This paper presents a comparison among the bee colony optimization (BCO), differential evolution (DE), and harmony search (HS) algorithms. In addition, for each algorithm, a type-1 fuzzy logic system (T1FLS) for the dynamic modification of the main parameters is presented. The dynamic adjustment in the main parameters for each algorithm with the implementation of fuzzy systems aims at enhancing the performance of the corresponding algorithms. Each algorithm (modified and original versions) is analyzed and compared based on the optimal design of fuzzy systems for benchmark control problems, especially in fuzzy controller design. Simulation results provide evidence that the FDE algorithm outperforms the results of the FBCO and FHS algorithms in the optimization of fuzzy controllers. Statistically is demonstrated that the better errors are found with the implementation of the fuzzy systems to enhance each proposed algorithm.

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Comparative Study in Fuzzy Controller Optimization Using Bee Colony, Differential Evolution, and Harmony Search Algorithms

Surface temperature control of a thin aluminium plate were investigated using closed loop control approach implemented using inverse problem. The one-dimensional model with periodic boundary condition was solved using the Laplace transform and both direct problem and inverse problem transfer functions were obtained. The resulting transfer functions were expanded using Zero-Pole expansion to obtain a finite order polynomial transfer function. Simulation results for closed loop control using fractional controllers (FOPIλ, FOPDμ, and FOPIDμ) were evaluated.

Fractional Controller for Thin Plate Surface Temperature Control

Battery is a very important necessity as an electrical energy storage for DC load type. However, as electric energy storage, the battery has a limit storage capacity. The battery must be recharged when the electrical energy stored in the battery has been exhausted to keep the DC load in operation. Unfortunately, batteries in different types of DC loads have different voltages and capacities. So for charging the battery also requires a different voltage. While the existing battery charger is generally static specifically for one type of battery. From this problem, the paper proposed an adaptive power charge system. A system that can adaptively charge electrical energy on batteries that have different voltages and capacities through one port. The converter used in this paper is the buck converter to lower the output voltage from the input voltage. The system uses tracking duty cycle to know the type of DC load. After knowing the type of dc load then the system determines the setting point voltage. To keep the output voltage stable, the system uses PID control. With this adaptive power charge, the charging process will be more efficient and multifunction because it can be used for various types of DC load. The system can provide an output voltage of 19 volts when connected to a laptop and provides an output voltage of 5 volts when connected to a mobile phone on setling time 10ms.

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Adaptive Power Charge Using PID Controller on DC Load Application

Fractional-order Proportional Integral Derivative Controllers

An active vibration control method based on energy-fuzzy for cantilever structures excited by aerodynamic loads

This article propose a hybrid fractional order PIDcontroller which is optimized with classical proportional integralderivative controller (PID) gives an exquisite response. Here thetwo tuning method are used to evaluate the parameters of PIDcontroller, first one is Ziegler-Nichols and other one is Astrom-Hagglund method. The parameters of FO-PID controller in useas the proportional constant, integral constant are by Ziegler-Nichols and derivative constant by Astrom-Hagglund method. In order to obtain required solutions, two non-linear equationsare derived to find the fractional order of the integral term andderivative term The step response shows the benefits of abovediscussed hybrid fractional order PID controller when comparingwith existing controller. Simulated results are carried by matlab2012(a).

Design of Optimal PID[FOPID] Controller for Linear System

Differential Evolution algorithm has recently emerged as a simple yet very powerful technique for real parameter optimization. This article describes an application of DE for the design of fractional order proportional Integral Derivative controller. FOPID controller parameter are composed of the proportional constant, integral constant, derivative constant, derivative order and integer order, and its design is more complex than that of conventional integer order PID controller. Here the controller synthesis is based on minimising the integral square error of given plant by which a single objective optimization problem achieved. This article proposes a tuning method of fractional order PID controller for a given plant by nature inspired algorithms which is Differential Evolutionary for better dynamic and static performance. The mutation of DE algorithm is modified by which the optimal response is obtained and also compared with Genetic Algorithm and PSO algorithm. The comparison is done on the basis of step response with characteristics like maximum overshoot (M p%), rise time(t r) and steady state error. Simulated results are represented on matlab 2012(a).

Fractional Order PID Controller with an Improved Differential Evolution Algorithm

This article proposes a hybrid fractional order PID controller optimized with Tilted integral derivative controller (TID) which gives an exquisite response. The coefficients of PID controller are tuned with ziegler-Nichols and Astrom-Hagglund method. FO-PID controller parameters i.e. proportional constant(Kp), integral constant (Ki) are taken by Ziegler-Nichols, derivative constant (Kd) by Astrom-Hagglund method and Tilted integral derivative controller parameters are taken by Astrom-Hagglund. In order to calculate required solutions, two non-linear equations are derived to find the fractional order of the integral term (λ) and derivative term (μ). The step response shows the benefits of above discussed hybrid fractional order PID controller when compared with existing controller. Simulated results are represented on matlab 2012(a).

Rinki Maurya

Papers

Design of Optimal PID[FOPID] Controller for Linear System

Fractional Order PID Controller with an Improved Differential Evolution Algorithm

Design of optimal hybrid fractional order PID controller

Challenges, Configuration, Control, and Scope of DC Microgrid Systems: A Review