Hierarchical Control

This paper considers the problem of pursuit evasion games (PEGs), where the objective of a group of pursuers is to chase and capture a group of evaders in minimum time with the aid of a sensor network. The main challenge in developing a real-time control system using sensor networks is the inconsistency in sensor measurements due to packet loss, communication delay, and false detections. We address this challenge by developing a real-time hierarchical control system, named LochNess, which decouples the estimation of evader states from the control of pursuers via multiple layers of data fusion. The multiple layers of data fusion convert noisy, inconsistent, and bursty sensor measurements into a consistent set of fused measurements. Three novel algorithms are developed for LochNess: multisensor fusion, hierarchical multitarget tracking, and multiagent coordination algorithms. The multisensor fusion algorithm converts correlated sensor measurements into position estimates, the hierarchical multitarget tracking algorithm based on Markov chain Monte Carlo data association (MCMCDA) tracks an unknown number of targets, and the multiagent coordination algorithm coordinates pursuers to chase and capture evaders using robust minimum-time control. The control system LochNess is evaluated in simulation and successfully demonstrated using a large-scale outdoor sensor network deployment

Examines the contemporary discourse on organizational change and transformational leadership in the larger context of a shift from a dominator to a partnership model of social and ideological organization. Traces the historic tension between these ...

This paper proposes a new approach to robustly control a 2D under-actuated overhead crane system, where a payload is effectively transported to a destination in real time with small sway angles, given its inherent uncertainties such as actuator nonlinearities and external disturbances. The control law is proposed to be developed by the use of the robust hierarchical sliding mode control (HSMC) structure in which a second-level sliding surface is formulated by two first-level sliding surfaces drawn on both actuated and under-actuated outputs of the crane. The unknown and uncertain parameters of the proposed control scheme are then adaptively estimated by the fuzzy observer, where the adaptation mechanism is derived from the Lyapunov theory. More importantly, stability of the proposed strategy is theoretically proved. Effectiveness of the proposed adaptive fuzzy observer based HSMC (AFHSMC) approach was extensively validated by implementing the algorithm in both synthetic simulations and real-life experiments, where the results obtained by our method are highly promising.

In this paper, hierarchical control techniques is used for controlling a robotic manipulator. The proposed method is based on the establishment of a non-linear mapping between Cartesian and joint coordinates using fuzzy logic in order to direct each individual joint. The hierarchical control will be implemented with fuzzy logic to improve the robustness and reduce the run time computational requirements. Hierarchical control consists of solving the inverse kinematic equations using fuzzy logic to direct each individual joint. A commercial Microbot with three degrees of freedom is utilized to evaluate this methodology. A decentralized fuzzy controller is used for each joint, with a Fuzzy Associative Memories (FAM) performing the inverse kinematic mapping in a supervisory mode. The FAM determines the inverse kinematic mapping which maps the desired Cartesian coordinates to the individual joint angles. The individual fuzzy controller for each joint generates the required control signal to a DC motor to move the associated link to the new position. The proposed hierarchical fuzzy controller is compared to a conventional controller. The simulation experiments indeed demonstate the effectiveness of the proposed method.

In reinforcement learning an agent may explore ineffectively when dealing with sparse reward tasks where finding a reward point is difficult. To solve the problem, we propose an algorithm called hierarchical deep reinforcement learning with automatic sub-goal identification via computer vision (HADS) which takes advantage of hierarchical reinforcement learning to alleviate the sparse reward problem and improve efficiency of exploration by utilizing a sub-goal mechanism. HADS uses a computer vision method to identify sub-goals automatically for hierarchical deep reinforcement learning. Due to the fact that not all sub-goal points are reachable, a mechanism is proposed to remove unreachable sub-goal points so as to further improve the performance of the algorithm. HADS involves contour recognition to identify sub-goals from the state image where some salient states in the state image may be recognized as sub-goals, while those that are not will be removed based on prior knowledge. Our experiments verified the effect of the algorithm.

