Intelligent Tuning Method Research Articles

Intelligent Speed Control and Performance Investigation of a Vector Controlled Electric Vehicle Considering Driving Cycles

In this paper, battery electric vehicle (BEV) controllers are smartly tuned with particle swarm optimization (PSO) and genetic algorithm (GA) to ensure good speed regulation. Intelligent tuning is ensured with a proposed and well-defined cost function that aims to satisfy the design requirements in terms of minimum overshoot, fast response, and tolerable steady state input. Two proposed cost functions are formulated for both simple speed input and for driving cycles. The BEV is controlled with the field oriented control technique (FOC), and it is driven by a permanent magnet synchronous motor (PMSM). An efficient control scheme based on FOC is built using a simplified closed loop control system including BEV components such as regulators, inverter, traction machine, and sensors. Simulation results show that the optimum controller gains obtained by intelligent tuning have resulted in satisfactory BEV performance that sustains the harsh environmental conditions. Robustness tests against BEV parameter changes and environmental parameter variations confirmed the effectiveness of intelligent tuning methods.

Numerical Approach on Hybrid PID-AFC Controller using Different Intelligent Tuning Methods to Reduce the Vibration of the Suspended Handle

This paper focusses on the study of vibration attenuations for suspended handle models that are generated from power tools using an intelligent active force control (AFC) tuning strategy. Four types of control schemes are comparatively evaluated in suppressing the vibration of the handle, such as proportional-integral-derivative (PID), PID-AFC-crude approximation (AFCCA), PID-AFC-fuzzy logic (AFCFL) and PID-AFC-iterative learning method (AFCILM) control schemes. In all control schemes, the estimated counter force is generated from the actuating force and appropriate estimated mass M* that has been intelligently tuned to counter the system disturbances. The disturbances are modelled based on the power tools vibration (i.e., internal disturbance) and uncertainties during the operation (i.e., external disturbances). The study shows that the AFCCA scheme demonstrates the best performance when the M(CL) is tuned at 0.04 kg. For the AFCFL control scheme, the best response is obtained for the membership function of trapezoidal shape with M(FL) of 0.0403 kg, while for AFCILM control scheme, the best response is achieved when M(ILM) is tuned to 0.04 kg, with both parameters (A and B) set at 0.6. Overall, PID-AFCCA scheme shows the best performances for all of the case studies, followed by PID-AFCFL and PID-AFCILM. The findings of this study can benefit the power tool manufacturers and provide the basis of effectively intelligent controller design for the power tools application.

Intelligent Tuning of Microwave Cavity Filters Using Granular Multi-Swarm Particle Swarm Optimization

The tuning of microwave cavity filters (MCFs) is a complex process to improve the filtering performance. In practice, the tuning is mostly conducted in a manual way, and thus is time and resource intensive. Toward the demand for automatic tuning of MCFs with high accuracy and efficiency, this article proposes an intelligent tuning method for MCFs via modeling and optimization. The main contributions are threefold as follows: 1) A series of performance evaluation functions are defined to comprehensively characterize the tuning output; 2) a block modeling method is proposed to construct the electromechanical characteristic model to facilitate the calculation of the cost function; 3) an improved particle swarm optimization (PSO) algorithm, named granular multi-swarm PSO (GMS-PSO), is proposed to achieve quick search of optimal combinations of the tunable components. The effectiveness and practicality of the proposed method are demonstrated by experiments with a real intelligent tuning system.

Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy

Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to the sample topography. This method gets enough information about the output signals of PID controller and tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve good tracking performance.

Effect of Probabilistic Pattern on System Voltage Stability in Decentralized Hybrid Power System

This paper presents an proportional integral (PI) based voltage-reactive power control for wind diesel based decentralized hybrid power system with wide range of disturbances to demonstrate the compensation effect on system with intelligent tuning methods such as genetic algorithm (GA), artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). The effect of probabilistic load and/or input power pattern is introduced which is incorporated in MATLAB simulink model developed for the study of decentralized hybrid power system. Results show how tuning method becomes important with high percentage of probabilistic pattern in system. Testing of all tuning methods shows that GA, ANN and ANFIS can preserve optimal performances over wide range of disturbances with superiority to GA in terms of settling time using Integral of Square of Errors (ISE) criterion as fitness function.