Thermal Management in Laminated Die Systems Using Neural Networks

The thermal control of a die is crucial for the development of high efficiency injection moulds. For successful thermal management, this research provides an effective control strategy to find sensor locations, identify thermal dynamic models, and design controllers. By applying a clustering method and sensitivity analysis, sensor locations are identified. The neural network and finite element analysis techniques enable the modeling to deal with various cycle-times for the moulding process and uncertain dynamics of a die. A combination of off-line training through finite element analysis and training using on-line learning algorithms and experimental data is used for the system identification. Based on the system identification which is experimentally validated using a real system, controllers are designed using fuzzy-logic and self-adaptive PID methods with backpropagation (BP) and radial basis function (RBF) neural networks to tune control parameters. Direct adaptive inverse control and additive feedforward control by adding direct adaptive inverse control to self-adaptive PID controllers are also provided. Through a comparative study, each controller’s performance is verified in terms of response time and tracking accuracy under different moulding processes with multiple cycle-times. Additionally, the improved cooling effectiveness of the conformal cooling channel designed in this study is presented by comparing with a conventional straight channel

Online Tuning Rule Based Adaptive Speed Control Algorithm For Dc Motors Using Recursive Least Squares With Forgetting

This paper presents an online tuning rule-based adaptive control algorithm to control DC motor speed. For this, a method of recursive least squares with forgetting was proposed to approximate the relation between single input (voltage) and single output (angular speed) of the DC motor system as a first order differential equation. Using this approximated first order system and Lyapunov method-based disturbance observer with the online turning rule, a voltage input of the DC motor was generated to track a desired value of rotational speed of the motor. A performance evaluation of the proposed algorithm was conducted in the MATLAB/Simulink environment. The results show that the designed algorithm enables to track the reference speed successfully using only a single input and a single output of DC motor system

Thermal management in laminated die system

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Control, Automation and Systems on August 2014, available online: http://dx.doi.org/10.1007/s12555-013-0348-6The thermal control of a die is crucial for the development of high efficiency injection moulds. For an effective thermal management, this research provides a strategy to identify a thermal dynamic model and to design a controller. The neural network techniques and finite element analysis enable modeling to deal with various cycle-times for moulding process and uncertain dynamics of a die. Based on the system identification which is experimentally validated using a real system, controllers are designed using fuzzy-logic and self-tuning PID methods with backpropagation and radial basis function neural networks to tune control parameters. Through a comparative study, each controller’s performance is verified in terms of response time and tracking accuracy under different moulding processes with multiple cycle-times

Development of a Sliding-Mode-Control-Based Path-Tracking Algorithm with Model-Free Adaptive Feedback Action for Autonomous Vehicles

This paper presents a sliding mode control (SMC)-based path-tracking algorithm for autonomous vehicles by considering model-free adaptive feedback actions. In autonomous vehicles, safe path tracking requires adaptive and robust control algorithms because driving environment and vehicle conditions vary in real time. In this study, the SMC was adopted as a robust control method to adjust the switching gain, taking into account the sliding surface and unknown uncertainty to make the control error zero. The sliding surface can be designed mathematically, but it is difficult to express the unknown uncertainty mathematically. Information of priori bounded uncertainties is needed to obtain closed-loop stability of the control system, and the unknown uncertainty can vary with changes in internal and external factors. In the literature, ongoing efforts have been made to overcome the limitation of losing control stability due to unknown uncertainty. This study proposes an integrated method of adaptive feedback control (AFC) and SMC that can adjust a bounded uncertainty. Some illustrative and representative examples, such as autonomous driving scenarios, are also provided to show the main properties of the designed integrated controller. The examples show superior control performance, and it is expected that the integrated controller could be widely used for the path-tracking algorithms of autonomous vehicles

Energy Saving in an Autonomous Excavator via Parallel Actuators Design and PSO-Based Excavation Path Generation

