Parameters of digital proportional-integral/proportional-integral-derivative controllers are usually calculated using commonly known conventional methods or solution of discrete-time equations. In literature, a model-based compact form formulation for calculation of discrete-time proportional-integral/proportional-integral-derivative controller parameters has not been come across yet. The proposed model-based compact form formulations are introduced to calculate the proportional-integral parameters in discrete time as a new approach. Generally, different types of control techniques are chosen in similar studies for double-loop control for direct current-direct current boost converter control except proportional-integral/proportional-integral. In this study, double-loop proportional-integral controller is used as a different control method from literature. By this way, the most important advantages of the proposed study are to reduce different design methods to a unique proportional-integral design method and shorten all calculations. The accuracy of the double-loop proportional-integral controller's parameters calculated using the model-based compact form formulations is validated both in simulation and experimental studies under various disturbance effects. Satisfactory performance of the proposed controller under model uncertainty and other cases are comparatively shown with the predefined performance criteria.

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PID controller is a popular control method still widely used in process industry. In literature there are model/non-model based calculation methods for PID parameters. However, a model based analytic formulation in compact form in discrete time has not been come across yet. This study presents a new approach for calculation of PID parameters with model based analytic formulation (MBCF), which is presented uniquely in this paper, in compact form, in discrete time. Furthermore, a procedure for implementation of the proposed formulas is given in four stages. The formulations in related literature for PID parameter calculation are all derived for continuous time. Therefore, extra transformations are required for a discrete time design. The proposed MBCF formulation method reduces extra calculation burden and simplifies calculation complexity. Moreover, this method provides a direct calculation method for digital PID controller design in discrete time. The derived expressions in this study also provide a fast, easy-implemented, and practical PID parameter calculation method for all field researchers and application engineers. The validation of proposed MBCF formulations are comparatively proved with the simulations and the real time application results.

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Maximum power point tracker in a photovoltaic system allows to maximize the energy drawn from the connected photovoltaic modules. In the partial shade conditions there can be more than one maximum point in photovoltaic output power curve. The solution for this situation is a maximum power point tracker algorithm, which finds the global maximum. In literature, there is a large number of studies on maximum power point trackers. Therefore designers are drowning in a sea of knowledge. This study eliminates similar studies and classified them into groups, and at the end of the study a comparison table is given to guide the designers in the performance information of the selected studies. This study aims to guide the designers to make a sensible selection of a maximum power point tracker algorithm for partial shade conditions.

The purpose of this study is to obtain the dynamic model of an electrical powered wheelchair and to estimate the state variables of right and left DC motor currents with the designed observer. First, the dynamic equations are written and then discrete-time state space model of the electrical powered wheelchair is directly obtained from this dynamic equations. Discrete time state space model of the electrical powered wheelchair is verified with the transfer function obtained using the dynamic equations. In addition, the accuracy of the estimated left and right DC motor current values are validated in the simulation results.

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One of the crucial challenges of solving many-objective optimization problems is uniformly well covering of the Pareto-front (PF). However, many the state-of-the-art optimization algorithms are capable of approximating the shape of many-objective PF by generating a limited number of non-dominated solutions. The exponential increase of the population size is an inefficient strategy that increases the computational complexity of the algorithm dramatically—especially when solving many-objective problems. In this paper, we introduce a machine learning-based framework to cover sparse PF surface which is initially generated by many-objective optimization algorithms; either by classical or meta-heuristic methods. The proposed method, called many-objective reverse mapping (MORM), is based on constructing a learning model on the initial PF set as the training data to reversely map the objective values to corresponding decision variables. Using the trained model, a set of candidate solutions can be generated by a variety of inexpensive generative techniques such as Opposition-based Learning and Latin Hypercube Sampling in both objective and decision spaces. Iteratively generated non-dominated candidate solutions cover the initial PF efficiently with no further need to utilize any optimization algorithm. We validate the proposed framework using a set of well-known many-objective optimization benchmarks and two well-known real-world problems. The coverage of PF is illustrated and numerically compared with the state-of-the-art many-objective algorithms. The statistical tests conducted on comparison measures such as HV, IGD, and the contribution ratio on the built PF reveal that the proposed collaborative framework surpasses the competitors on most of the problems. In addition, MORM covers the PF effectively compared to other methods even with the aid of large population size.