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In our study, we compared classical genetic algorithm (GA) and non-dominated sorting GA (NSGA) II to optimize LQR and sliding mode control (SMC) for an inverted pendulum. We aimed to identify the algorithm that best improves stability and peak performance. Our results clarified each algorithm’s unique strengths. The provement is shown in Matlab simulations.

This paper surveys the Linear Quadratic Estimation (LQE) and Model Predictive Control (MPC) discrete control methods applied to the Acrobot system. Both techniques aim to achieve and maintain a balanced position for the Acrobot. Through evaluation and comparison, we highlight their strengths, limitations, and potential applications, offering insights for future robotic control research.

Ball and plate system (BPS) is known as a nonlinear multi-input multi-output (MIMO) system that has been usually used to investigate and develop control algorithms. In this paper the study deals with a 3-RRS parallel manipulator which has a metal ball, three DC motors with linkage mechanism to a fixed base that can be a resistive touch screen . The moving base has two rotational movements, it can be rotated around the x-axis and y-axis and translated along the z-axis. The main purpose of this study is obtaining dynamics equations of motions and designing PD and LQR controllers. Through simulation and experiment, both control algorithms are proved to work well on this model.

In this paper, we apply genetic algorithm (GA) to optimize LQR controller – a linear control algorithm which stability is guaranteed by mathematics. This searching algorithm proves its ability in finding better control parameters through generations. Our model is ball and beam (B&B) – a classical single input – multi output (SIMO) system. This system is balanced around equilibrium point in simulation.

Manuscript provides a key technology, namely Input-Output Feedback Linearization Associates with Linear Quadratic Regulator (for short, IOFLALQR). The objective of this research is to study the possibility of integrating two control strategies, which includes input-output feedback linearization technique (for short, IOFL) and linear quadratic regulator controller (for short, LQR), for stabilization control of Furuta pendulum system. Furuta pendulum system belongs to the group of under-actuated robot systems. In this work, structure of IOFLALQR, control implementation, comparison of IOFLALQR and conventional LQR are adequately studied and discussed. Simulation is completed in MATLAB/Simulink environment and experiment is done on real-time experimental setup. Numerical simulation and experimental results show that the IOFLALQR are implemented on Furuta pendulum successfully. Besides, results have been drawn for demonstrating IOFLALQR better than another classical method.