An autonomous excavator can be a good solution in the construction industry to deal with existing issues such as high labor costs and harsh and hazardous environmental conditions. To increase energy efficiency for autonomous excavators, this study proposes two approaches. First, a new and unique design with parallel arm and bucket actuators is proposed for an electric excavator manipulator. Since the three actuators of the boom, arm, and bucket are in series for the conventional design of excavators, it is difficult to share external loads between them. However, a parallel configuration of the arm and bucket actuators in the proposed new design can facilitate load sharing and overcome higher external loads. By replacing hydraulic actuators with electric linear actuators, this design also reduces energy consumption during idling. Moreover, with low back drivability, the electric linear actuators can handle relatively high external forces without spending energy while not in motion. Secondly, a PSO-based path-generation algorithm was developed for autonomous excavation to minimize energy consumption while avoiding collisions with unwanted obstacles. In the PSO algorithm, it is possible to change the priorities of the elements to the minimum by adjusting the gains in the cost function. Two scenarios—scenarios with and without considering energy saving—were considered to test the performance of the developed algorithm, with the results between the scenarios compared. Simulation results show that the proposed algorithm reduces energy consumption in each digging cycle by 18.51%

Development of Integrative Methodologies for Effective Excavation Progress Monitoring

Excavation is one of the primary projects in the construction industry. Introducing various technologies for full automation of the excavation can be a solution to improve sensing and productivity that are the ongoing issues in this area. This paper covers three aspects of effective excavation progress monitoring that include excavation volume estimation, occlusion area detection, and 5D mapping. The excavation volume estimation component enables estimating the bucket volume and ground excavation volume. To achieve mapping of the hidden or occluded ground areas, integration of proprioceptive and exteroceptive sensing data was adopted. Finally, we proposed the idea of 5D mapping that provides the info of the excavated ground in terms of geometric space and material type/properties using a 3D ground map with LiDAR intensity and a ground resistive index. Through experimental validations with a mini excavator, the accuracy of the two different volume estimation methods was compared. Finally, a reconstructed map for occlusion areas and a 5D map were created using the bucket tip’s trajectory and multiple sensory data with convolutional neural network techniques, respectively. The created 5D map would allow for the provision of extended ground information beyond a normal 3D ground map, which is indispensable to progress monitoring and control of autonomous excavation

Development of Sensing Algorithms for Object Tracking and Predictive Safety Evaluation of Autonomous Excavators

This article presents the sensing and safety algorithms for autonomous excavators operating on construction sites. Safety is a key concern for autonomous construction to reduce collisions and machinery damage. Taking this point into consideration, our study deals with LiDAR data processing that allows for object detection, motion tracking/prediction, and track management, as well as safety evaluation in terms of potential collision risk. In the safety algorithm developed in this study, potential collision risks can be evaluated based on information from excavator working areas, predicted states of detected objects, and calculated safety indices. Experiments were performed using a modified mini hydraulic excavator with Velodyne VLP-16 LiDAR. Experimental validations prove that the developed algorithms are capable of tracking objects, predicting their future states, and assessing the degree of collision risks with respect to distance and time. Hence, the proposed algorithms can be applied to diverse autonomous machines for safety enhancement

Design of a LIOR-Based De-Dust Filter for LiDAR Sensors in Off-Road Vehicles

LiDAR sensors have played an important role in a variety of related applications due to their merits of providing high-resolution and accurate information about the environment. However, their detection performance significantly degrades under dusty conditions, thereby making the whole perception of the vehicles prone to failure. To deal with this problem, we designed a de-dust filter using a LIOR filtering technique that offers a viable method of eliminating dust particles from the measurement data. Experimental results confirm that the proposed method is robust in the face of dust particles by successfully removing them from the measured point cloud with good filtering accuracy while maintaining rich information about the environment

Design of Dust-Filtering Algorithms for LiDAR Sensors Using Intensity and Range Information in Off-Road Vehicles

Although the LiDAR sensor provides high-resolution point cloud data, its performance degrades when exposed to dust environments, which may cause a failure in perception for robotics applications. To address this issue, our study designed an intensity-based filter that can remove dust particles from LiDAR data in two steps. In the first step, it identifies potential points that are likely to be dust by using intensity information. The second step involves analyzing the point density around selected points and removing them if they do not meet the threshold criterion. To test the proposed filter, we collected experimental data sets under the existence of dust and manually labeled them. Using these data, the de-dusting performance of the designed filter was evaluated and compared to several types of conventional filters. The proposed filter outperforms the conventional ones in achieving the best performance with the highest F1 score and removing dust without sacrificing the original surrounding data