In this study we present a supervisory control system and implement it on a pneumatic system controlled by a PLC. We introduce a step-by-step implementation procedure including a realization methodology of finite state machines by PLCs. A hierarchical control structure is proposed and implemented. The pneumatic system control problem presented in this study is a typical logical Discrete Event System (DES) control problem, therefore it is easy to generalise the methodology for most of the DES control problems.

Features Discusses economic optimization of greenhouse control through mathematical modeling Examines 30 years of scientific research to present a unified framework for efficient decision-making Presents modern methods of control and optimization including classical rule-based and multivariable feedback controllers Utilizes real and experimental examples and novel case discussions such as solar greenhouses Concludes with a discussion of open issues to stimulate

This paper presents a novel application of backstepping controller for autonomous landing of a rotary wing UAV (RUAV). This application, which holds good for the full flight envelope control, is an extension of a backstepping algorithm for general rigid body velocity control. The nonlinear RUAV model used in this paper includes the flapping and servo dynamics. The backstepping-based controller takes advantage of the ‘decoupling’ of the translation and rotation dynamics of the rigid body, resulting in a two-step procedure to obtain the RUAV control inputs. The first step is to compute desired thrusts and flapping angles to achieve the commanded position and the second step is to compute control inputs, which achieve the desired thrusts and flapping angles. This paper presents a detailed analysis of the inclusion of a flapping angle correction term in control. The performance of the proposed algorithm is tested using a high-fidelity RUAV simulation model. The RUAV simulation model is based on miniature rotorcraft parameters. The closed-loop response of the rotorcraft indicates that the desired position is achieved after a short transient. The Eagle RUAV control inputs, obtained using high-fidelity simulation results, clearly demonstrate that this algorithm can be implemented on practical RUAVs. Copyright © 2009 John Wiley & Sons, Ltd.

The development process i.e. assumptions, construction, simulations and analysis of a control strategy for the hybrid drive of the vehicle mounted aerial work platform is presented in the paper. Particular attention is paid to the development of the control system strategy ensuring appropriate energy recuperation by making use of energy stored in the electrochemical form. The control strategy is built up around the concept of bi-level hierarchic control system. The elevation control of the aerial work platform is assumed as the primary goal of the control system. The secondary goal of the control system is formulated in terms of tracking and keeping the charging level of the rechargeable electrochemical accumulator in predefined limits. A control system simulation model is developed in Matlab-Simulink environment. Exemplary results of control system simulations are shown on the example of a hydraulic power unit driving aerial work platform mounted on special vehicle MONTRAKS.

This paper presents a novel application of backstepping controller for autonomous landing of a rotary wing UAV (RUAV). This application, which holds good for the full flight envelope control, is an extension of a backstepping algorithm for general rigid body velocity control. The nonlinear RUAV model used in this paper includes the flapping and servo dynamics. The backstepping-based controller takes advantage of the ‘decoupling’ of the translation and rotation dynamics of the rigid body, resulting in a two-step procedure to obtain the RUAV control inputs. The first step is to compute desired thrusts and flapping angles to achieve the commanded position and the second step is to compute control inputs, which achieve the desired thrusts and flapping angles. This paper presents a detailed analysis of the inclusion of a flapping angle correction term in control. The performance of the proposed algorithm is tested using a high-fidelity RUAV simulation model. The RUAV simulation model is based on miniature rotorcraft parameters. The closed-loop response of the rotorcraft indicates that the desired position is achieved after a short transient. The Eagle RUAV control inputs, obtained using high-fidelity simulation results, clearly demonstrate that this algorithm can be implemented on practical RUAVs. Copyright © 2009 John Wiley & Sons, Ltd.

e human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the eff orts required to the user to carry out the diff erent movements is quite small (albeit after an appropriate and lengthy training). On the contrary, prosthetic