Amirhossein Nikoofard

Bio: Amirhossein Nikoofard is an academic researcher from K.N.Toosi University of Technology. The author has contributed to research in topics: Model predictive control & Computer science. The author has an hindex of 8, co-authored 35 publications receiving 211 citations. Previous affiliations of Amirhossein Nikoofard include University of Tehran & Norwegian University of Science and Technology.

Multiobjective invasive weed optimization: Application to analysis of Pareto improvement models in electricity markets

Abstract: This paper presents a proposal for multiobjective Invasive Weed Optimization (IWO) based on nondominated sorting of the solutions. IWO is an ecologically inspired stochastic optimization algorithm which has shown successful results for global optimization. In the present work, performance of the proposed nondominated sorting IWO (NSIWO) algorithm is evaluated through a number of well-known benchmarks for multiobjective optimization. The simulation results of the test problems show that this algorithm is comparable with other multiobjective evolutionary algorithms and is also capable of finding better spread of solutions in some cases. Next, the proposed algorithm is employed to study the Pareto improvement model in two complex electricity markets. First, the Pareto improvement solution set is obtained for a three-player oligopolistic electricity market with a nonlinear demand function. Then, the IEEE 30-bus power system with transmission constraints is considered, and the Pareto improvement solutions are found for the model with deterministic cost functions. In addition, NSIWO algorithm is used to analyze this system with stochastic cost data in a risk management problem which maximizes the expected total profit but minimizes the profit risk in the market.

State and Parameter Estimation of a Drift-Flux Model for Underbalanced Drilling Operations

Abstract: We consider a drift-flux model (DFM) describing multiphase (gas–liquid) flow during drilling. The DFM uses a specific slip law, which allows for transition between single and two phase flows. With this model, we design unscented Kalman filter (UKF) and extended Kalman filter (EKF) for the estimation of unmeasured state, production, and slip parameters using real-time measurements of the bottom-hole pressure, outlet pressure, and outlet flow rate. The OLGA high-fidelity simulator is used to create two scenarios from underbalanced drilling on which the estimators are tested: a pipe connection scenario and a scenario with a changing production index (PI). A performance comparison reveals that both UKF and EKF are capable of identifying the PIs of gas and oil from the reservoir into the well with acceptable accuracy, while the UKF is more accurate than the EKF. Robustness of the UKF and EKF for the pipe connection scenario is studied in case of uncertainties and errors in the reservoir and well parameters of the model. It is found that these methods are very sensitive to errors in the reservoir pore pressure value. However, they are robust in the presence of error in the liquid density value of the model.

Constrained MPC design for heave disturbance attenuation in offshore drilling systems

Abstract: This paper presents a constrained model predictive control scheme for regulation of the annular pressure in a well during managed pressure drilling from a floating rig subject to heave motion. The results show that closed-loop simulation without disturbance has a fast regulation response and without any overshoot. The robustness of controller to deal with heave disturbances is investigated. The constrained MPC shows good disturbance rejection capabilities. The simulation results show that this controller has better performance than a PID controller and is also capable of handling constraints of the system with the heave disturbance.

Stable Adaptive Systems

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Unscented Kalman Filterの計測への応用に関する研究

Abstract: where xt+1 is the position and orientation of the robot (with respect to a reference frame) at time t + 1, with (ξ, η) giving the x and y coordinates and θ the angle (with respect to the x-axis) that the robot is facing. The robot has two drive wheels, of radius r on an axle of length d. During time period t the right wheel is believed to rotate at a velocity of φRt and the left at a velocity of φLt. In this example, these velocities are fixed with φRt = 0.4 and φLt = 0.1. The state update function, F , calculates where the robot should be at each time point, given its previous position. However, in reality, there is some random fluctuation in the velocity of the wheels, for example, due to slippage. Therefore the actual position of the robot and the position given by equation F will differ. The magnitude of these random fluctuations is included in the model through Σx.

Plant intelligence based metaheuristic optimization algorithms

Abstract: Classical optimization algorithms are insufficient in large scale combinatorial problems and in nonlinear problems. Hence, metaheuristic optimization algorithms have been proposed. General purpose metaheuristic methods are evaluated in nine different groups: biology-based, physics-based, social-based, music-based, chemical-based, sport-based, mathematics-based, swarm-based, and hybrid methods which are combinations of these. Studies on plants in recent years have showed that plants exhibit intelligent behaviors. Accordingly, it is thought that plants have nervous system. In this work, all of the algorithms and applications about plant intelligence have been firstly collected and searched. Information is given about plant intelligence algorithms such as Flower Pollination Algorithm, Invasive Weed Optimization, Paddy Field Algorithm, Root Mass Optimization Algorithm, Artificial Plant Optimization Algorithm, Sapling Growing up Algorithm, Photosynthetic Algorithm, Plant Growth Optimization, Root Growth Algorithm, Strawberry Algorithm as Plant Propagation Algorithm, Runner Root Algorithm, Path Planning Algorithm, and Rooted Tree Optimization.

Catastrophic cascade of failures in interdependent networks

Abstract: Complex networks have been studied intensively for a decade, but research still focuses on the limited case of a single, non-interacting network. Modern systems are coupled together and therefore should be modelled as interdependent networks. A fundamental property of interdependent networks is that failure of nodes in one network may lead to failure of dependent nodes in other networks. This may happen recursively and can lead to a cascade of failures. In fact, a failure of a very small fraction of nodes in one network may lead to the complete fragmentation of a system of several interdependent networks. A dramatic real-world example of a cascade of failures (‘concurrent malfunction’) is the electrical blackout that affected much of Italy on 28 September 2003: the shutdown of power stations directly led to the failure of nodes in the Internet communication network, which in turn caused further breakdown of power stations. Here we develop a framework for understanding the robustness of interacting networks subject to such cascading failures. We present exact analytical solutions for the critical fraction of nodes that, on removal, will lead to a failure cascade and to a complete fragmentation of two interdependent networks. Surprisingly, a broader degree distribution increases the vulnerability of interdependent networks to random failure, which is opposite to how a single network behaves. Our findings highlight the need to consider interdependent network properties in designing robust networks.