Model-Assisted Online Optimization of Gain-Scheduled PID Control Using NSGA-II Iterative Genetic Algorithm

In the practical control of nonlinear valve systems, PID control, as a model-free method, continues to play a crucial role thanks to its simple structure and performance-oriented tuning process. To improve the control performance, advanced gain-scheduling methods are used to schedule the PID control gains based on the operating conditions and/or tracking error. However, determining the scheduled gain is a major challenge, as PID control gains need to be determined at each operating condition. In this paper, a model-assisted online optimization method is proposed based on the modified Non-Dominated Sorting Genetic Algorithms-II (NSGA-II) to obtain the optimal gain-scheduled PID controller. Model-assisted offline optimization through computer-in-the-loop simulation provides the initial scheduled gains for an online algorithm, which then uses the iterative NSGA-II algorithm to automatically schedule and tune PID gains by online searching of the parameter space. As a summary, the proposed approach presents a PID controller optimized through both model-assisted learning based on prior model knowledge and model-free online learning. The proposed approach is demonstrated in the case of a nonlinear valve system able to obtain optimal PID control gains with a given scheduled gain structure. The performance improvement of the optimized gain-scheduled PID control is demonstrated by comparing it with fixed-gain controllers under multiple operating conditions

Flow Allocation for Maximum Throughput and Bounded Delay on Multiple Disjoint Paths for Random Access Wireless Multihop Networks

In this paper, we consider random access, wireless, multi-hop networks, with multi-packet reception capabilities, where multiple flows are forwarded to the gateways through node disjoint paths. We explore the issue of allocating flow on multiple paths, exhibiting both intra- and inter-path interference, in order to maximize average aggregate flow throughput (AAT) and also provide bounded packet delay. A distributed flow allocation scheme is proposed where allocation of flow on paths is formulated as an optimization problem. Through an illustrative topology it is shown that the corresponding problem is non-convex. Furthermore, a simple, but accurate model is employed for the average aggregate throughput achieved by all flows, that captures both intra- and inter-path interference through the SINR model. The proposed scheme is evaluated through Ns2 simulations of several random wireless scenarios. Simulation results reveal that, the model employed, accurately captures the AAT observed in the simulated scenarios, even when the assumption of saturated queues is removed. Simulation results also show that the proposed scheme achieves significantly higher AAT, for the vast majority of the wireless scenarios explored, than the following flow allocation schemes: one that assigns flows on paths on a round-robin fashion, one that optimally utilizes the best path only, and another one that assigns the maximum possible flow on each path. Finally, a variant of the proposed scheme is explored, where interference for each link is approximated by considering its dominant interfering nodes only.Comment: IEEE Transactions on Vehicular Technolog

The application of multi-objective robust design methods in ship design

When designing large complex vessels, the evaluation of a particular design can be both complicated and time consuming. Designers often resort to the use of concept design models enabling both a reduction in complexity and time for evaluation. Various optimisation methods are then typically used to explore the design space facilitating the selection of optimum or near optimum designs. It is now possible to incorporate considerations of seakeeping, stability and costs at the earliest stage in the ship design process. However, to ensure that reliable results are obtained, the models used are generally complex and computationally expensive. Methods have been developed which avoid the necessity to carry out an exhaustive search of the complete design space. One such method is described which is concerned with the application of the theory of Design Of Experiments (DOE) enabling the design space to be efficiently explored. The objective of the DOE stage is to produce response surfaces which can then be used by an optimisation module to search the design space. It is assumed that the concept exploration tool whilst being a simplification of the design problem, is still sufficiently complicated to enable reliable evaluations of a particular design concept. The response surface is used as a representation of the concept exploration tool, and by it's nature can be used to rapidly evaluate a design concept hence reducing concept exploration time. While the methodology has a wide applicability in ship design and production, it is illustrated by its application to the design of a catamaran with respect to seakeeping. The paper presents results exploring the design space for the catamaran. A concept is selected which is robust with respect to the Relative Bow Motion (RBM), the heave, pitch and roll at any particular waveheading. The design space is defined by six controllable design parameters; hull length, breadth to draught ratio, distance between demihull centres, coefficient of waterplane, longitudinal centre of floatation, longitudinal centre of buoyancy, and by one noise parameter, the waveheading. A Pareto-optimal set of solutions is obtained using RBM, heave, pitch and roll as criteria. The designer can then select from this set the design which most closely satisfies their requirements. Typical solutions are shown to yield average reductions of over 25% in the objective functions when compared to earlier results obtained using conventional optimisation methods

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. Surrogate models are widely used in the field of simulation sciences to efficiently analyze computationally expensive simulation models. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic

Kalman Filter for Noise Reduction in Aerial Vehicles using Echoic Flow

Echolocation is a natural phenomenon observed in bats that allows them to navigate complex, dim environments with enough precision to capture insects in midair. Echolocation is driven by the underlying process of echoic flow, which can be broken down into a ratio of the distance from a target to the velocity towards it. This ratio produces a parameter τ representing the time to collision, and controlling it allows for highly efficient and consistent movement. When a quadcopter uses echoic flow to descend to a target, measurements from the ultrasonic range sensor exhibit noise. Furthermore, the use of first order derivatives to calculate the echoic flow parameters results in an even greater magnitude of noise. The implementation of an optimal Kalman filter to smooth measurements allows for more accurate and precise tracking, ultimately recreating the high efficiency and consistency of echolocation tracking techniques found in nature. Kalman filter parameters were tested in realistic simulations of the quadcopter's descent. These tests determined an optimal Kalman filter for the system. The Kalman filter's effect on an accurate echoic flow descent was then tested against that of other filtering methods. Of the filtering methods tested, Kalman filtering best allowed the quadcopter to control its echoic flow descent in a precise and consistent manner. In this presentation, the test methodology and results of the various tests are presented.No embargoAcademic Major: Electrical and Computer Engineerin