Mohammad Hossein Ramezani

Robust control of speed and temperature in a power plant gas turbine.

In this paper, an H(∞) robust controller has been designed for an identified model of MONTAZER GHAEM power plant gas turbine (GE9001E). In design phase, a linear model (ARX model) which is obtained using real data has been applied. Since the turbine has been used in a combined cycle power plant, its speed and also the exhaust gas temperature should be adjusted simultaneously by controlling fuel signals and compressor inlet guide vane (IGV) position. Considering the limitations on the system inputs, the aim of the control is to maintain the turbine speed and the exhaust gas temperature within desired interval under uncertainties and load demand disturbances. Simulation results of applying the proposed robust controller on the nonlinear model of the system (NARX model), fairly fulfilled the predefined aims. Simulations also show the improvement in the performance compared to MPC and PID controllers for the same conditions.

Hierarchical optimal control of large-scale nonlinear chemical processes

In this paper, a new approach is presented for optimal control of large-scale chemical processes. In this approach, the chemical process is decomposed into smaller sub-systems at the first level, and a coordinator at the second level, for which a two-level hierarchical control strategy is designed. For this purpose, each sub-system in the first level can be solved separately, by using any conventional optimization algorithm. In the second level, the solutions obtained from the first level are coordinated using a new gradient-type strategy, which is updated by the error of the coordination vector. The proposed algorithm is used to solve the optimal control problem of a complex nonlinear chemical stirred tank reactor (CSTR), where its solution is also compared with the ones obtained using the centralized approach. The simulation results show the efficiency and the capability of the proposed hierarchical approach, in finding the optimal solution, over the centralized method.

An Adaptive Path Loss Channel Model for Wave Propagation in Multilayer Transmission Medium

Advances in micro robots in non-invasive medicine have enabled physicians to perform diagnostic and therapeutic procedures with higher resolution and lower risk than before. However, navigation and precise localisation of such micro robots inside human body still remains a challenge. This is mostly due to the 1) lack of precise communication channel models, 2) inhomogeneity of the propagation medium and 3) non-geometric boundaries of the tissues morphometric parameters. In this study, we derive novel intra-body path loss channel models for wave propagation in wireless capsule endoscopy, i.e., propagation through the gastrointestinal tract and the abdominal wall. We formulate an adaptive attenuation parameter as a function of permittivity, conductivity and the thickness of various layers between the transmitter and the receiver. The standard deviation of modelling error of the path loss using our adaptive channel model is smaller than 50% of that of existing channel models. We further analyse the sensitivity of the path loss model to the variations of thickness of different abdominal wall layers. We finally show that the thickness of the fat layer has the greatest influence on the total attenuation parameter of the path loss model and therefore, we modify our adaptive model accordingly.

State transition of wind turbines based on empirical inference on model residuals

In this study, we propose a method to monitor state transitions for wind turbines based on the online inference on model residuals. Slowly developing faults in wind turbine can, when not detected and fixed on time, cause severe damage and downtime. Early state transition detection attempts to reduce the risk of sever damage and downtime by recognizing changes in the data and adapted predictive models appropriately. As fault detection studies often deal with hard thresholds, the Bayesian analysis comes with the advantage of probability measures. We propose a Bayesian approach to state transition based on hidden variables relevant for the online predictor, namely the time since the last state transition. It is of great interest to see that exact online inference can be performed at every time step, given an underlying predictive model based on a hazard function. Here the hazard function describes how likely it is to undergo a transition given the data since the last state transition. It is imperative that the hyper-parameters are known before-hand in order to perform the inference on the model. We show that Bayesian inference on state transition can be performed for assumed fixed and known hyper-parameters, and we emphasize that the selection of the hyper-parameters can be treated as a machine learning problem and trained given a data set. Comparing fixed to learned hyper-parameters points out the impact they have on the predictive performance.

Hierarchical optimal control of nonlinear systems; An application to a benchmark CSTR problem

In this paper, a novel computational algorithm is proposed for hierarchical optimal control of nonlinear systems. The hierarchical control uses a new coordination strategy based on the gradient of the coordination errors. This type of coordination extremely reduces the number of iterations required for obtaining the overall optimal solution. The performance and the convergence rate of the proposed approach, in compare to the classical gradient-type interaction prediction approach, is shown through simulations of a benchmark continuous stirred tank reactor (CSTR) problem.

On the ratio of independent complex Gaussian random variables

In this paper, we derive a closed form equation for the joint probability distribution

Tracking of a 3.D.O.F robot using adaptive robust control under uncertainties

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Semi-definite programming-based localization algorithm in networks with inhomogeneous transmission medium

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