In this study, a discrete fractional order PID controller for nonlinear systems is proposed. U-model is firstly employed to transform nonlinear systems into a linear-like model and model complexity...

Discrete fractional order PID controller design for nonlinear systems

Machine learning has revolutionized the way complex problems are solved in engineering. In the current work, machine learning methodology has been applied for predictive modeling of surface roughne...

Machine learning based predictive modeling and control of surface roughness generation while machining micro boron carbide and carbon nanotube particle reinforced Al-Mg matrix composites

Complex Order PIα+jβ Dγ+jθ Design for Surface Roughness Control in Machining CNT Al-Mg Hybrid Composites

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Complex Order PIa+jbDc+jd Controller Design for a Fractional Order DC Motor System

The auto industry is critically dependent on lean and flexible manufacturing systems to sustain in today’s dynamic and price sensitive markets. In the current work, a machine learning-based predictive modeling and control strategy is proposed for the attainment of lean manufacturing (LM) through effective management of manufacturing flexibility. Firstly, lean manufacturing models were identified based on machine, labour, volume, routing, product flexibilities and material handling for forty six auto parts manufacturing companies in Pune region (India). As many as twenty three lean manufacturing models were derived based on system identification (control theory) structures: auto regressive with exogenous variables (ARX), auto regressive moving average with exogenous variables (ARMAX), output error (OE) and Box Jenkins (BJ) methods. All predictive models were compared for their relative performances based on validation indices such as the FIT%, mean squared error (MSE), final prediction error (FPE) and the number of model parameters. The ARX 750 structure attained the best predictive characteristics for LM (FIT 91.86%). This model was controlled for a set point of 0.8 LM level and corresponding levels of flexibilities were determined. The machine flexibility (MF) was identified to be the most significant contributor to lean manufacturing at a level of 0.7596. Consequently, MF was also modeled based on its seven sub parameters. The ARMAX 2120 structure obtained the best performance characteristics (FIT 99.94%) for MF modeling. Furthermore, this MF model was controlled at a level of 0.7596 (corresponding to 0.8 LM) and the required levels of MF sub parameters were determined. Thus, the current work provides definite guidelines for company managers to target quantitative attainment of specific machine flexibility parameters to ultimately attain an improved lean manufacturing level in the automotive industry.

Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry

Tuning of Fractional Complex Order PID Controller

This paper deals with Fractional Complex Order Controller FCOC tuning. The paper presents new structure of FCOC with the form PIx+iyD, in which x and y are the real and imaginary parts of the integral complex order, respectively. With the controller's five parameters, we can fulfil five design requirements. Design specifications are set to ensure robustness toward gain variations, noise on system output and disturbance. A tuning method for the Controller is presented to accomplish design requirements. The proposed design method is investigated with a Second Order Plus Time Delay resonant system. Frequency and time domain analysis are presented in this manuscript.

Tuning of a Pi x+iy D Fractional Complex Order Controller

In this paper, a discrete-time robust controller design method for optimal reference tracking in preview systems is validated on an experimental test bench. In the context of preview systems, it is supposed that future values of the reference signal are available a number of time steps ahead. The objective is to design a control algorithm that minimizes a quadratic error between the reference and the output of the system. The proposed solution combines a robust feedback controller with a feedforward anticipative filter. The theoretical description of this new approach is given and experimental results on a water level control system are presented.

Ayadi Guefrachi

Papers

Tuning of Fractional Complex Order PID Controller

Tuning of a Pi x+iy D Fractional Complex Order Controller

Anticipative Robust Design Applied to a Water Level Control System