Design of an Internal Model Controller for Binary Distillation Column

In this report Internal Model Control, Lead- Lag based Internal Model Control and modified Internal Model Control for distillation column has been proposed. The prime objective of any industrial process is to perform efficiently with optimum cost reduction. Internal Model Control (IMC) is a commonly used technique that provides a transparent mode for the designing and easy tuning of control structure . I have designed the internal model control for binary distillation column .The transfer function has been taken from Wood and Berry model. The internal model control has been designed considering three strategies namely, process perfect, process mismatch with disturbances and process model with considering only disturbance. It has also been tried to reduce the disturbance created in the system by varying tuning parameter (ë). In the second proposal, Lead-Lag based Internal Model Control method is proposed based on Internal Model Control (IMC) strategy. We have also designed the Lead-Lag based Internal Model Control for binary distillation column. We have found the composition control and disturbance rejection using Lead-Lag based IMC and comparing with the response of generalize Internal Model Controller. Finally we have design the Modified Internal Model Structure, and find the response for binary distillation column and compare with generalize Internal Model Controller response. This thesis presents an Internal Model Control, lead- lag based internal model control and modified internal model control strategy for binary distillation column and comparing the response with each other. The aim is to provide a best strategy to control the distillation column that is favourable in terms of industrial implementation. I have used matlab software to simulate the all process

A Theoretical Exploration of the Adoption and Design of Flexible Benefit Plans: A Case of Human Resource Innovation

This article explores theoretical explanations of managers\u27 decisions about flexible benefit plans. We (1) examine the adoption and design of flexible benefit plans through four theoretic lenses: institutional, resource dependence, agency, and transaction costs; (2) integrate the relevant insights gained from these theories into a more complete model and derive propositions for future research; and (3) generalize the insights gained from exploring a specific innovation to broader questions surrounding decisions about other human resource innovations

A Process Algebra Software Engineering Environment

In previous work we described how the process algebra based language PSF can be used in software engineering, using the ToolBus, a coordination architecture also based on process algebra, as implementation model. In this article we summarize that work and describe the software development process more formally by presenting the tools we use in this process in a CASE setting, leading to the PSF-ToolBus software engineering environment. We generalize the refine step in this environment towards a process algebra based software engineering workbench of which several instances can be combined to form an environment

Sim-to-Real Transfer of Robotic Control with Dynamics Randomization

Simulations are attractive environments for training agents as they provide an abundant source of data and alleviate certain safety concerns during the training process. But the behaviours developed by agents in simulation are often specific to the characteristics of the simulator. Due to modeling error, strategies that are successful in simulation may not transfer to their real world counterparts. In this paper, we demonstrate a simple method to bridge this "reality gap". By randomizing the dynamics of the simulator during training, we are able to develop policies that are capable of adapting to very different dynamics, including ones that differ significantly from the dynamics on which the policies were trained. This adaptivity enables the policies to generalize to the dynamics of the real world without any training on the physical system. Our approach is demonstrated on an object pushing task using a robotic arm. Despite being trained exclusively in simulation, our policies are able to maintain a similar level of performance when deployed on a real robot, reliably moving an object to a desired location from random initial configurations. We explore the impact of various design decisions and show that the resulting policies are robust to significant calibration error

Memory Augmented Control Networks

Planning problems in partially observable environments cannot be solved directly with convolutional networks and require some form of memory. But, even memory networks with sophisticated addressing schemes are unable to learn intelligent reasoning satisfactorily due to the complexity of simultaneously learning to access memory and plan. To mitigate these challenges we introduce the Memory Augmented Control Network (MACN). The proposed network architecture consists of three main parts. The first part uses convolutions to extract features and the second part uses a neural network-based planning module to pre-plan in the environment. The third part uses a network controller that learns to store those specific instances of past information that are necessary for planning. The performance of the network is evaluated in discrete grid world environments for path planning in the presence of simple and complex obstacles. We show that our network learns to plan and can generalize to new environments

Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots

One of the open challenges in designing robots that operate successfully in the unpredictable human environment is how to make them able to predict what actions they can perform on objects, and what their effects will be, i.e., the ability to perceive object affordances. Since modeling all the possible world interactions is unfeasible, learning from experience is required, posing the challenge of collecting a large amount of experiences (i.e., training data). Typically, a manipulative robot operates on external objects by using its own hands (or similar end-effectors), but in some cases the use of tools may be desirable, nevertheless, it is reasonable to assume that while a robot can collect many sensorimotor experiences using its own hands, this cannot happen for all possible human-made tools. Therefore, in this paper we investigate the developmental transition from hand to tool affordances: what sensorimotor skills that a robot has acquired with its bare hands can be employed for tool use? By employing a visual and motor imagination mechanism to represent different hand postures compactly, we propose a probabilistic model to learn hand affordances, and we show how this model can generalize to estimate the affordances of previously unseen tools, ultimately supporting planning, decision-making and tool selection tasks in humanoid robots. We present experimental results with the iCub humanoid robot, and we publicly release the collected sensorimotor data in the form of a hand posture affordances dataset.Comment: dataset available at htts://vislab.isr.tecnico.ulisboa.pt/, IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob 2017