In this article, we propose a new 3-D maneuver controller for a class of nonlinear multiagent systems (MASs) with nonholonomic constraint and saturated control. The system is designed under a distributed communication topology and the controller is more flexible and efficient for general formation maneuver tasks. The saturation design generates control inputs within pregiven bounds, which makes the system more applicable in practice. Moreover, based on the nonholonomic model, the proposed control also considers the heading angles of the agents. Thus, the maneuver controller can achieve a more natural tracking movement where the heading of the formation will align to the direction of the reference trajectory during the tracking motion. Several simulation examples are given to validate our results and demonstrate the competence for various maneuver tasks of MASs.

In this article, we design the controller for multiagent systems such that the agents with nonlinear dynamics can form into arbitrarily given shapes, rotate around the formation centroid at the given angular velocity, track the reference trajectory, and dynamically adjust the formation for obstacle avoidance. In addition, the proposed design ensures that the agents can be positioned with a specified order in the formation. We propose an extended model for the system to facilitate the design. To deal with the order issue, we introduce thephase weighting parameterand thephase penalty flow exchange mechanismsuch that the agent’s position can be adjusted to the desired order. With the aid of the proposed tools, we first design the formation controller for MAS with a planar unicycle model and then extend it to the three-dimensional space. The controller is designed based on information from the neighbors and is measured in the agents’ local reference frame. Moreover, unlike most of the existing works, the initial conditions are not restricted in our approach. Finally, system stability is proved via the Lyapunov theorem and several simulation examples are provided to validate our results.

There are limitations of conventional active-assistive control for upper limb rehabilitation exoskeleton robot, such as 1). prior time-dependent trajectories are generally required, 2). task-based rehabilitation exercise involving multi-joint motion is hard to implement, and 3). assistive mechanism normally is so inflexible that the resulting exercise performed by the subjects becomes inefficient. In this paper, we propose a novel velocity field based active-assistive control system to address these issues. First, we design a Kalman filter based interactive torque observer to obtain subjects' active intention of motion. Next, a joint-position-dependent velocity field which can be automatically generated via the task motion pattern is proposed to provide the time-independent assistance to the subjects. We further propose a novel integration method that combines the active and assistive motions based on the performance and the involvement of subjects to guide them to perform the task more voluntarily and precisely. The experiment results show that both the execution time and the subjects' torque exertion are reduced while performing both given single joint tasks and task-oriented multi-joint tasks as compared with the related work in the literature. To sum up, the proposed system not only can efficiently retain subjects' active intention but also can assist them to accomplish the rehabilitation task more precisely.

In this paper, we propose a novel system architecture called multi-layer environmental affordance map for social and service companion robots. Based on this architecture, robots can organize the perception and inference information efficiently and generate social friendly navigation strategies. In other words, robots are able to strengthen their perception and inference abilities to interact with domestic environment and users under our efficient framework. The main feature of this architecture is that the relations between layers can be viewed as affordances to improve the accuracy and the robustness of the detection and inference. The results show that our architecture achieves robust indoor localization, scene localization, human event detection and socially friendly navigation in real time under limited computational resource.

The following topics are dealt with: learning (artificial intelligence); medical signal processing; neurophysiology; electroencephalography; mobile robots; feature extraction; brain-computer interfaces; neural nets; convolutional neural nets; production engineering computing.

In this paper, a vision based adaptive controller is proposed for the obstacle avoidance of a micro aerial vehicle (MAV) by using the optical flow information. In order to employ the optical flow for indicating the distance between the MAV and surrounding, the multi-thread processing algorithm is proposed to reliably obtain the optical flow information uniformly diffused in the whole image frame. The MAV system is treated as a switched system while manipulating the different modes during the obstacle avoidance task. Based on the desired flying direction of obstacle avoidance obtained by the optical flow estimation, we design an adaptive controller such that the desired trajectory can be tracked under different switching modes. The simulations present the tracking response of the adaptive controller of a switched system, and the experiments of the overall system validate the collision-avoidance performance of the MAV.

In this paper, we design adaptive and robust controller for the nonlinear MIMO heating, ventilating, and air conditioning (HVAC) system, respectively. The designed adaptive controller maintains good tracking performance for temperature and humidity ratio regardless of the slowly time-varying load changes, which are regarded as parametric uncertainties in thermal space. The robust controller considering non-parametric uncertainties is designed so as to remedy the drawback of feedback linearization technique. The obtained control system shows robustness to the non-constant thermal loads and mismatched uncertainties, respectively. Some simulation results are provided to illustrate the satisfactory control performance of the adaptive controller.

In this paper, we propose a supervisory controller for the discrete model of a heating, ventilating and air conditioning (HVAC) system and then design the nonlinear controller for the heating and cooling subsystems within. The HVAC system includes the discrete event dynamics and nonlinear continuous dynamics, both are very complex and challenging. Here we discuss the whole system and systematically design the controller and the interface between continuous and discrete controller.