New dynamic modeling and pratical control design for MacPherson suspension system

The ride quality, handling, and stability are three main issues in vehicle suspension design. Different suspension systems have been designed in the past to fulfil these conflicting requirements. One of the popular suspension systems integrated in small and midsize passenger cars is MacPherson suspension system. A suspension system is either passive if a conventional damper is incorporated or is semi-active with a variable damper. A new control oriented dynamic model of the MacPherson suspension system is developed in this thesis to consider the effects of the suspension structure on the dynamic response and a new kinematic model is proposed to investigate those suspension kinematic parameters affecting both handling performance and stability of the vehicle. The performance of MacPherson suspension system under alternative hybrid semi-active controls is evaluated. It is shown that the contribution of different control strategies on the ride quality enhancement of the vehicle could be similar whereas their effectiveness on the performance of suspension kinematc parameters is completely different. Using the H {592} robust control theory, a full state feedback controller is designed to improve MacPherson suspension specifications. The gain of the controller is optimized so that the trade-off between the requirements is achieved. To be more practical and to reduce the design cost, H, output feedback control theory is employed to design a controller with the minimal cost design. To optimize the controller gain, the LMI and Genetic Algorithm optimization tools are used. It is shown that the output controller can improve the suspension performance close to that of a full state feedback controller. A magnetorheological damper with continuously variable damping is considered as the actuator to the system. In order to tune the current signal of the damper so as to track the desired force calculated from the controller unit, a mathematical dynamic model of the damper is required. For modelling the damper, the MR damper is characterized by a piece-wise polynomial model which is identified by using the data acquired from various tests in the laboratory. The dynamic behaviour of the MR damper on control performance is investigated. The Hardware-in-the-Loop Simulation is made and the effectiveness of the controllers is evaluated through experiments

Bayesian Estimation of Shift Point in Shape Parameter of Inverse Gaussian Distribution Under Different Loss Functions

In this paper, a Bayesian approach is proposed for shift point detection in an inverse Gaussian distribution. In this study, the mean parameter of inverse Gaussian distribution is assumed to be constant and shift points in shape parameter is considered. First the posterior distribution of shape parameter is obtained. Then the Bayes estimators are derived under a class of priors and using various loss functions. We assumed uniform, Jeffreys, exponential, gamma and chi square distributions as prior distributions. The squared error loss function (SELF), entropy loss function (ELF), linex loss function (LLF) and precautionary loss function (PLF), are used as loss functions. We attempt to find out the best estimator for shift point under various priors and loss functions. The proposed Bayesian approach can be adapted to any similar problem for shift point detection. Simulation studies were done to investigate the performance of different loss functions. The results of simulation study denote that the Jeffrey prior distribution under PLF has the most accurate estimation of shift point for sample size of 20, and the gamma prior distribution under SELF has the most accurate estimation of shift point for sample size of 50

Cost Analysis of Acceptance Sampling Models Using Dynamic Programming and Bayesian Inference Considering Inspection Errors

Acceptance Sampling models have been widely applied in companies for the inspection and testing the raw material as well as the final products. A number of lots of the items are produced in a day in the industries so it may be impossible to inspect/test each item in a lot. The acceptance sampling models only provide the guarantee for the producer and consumer that the items in the lots are according to the required specifications that they can make appropriate decision based on the results obtained by testing the samples. Acceptance sampling plans are practical tools for quality control applications which consider quality contracting on product orders between the vendor and the buyer. Acceptance decision is based on sample information. In this research, dynamic programming and Bayesian inference is applied to decide among decisions of accepting, rejecting, tumbling the lot or continuing to the next decision making stage and more sampling. We employ cost objective functions to determine the optimal policy. First, we used the Bayesian modelling concept to determine the probability distribution of the nonconforming proportion of the lot and then dynamic programming is utilized to determine the optimal decision. Two dynamic programming models have been developed. First one is for the perfect inspection system and the second one is for imperfect inspection. At the end, a case study is analysed to demonstrate the application the proposed methodology and sensitivity analyses are performed

Absorbing Markov Chain Models to Determine Optimum Process Target Levels in Production Systems with Rework and Scrapping

In this paper, absorbing Markov chain models are developed to determine the optimum process mean levels for both a single-stage and a serial two-stage production system in which items are inspected for conformity with their specification limits. When the value of the quality characteristic of an item falls below a lower limit, the item is scrapped. If it falls above an upper limit, the item is reworked. Otherwise, the item passes the inspection. This flow of material through the production system can be modeled in an absorbing Markov chain characterizing the uncertainty due to scrapping and reworking. Numerical examples are provided to demonstrate the application of the proposed model

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

New Insights into Bayesian Inference

This book is an introduction to the mathematical analysis of Bayesian decision-making when the state of the problem is unknown but further data about it can be obtained. The objective of such analysis is to determine the optimal decision or solution that is logically consistent with the preferences of the decision-maker, that can be analyzed using numerical utilities or criteria with the probabilities assigned to the possible state of the problem, such that these probabilities are updated by gathering new information