Design of Switching Multiobjective Controller: A New Approach

Design of switching 2/∞ output-feedback controller for discrete-time LTI systems with state-multiplicative noise is considered. The closed loop system achieves a minimum bound on the stochastic 2 performance level, while satisfying the ∞ performance. The proposed controller is based on a fuzzy supervisor which manages the combination of two separate 2 and ∞ controllers. A convex formulation of the two controllers leads to a structure which benefits from the advantages of both controllers to ensure a good performance in both the transient phase (2 controller) and the steady phase (∞ controller). The stability analysis uses the Lyapunov technique, inspired from switching system theory, to prove that the closed loop system with the proposed controller structure remains globally stable despite the configuration changing

Design and Simulation of Adaptive Neuro Fuzzy Inference Based Controller for Chaotic Lorenz System

Chaos is a nonlinear behavior that shows chaotic and irregular responses to internal and external stimuli in dynamic systems. This behavior usually appears in systems that are highly sensitive to initial condition. In these systems, stabilization is a highly considerable tool for eliminating aberrant behaviors. In this paper, the problem of stabilization and tracking the chaos are investigated. In fact, this problem can be divided into two categories, regulation and tracking. These kinds of stabilization have been studied, first regardless of the chaos and then considering the chaos. For this purpose, smart and powerful adaptive neuro fuzzy inference system (ANFIS) technique is used because intelligent approaches unlike the classical methods do not require complex mathematical equations and do not need to acquire the dynamics. Moreover, ANFIS is a complete and optimized fuzzy approach that has both advantages of neural network and fuzzy network. Furthermore, it can acquire fuzzy membership functions automatically. The proposed technique is examined by a famous example of a chaos system called Lorenz system. The simulation results show the ability of the proposed technique and its effectiveness in comparison with PID controller in the system

Direct Optimal Motion Planning for Omni-directional Mobile Robots under Limitation on Velocity and Acceleration

This paper describes a low computational direct approach for optimal motion planning and obstacle avoidance of Omni-directional mobile robots within velocity and acceleration constraints on the robot motion. The main purpose of this problem is the minimization of a quadratic cost function while limitation on velocity and acceleration of robot is considered and collision with any obstacle in the robot workspace is avoided. This problem can be formulated as a constraint nonlinear optimal control problem. To solve this problem, a direct method is utilized which employs polynomials functions for parameterization of trajectories. By this transforming, the main optimal control problem can be rewritten as a nonlinear programming problem (NLP) with lower complexity. To solve the resulted NLP and obtain optimal trajectories, a new approach with very small run time is used. Finally, the performance and effectiveness of the proposed method are tested in simulations and some performance indexes are computed for better assessment. Furthermore, a comparison between proposed method and another direct method is done to verify the low computational cost and better performance of the proposed method

Resiliency-oriented operation of distribution networks under unexpected wildfires using multi-horizon information-gap decision theory

Extreme events may trigger cascading outages of different components in power systems and cause a substantial loss of load. Forest wildfires, as a common type of extreme events, may damage transmission/distribution lines across the forest and disconnect a large number of consumers from the electric network. Hence, this paper presents a robust scheduling model based on the notion of information-gap decision theory (IGDT) to enhance the resilience of a distribution network exposed to wildfires. Since the thermal rating of a transmission/distribution line is a function of its temperature and current, it is assumed that the tie-line connecting the distribution network to the main grid is equipped with a dynamic thermal rating (DTR) system aiming at accurately evaluating the impact of a wildfire on the ampacity of the tie-line. The proposed approach as a multi-horizon IGDT-based optimization problem finds a robust operation plan protected against the uncertainty of wind power, solar power, load, and ampacity of tie-lines under a specific uncertainty budget (UB). Since all uncertain parameters compete to maximize their robust regions under a specific uncertainty budget, the proposed multi-horizon IGDT-based model is solved by the augmented normalized normal constraint (ANNC) method as an effective multi-objective optimization approach. Moreover, a posteriori out-of-sample analysis is used to find (i) the best solution among the set of Pareto optimal solutions obtained from the ANNC method given a specific uncertainty budget, and (ii) the best resiliency level by varying the uncertainty budget and finding the optimal uncertainty budget. The proposed approach is tested on a 33-bus distribution network under different circumstances. The case study under different conditions verifies the effectiveness of the proposed operation planning model to enhance the resilience of a distribution network under a close wildfire. © 2022 The Author(s

Assessment of the potential contamination risk of nitrate in groundwater using indicator kriging (in Amol-Babol Plain, Iran)

In arid and semi-arid regions such as Amol–Babol Plain in north Iran, groundwater is a major source of drinking water. Excessive usage of fertilizers in agricultural land, domestic sewage and industrial wastewater may result in nitrate contamination. The main objective of this study is to assess the potential contamination risk of nitrate pollution. The groundwater samples were collected from 100 agriculture wells during wet and dry seasons in 2009 and analyzed for nitrate concentration. Indicator kriging (IK) method is applied to create maps indicating the predicted probability of nitrate concentrations in groundwater exceeding the WHO drinking water standard of 10 mg/L-N. Based on the risk probability maps, some areas on the southern side of Babol City and the north and north-western side of Amol City showed a high probability of nitrate contamination. Seasonal maps indicated that the probability of nitrate contamination increased in the wet season, compared to the dry season in the study area, due to increase runoff from irrigated lands. Indicator kriging with local indicator thresholds is shown to be a reliable method to assess uncertainty in the estimation

Spatiotemporal variation of groundwater quality using integrated multivariate statistical and geostatistical approaches in Amol–Babol Plain, Iran

In recent years, groundwater quality has become a global concern due to its effect on human life and natural ecosystems. To assess the groundwater quality in the Amol–Babol Plain, a total of 308 water samples were collected during wet and dry seasons in 2009. The samples were analysed for their physico-chemical and biological constituents. Multivariate statistical analysis and geostatistical techniques were applied to assess the spatial and temporal variabilities of groundwater quality and to identify the main factors and sources of contamination. Principal component analysis (PCA) revealed that seven factors explained around 75 % of the total variance, which highlighted salinity, hardness and biological pollution as the dominant factors affecting the groundwater quality in the Plain. Two-way analysis of variance (ANOVA) was conducted on the dataset to evaluate the spatio-temporal variation. The results showed that there were no significant temporal variations between the two seasons, which explained the similarity between six component factors in dry and wet seasons based on the PCA results. There are also significant spatial differences (p > 0.05) of the parameters under study, including salinity, potassium, sulphate and dissolved oxygen in the plain. The least significant difference (LSD) test revealed that groundwater salinity in the eastern region is significantly different to the central and western side of the study area. Finally, multivariate analysis and geostatistical techniques were combined as an effective method for demonstrating the spatial structure of multivariate spatial data. It was concluded that multiple natural processes and anthropogenic activities were the main sources of groundwater salinization, hardness and microbiological contamination of the study area