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SUMMARYIn this paper, design, analysis and real-time trajectory tracking control of a 6-degree of freedom revolute spherical-spherical type parallel manipulator, actuated by six hybrid stepper motors, has been studied. Two different control approaches have been used to improve the trajectory tracking performance of the designed manipulator. The first approach considered a single input-single output (SISO) linear quadratic regulator (LQR) for trajectory tracking control of the manipulator. Another controller type based on a nonlinear sliding mode controller method has been utilized to take decoupled dynamic approximation model of the manipulator into account and to improve tracking performance of the manipulator. Real-time experimental results for the two different control techniques have been verified. Finally, according to the results, the nonlinear sliding mode controller method has improved the tracking performance of the designed manipulator.

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This article presents the self-tuning regulator control technique for a coupled tank liquid level system that often used in industry. An autoregressive with exogenous model has been used as the liquid process model with the self-tuning control implementation in order to track the desired tank level trajectories with disturbances and uncertainties of the system dynamics. The designed self-tuning controller has been sensitive to parameter variations of the nonlinear coupled tank system. The parameters of the proposed controller are periodically updated themselves during the process by means of online recursive least square method with the forgetting factor algorithm. In this way, the parameter variations and unwanted disturbances of the system are eliminated in real-time application. In order to demonstrate the efficiency of the self-tuning regulator control technique, the real-time studies have been executed. The obtained experimental results demonstrated that the proposed controller gives the better trajectory tracking performance and smaller magnitude in overshot and undershot than the designed classical proportional–integral and sliding mode controllers.

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Data-driven modelling techniques have recently been used in the development of engine design and control systems for defining the engine in-cylinder complex combustion process. The aim of this investigation is to improve exhaust emissions and fuel consumption by designing an electromechanical exhaust gas recirculation (EGR) cooling system consisting of an electric water pump and fan unlike conventional systems. To determine the effects of the EGR ratio and the temperature of the exhaust gas entering the intake manifold on the diesel engine parameters of nitrogen oxides (NOx) emission and brake specific fuel consumption (BSFC), four learning (ML) algorithms were adapted according to statistical evaluation criteria such as root mean squared error (RMSE), coefficient of determination (R2), mean squared error (MSE) and mean absolute error (MAE). The hyper parameters of the selected best model among four learning algorithms were determined by using grid search method. The results showed that the Gaussian process regression model (GPR) outperformed other ML models according to success error prediction of NOx and BSFC. Then, performance of the designed electromechanical EGR cooling system was analyzed under global driving conditions, the New European driving cycle (NEDC), and the world-wide harmonized light duty test procedure (WLTP). In these test cycles, global optimization was utilized with the GPR model as the objective function based on minimizing NOx and BSFC. Consequently, this study demonstrates the potential of the proposed system based on ML-GA to reduce NOx and BSFC by achieving reductions of 13.7%(NEDC)–9.98%(WLTP) and 2.61%(NEDC)–2.07%(WLTP) in NEDC and WLTP conditions, respectively compared to the conventional EGR cooling approach.

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