Near infrared spectroscopy: A mature analytical technique with new perspectives - A review.

The academic literature on and industrial practice of control of solution crystallization processes have seen major advances in the past 15 years that have been enabled by progress in in-situ real-time sensor technologies and driven primarily by needs in the pharmaceutical industry for improved and more consistent quality of drug crystals. These advances include the accurate measurement of solution concentrations and crystal characteristics as well as the first-principles modeling and robust model-based and model-free feedback control of crystal size and polymorphic identity. Research opportunities are described in model-free controller design, new crystallizer designs with enhanced control of crystal size distribution, strategies for the robust control of crystal shape, and interconnected crystallization systems for multicomponent crystallization.

/pdf/advances-and-new-directions-in-crystallization-control-53sli0mxh7.pdf

Advances and new directions in crystallization control

Ship collision avoidance methods : State-of-the-art

Population balance modeling is undergoing phenomenal growth in its applications, and this growth is accompanied by multifarious reviews. This review aims to fortify the model's fundamental base, as well as point to a variety of new applications, including modeling of crystal morphology, cell growth and differentiation, gene regulatory processes, and transfer of drug resistance. This is accomplished by presenting the many faces of population balance equations that arise in the foregoing applications.

Population balance modeling: current status and future prospects

1.Introduction 2.Fundamental Modeling and Control of Unmanned Small-Scale and Mi-niature Helicopters 3.Autonomous Control of a Mini Quadrotor Vehicle Using LQG Controllers 4.Modeling and Control of an Autonomous Quad-Tilt-Wing (QTW) UAV 5.Linearlization and Identification of Helicopter Model for Hierarchical Control Design 6.Analysis of the Autorotation Maneuver in Small-Scale Helicopters and Application for Emergency Landing 7.Autonomous Acrobatic Flight based on Feedforward Sequence Control for Small Unmanned Helicopter 8.Mathematical Modeling and Nonlinear Control of VTOL Aerial Vehicles 9.Formation Flight Control of Multiple Autonomous Helicopters Using Predictive Control 10.Guidance and Navigation Systems for Small Aerial Robots 11.Design and Implementation of a Low-Cost Attitude Quaternion Sensor 12.Vision-Based Navigation and Visual Servoing of Mini Flying Machines 13.Autonomous Indoor Flight and Precise Auto-Landing Using Infrared and Ultrasonic Sensors

Autonomous flying robots : unmanned aerial vehicles and micro aerial vehicles

The adoption of a robust collision avoidance module is required to realise fully autonomous Unmanned Surface Vehicles (USVs). In this work, collision detection and path planning methods for USVs are presented. Attention is focused on the difference between local and global path planners, describing the most common techniques derived from classical graph search theory. In addition, a dedicated section is reserved for intelligent methods, such as artificial neural networks and evolutionary algorithms. Born as optimisation methods, they can learn a close-to-optimal solution without requiring large computation effort under certain constraints. Finally, the deficiencies of the existing methods are highlighted and discussed. It has been concluded that almost all the existing method do not address sea or weather conditions, or do not involve the dynamics of the vessel while defining the path. Therefore, this research area is still far from being considered fully explored.

/pdf/obstacle-avoidance-approaches-for-autonomous-navigation-of-3kik3rlcaw.pdf

Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles

The autonomous landing of an unmanned aerial vehicle (UAV) is still an open problem. Previous work focused on the use of hand-crafted geometric features and sensor-data fusion for identifying a fiducial marker and guide the UAV toward it. In this article we propose a method based on deep reinforcement learning that only requires low-resolution images coming from a down looking camera in order to drive the vehicle. The proposed approach is based on a hierarchy of Deep Q-Networks (DQNs) that are used as high-end control policy for the navigation in different phases. We implemented various technical solutions, such as the combination of vanilla and double DQNs trained using a form of prioritized buffer replay that separates experiences in multiple containers. The optimal control policy is learned without any human supervision, providing the agent with a sparse reward feedback indicating the success or failure of the landing. The results show that the quadrotor can autonomously land on a large variety of simulated environments and with relevant noise, proving that the underline DQNs are able to generalise effectively on unseen scenarios. Furthermore, it was proved that in some conditions the network outperformed human pilots.

Toward End-to-End Control for UAV Autonomous Landing via Deep Reinforcement Learning

Efficient Collision-Free Path Planning for Autonomous Underwater Vehicles in Dynamic Environments with a Hybrid Optimization Algorithm

Landing an unmanned aerial vehicle (UAV) on a ground marker is an open problem despite the effort of the research community. Previous attempts mostly focused on the analysis of hand-crafted geometric features and the use of external sensors in order to allow the vehicle to approach the land-pad. In this article, we propose a method based on deep reinforcement learning that only requires low-resolution images taken from a down-looking camera in order to identify the position of the marker and land the UAV on it. The proposed approach is based on a hierarchy of Deep Q-Networks (DQNs) used as high-level control policy for the navigation toward the marker. We implemented different technical solutions, such as the combination of vanilla and double DQNs, and a partitioned buffer replay. Using domain randomization we trained the vehicle on uniform textures and we tested it on a large variety of simulated and real-world environments. The overall performance is comparable with a state-of-the-art algorithm and human pilots.

Autonomous Quadrotor Landing using Deep Reinforcement Learning.

A population balance model for predicting the dynamic evolution of crystal shape distribution is further developed to simulate crystallization processes in which multiple crystal morphological forms co-exist and transitions between them can take place The new model is applied to derive the optimal temperature and supersaturation profiles leading to the desired crystal shape distribution in cooling crystallization Since tracking an optimum temperature or supersaturation trajectory can be easily implemented by manipulating the coolant flowrate in the reactor jacket, the proposed methodology provides a feasible closed-loop mechanism for crystal shape tailoring and control The methodology is demonstrated by applying it to a case study of seeded cooling crystallization of potash alum © 2009 American Institute of Chemical Engineers AIChE J, 2009

Jian Wan

Papers

Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles

Toward End-to-End Control for UAV Autonomous Landing via Deep Reinforcement Learning

Efficient Collision-Free Path Planning for Autonomous Underwater Vehicles in Dynamic Environments with a Hybrid Optimization Algorithm

Autonomous Quadrotor Landing using Deep Reinforcement Learning.

Particle shape manipulation and optimization in cooling crystallization involving multiple crystal morphological forms