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This paper discusses the problem of local path planning in a static-obstacle environment by designing a PSO-based receding horizon control approach. In order to avoid obstacles, a virtual robot is first designed and moves along the boundary of obstacles. Then, in the framework of receding horizon control, a cost function is proposed where the virtual robot and the target position are integrated, which...
In this paper, a local path planning algorithm based on particle swarm optimization is adopted. The obstacle avoidance principle of this method is to build a virtual robot on the edge of the obstacle and allowing it to move along the edge of the obstacle. The information of the virtual robot can be given based on the message of the actual mobile robot. The corresponding cost function can be designed...
The article presents results of parametric identification of mechanical part of drive system with permanent magnet synchronous motor by means of heuristic method of Particle Swarm Optimization (PSO). The assumed mechanical part has a complex structure, consisting of two-mass connected by an elastic joint with backlash. The optimization process was designed to find model parameters that assure the...
This study focuses on the Buck Converter which is one of the Power Electronic topologies. The output of converter is tried to keep under control. The control is implemented via discrete time PI algorithm. In order to maintain efficiency of control process at high level, the controller parameters of Kp and Ki are optimized with the help of Particle Swarm Optimization (PSO) which is an iterative algorithm...
The distributed optimal power flow problem is addressed. No assumptions on the problem cost function, and network topology are needed to solve the optimization problem. A distributed particle swarm optimization algorithm is proposed, based on Deb's rule to handle hard constraints. Moreover, the approach enables to treat a class of distributed optimization problems in which the agents share a common...
The paper focuses on Particle Swarm Optimization of an airfoil model for small unmanned aerial vehicles operating in low Reynolds number. The objective function to be maximized is the lift-to-drag ratio subject to different penalty constraints. The airfoil parameterization is done using Class/Shape function Transformation. Blade Element Momentum Theory is used to develop an optimum hovering propeller...
This paper formulates and solves the problem of optimizing switching surfaces (SSs) in switching LPV (SLPV) controller design to further enhance the control performance. The conditions for the SLPV controller synthesis under fixed SSs are first presented, which involves a finite number of linear matrix inequalities. The SS design problem is then formulated as an optimization problem where the cost...
Economic Dispatch (ED) is an important aspect in any power system. The conventional methods for solving ED include Lambda-Iterative, Newton-Raphson, Quadratic programming (QP), etc. However, conventional method cannot solved non quadratic function. The input-output characteristics of a generator produced highly non-linear leading so its challenging non-convex and non-smooth optimization problem. In...
In power market, it is rather important for Gencos to choose their bids wisely. This paper conducts a research on bidding strategies for Gencos under incomplete information and proposes a bidding strategy model with elimination mechanism. By narrowing the searching space of bids to simplify the model, it avoids the algorithm trapped into a local optimum to some extent. Meanwhile, the elimination mechanism...
Blood glucose management is a daily challenge that diabetics must face. The artificial pancreas (AP) system, especially the bi-hormonal type, is regarded as a hopeful way to achieve automated regulation of blood glucose (BG). At present, the existing design of AP mainly focuses on the control performance. However, the decrease of cure costs is also important in clinical practice. In this study, economic...
Home energy management (HEM) requires optimization techniques to solve multi-variable and multi-objective problems. The optimal use of energy, the occupants comfort, the reduction of peak power and energy cost are objectives with dissimilar variables behaviors. Their solutions increase in complexity with the number of variables which would be a challenge if the real-time response is needed. Meta-heuristics...
In this paper, we have successfully formulated problems of the closed-loop supply chain network based on the triple-bottom-line perspective. Specifically this paper measures the level of corporate social responsibility (CSR) and captures that the customer demand is influenced by the level of CSR, which is rarely discussed in the current supply chain management literature. Since the formulation has...
This paper proposes an improved version of the random drift particle swarm optimization algorithm for solving the economic dispatch problem. The improvement is achieved through adding a crossover operation followed by a greedy selection process while replacing the mean best position of the particles with the personal best position of each particle in the velocity updating equation. The improved algorithm...
For controlling the multi-robot formation system, a leader-follower separation-bearing-orientation scheme (S-BOS) is proposed and the leader-follower relationship can be represented as a formation-error kinematic system through SBOS strategy. In order to achieve the control objective, a nonlinear model predictive control (NMPC) strategy is applied to formulate the formation-error kinematic into a...
Due to existence of many variables in large scale systems, design and implementation of controllers for such systems have been always important challenges. A common way to solve the problem is to obtain an equivalent reduced order model of the system in the first step and then, design a suitable controller. In this paper model reduction of large scale systems based on multi objective particle swarm...
In this Paper, torque and shaft vibration were restrained and suppressed using explicit model predictive controller. Explicit model predictive controller is used to control and reduce vibration. Explicit model predictive controller is used to solve problems with complex equations. This controller, contrary to the implicit model predictive controller that operates online, only operates in offline mode...
The economic load dispatch (ELD) problem contains many equality and inequality constraints that convert the problem to a non-smooth and non-convex problem, which makes difficulty to find absolute minimum point by using classic mathematic methods. In this paper, the presented ELD problem is a non-smooth and non-convex optimization problem which contains linear and nonlinear constraints such as valve...
This paper presents results of simulation research on fuzzy speed controller in application of an electrical drive with complex mechanical structure. Presence of elasticity and backlash were assumed in the modeled mechanical construction. These features, related with phenomena of resonance and non-linear character of the system make the design of proper speed control solution very complex. Particle...
This paper presents a comparison of the Efficient Global Optimization (EGO) and Particle Swarm Optimization (PSO) algorithms in the context of defect characterization in Eddy current non-destructive evaluation (EC-NDE). An axisymmetric internal groove is applied to an electric conducting tube; the forward model linking measurements to electromagnetic properties of the tube is solved using the finite...
This paper presents the imaginary particle swarm optimizer with reset function (iPSOR) for maximum power point tracking in photovoltaic array under partial shading condition. The cost function corresponds to the voltage-versus-power characteristic of the photovoltaic array. Depending on insolation and temperature, the cost function and its MPP vary in a complicated way. In order to track the dynamic...
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