Euler angles, angle-axis parameterization, Rodriguez vector, and unit-quaternion. The mapping from one representation to others including SO (3) is given. Also, important results which could be useful for the process of filter and/or control design are given. The main weaknesses of attitude parameterization using Euler angles, angle-axis parameterization, Rodriguez vector, and unit-quaternion are illustrated. Keywords: Special Orthogonal Group 3, Euler angles, Angle-axis, Rodriguez Vector, Unit-quaternion, SO(3), Mapping, Parameterization, Attitude, Control, Filter, Observer, Estimator, Rotation, Rotational matrix, Transformation matrix, Orientation, Transformation, Roll, Pitch, Yaw, Quad-rotor, Unmanned aerial vehicle, Robot, spacecraft, satellite, UAV, Underwater vehicle, autonomous, system, Pose, literature review, survey, overview, comparison, comparative study, body frame, identity, origin, dynamics, kinematics, Lie group, inertial frame, zero, filter, control, estimate, observation, measurement, 3D, three dimensional space, advantage, disadvantage

Optimal Placement of Relay Nodes in Wireless Sensor Network Using Artificial Bee Colony Algorithm

Deploying sensor nodes randomly most of the time generates initial communication hole even in highly dense networks. These communication holes cannot be totally eliminated even when the deployment is done in a structured manner. In either case, the resulting inter-node distances may degrade the performance of the network. This paper proposes an enhanced deployment algorithm based on Artificial Bees Colony (ABC). The ABC-based deployment is guaranteed to extend the lifetime by optimizing the network parameters and constraining the total number of deployed relays. Simulations validate the effectiveness of the proposed strategy under different cases of problem complexity. Results show that the proposed approach improves the network lifetime considerably when compared to solutions reported in the literature such as Shortest Path 3-D grid Deployment (SP3D) algorithm. Keywords: Artificial Bee Colony, Wiener index, optimization, relay nodes, Laplacian matrix, connected graph, vertex, edge, average distance, Laplacian matrix, Shortest Path 3-D grid Deployment, ABC, SP3D, RNs/CHs, ILDCC, SPRN, O3DwLC, algorithm, approach, single objective, multi objective, eigenvalue, First Phase Relay Nodes, NP-Hard, Deployment, proposed, second phase relay nodes, Ideal Media Access Control, cluster head, Minimum Spanning Tree, non-deterministic polynomial-time hard, two-layer hierarchical structure, Optimized 3-D deployment with Lifetime Constraints, flux, Wireless Sensor Network, size, Lifetime, Network load, number of nodes using packets, Connectivity, two layered, protocols, collision and interference

Guaranteed Performance of Nonlinear Attitude Filters on the Special Orthogonal Group SO(3)

This paper proposes two novel nonlinear attitude filters evolved directly on the Special Orthogonal Group SO(3) able to ensure prescribed measures of transient and steady-state performance. The tracking performance of the normalized Euclidean distance of attitude error is trapped to initially start within a large set and converge systematically and asymptotically to the origin from almost any initial condition. The convergence rate is guaranteed to be less than the prescribed value and the steady-state error does not exceed a predefined small value. The first filter uses a set of vectorial measurements with the need for attitude reconstruction. The second filter instead uses only a rate gyroscope measurement and two or more vectorial measurements. These filters provide good attitude estimates with superior convergence properties and can be applied to measurements obtained from low cost inertial measurement units (IMUs). Simulation results illustrate the robustness and effectiveness of the proposed attitude filters with guaranteed performance considering high level of uncertainty in angular velocity along with body-frame vector measurements. Keywords: Attitude, estimate, estimator, observer, filter, nonlinear deterministic attitude filter, special orthogonal group, Euler angles, angle-axis, Rodrigues vector, mapping, parameterization, prescribed performance, representation, robust, Multiplicative Extended Kalman Filter, KF, EKF, MEKF, asymptotic stability, almost global asymptotic, noise, rotational matrix, identity, origin, orientation, body frame, inertial frame, rigid body, three dimensional, 3D, space, micro electromechanical systems, sensor, MEMS, roll, pitch, yaw, UAVs, QUAV, SVD, fixed, moving, vehicles, robot, robotic system, spacecraft, submarine, underwater vehicle, passive complementary filter, explicit complementary filter, autonomous, comparative study, SO(3).Comment: 2018, IEEE Acces

Guaranteed Performance Nonlinear Observer for Simultaneous Localization and Mapping

A geometric nonlinear observer algorithm for Simultaneous Localization and Mapping (SLAM) developed on the Lie group of \mathbb{SLAM}_{n}\left(3\right) is proposed. The presented novel solution estimates the vehicle's pose (i.e. attitude and position) with respect to landmarks simultaneously positioning the reference features in the global frame. The proposed estimator on manifold is characterized by predefined measures of transient and steady-state performance. Dynamically reducing boundaries guide the error function of the system to reduce asymptotically to the origin from its starting position within a large given set. The proposed observer has the ability to use the available velocity and feature measurements directly. Also, it compensates for unknown constant bias attached to velocity measurements. Unit-qauternion of the proposed observer is presented. Numerical results reveal effectiveness of the proposed observer. Keywords: Nonlinear filter algorithm, Nonlinear observer for Simultaneous Localization and Mapping, Nonlinear estimator, nonlinear SLAM observer on manifold, nonlinear SLAM filter on matrix Lie Group, observer design, asymptotic stability, systematic convergence, Prescribed performance function, pose estimation, attitude filter, position filter, feature filter, landmark filter, gradient based SLAM observer, gradient based observer for SLAM, adaptive estimate, SLAM observer, observer SLAM framework, equivariant observer, inertial vision unit, visual, SLAM filter, SE(3), SO(3)

Neuro-adaptive distributed control with prescribed performance for the synchronization of unknown nonlinear networked systems

This paper proposes a neuro-adaptive distributive cooperative tracking control with prescribed performance function (PPF) for highly nonlinear multi-agent systems. PPF allows error tracking from a predefined large set to be trapped into a predefined small set. The key idea is to transform the constrained system into unconstrained one through transformation of the output error. Agents' dynamics are assumed to be completely unknown, and the controller is developed for strongly connected structured network. The proposed controller allows all agents to follow the trajectory of the leader node, while satisfying necessary dynamic requirements. The proposed approach guarantees uniform ultimate boundedness of the transformed error and the adaptive neural network weights. Simulations include two examples to validate the robustness and smoothness of the proposed controller against highly nonlinear heterogeneous networked system with time varying uncertain parameters and external disturbances