A cart inverted pendulum is an under actuated system that highly unstable and nonlinear. Therefore, it makes a good problem example which attracts control engineers to validate the developed control algorithms. In this paper, an augmented PID control algorithm is proposed to stabilise a cart inverted pendulum at the desired state. The derivation of a mathematical model of the cart inverted pendulum using Lagrange's equation is discussed in detail. The system dynamics is illustrated to understand better the behaviour of the system. A simulation program has been developed to verify the performance of the proposed control algorithm. The system dynamic behaviours with respect to the variation of the controller parameters are analysed and discussed. Controllers parameters are expressed into two PID gain sets which associated with 2 dynamic states: the cart position (ϰ) and the pendulum angle (θ). It can be concluded from the simulation result that the proposed control algorithm can perform well where acceptable steady errors can be achieved. The best response from the cart inverted pendulum system has been obtained with the value of kPX 190, kDX 50, kIX 5, kPθ 140, kDθ 5, and kIθ 25.

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The system of a cart inverted pendulum has many problems such as  nonlinearity, complexity, unstable, and underactuated system. It makes this system be a benchmark for testing many control algorithm. This paper  presents a comparison between 2 conventional control methods consist of a linear quadratic regulator (LQR) and pole placement. The comparison  indicated by the most optimal steps and results in the system performance  that obtained from each method for stabilizing a cart inverted pendulum system. A mathematical model of DC motor and mechanical transmission are included in a mathematical model to minimize the realtime implementation problem. From the simulation, the obtained system performance shows that each method has its advantages, and the desired pendulum angle and cart position reached.

A Cart Inverted Pendulum System is an unstable, nonlinear and underactuated system. This makes a cart inverted pendulum system used as a benchmark for testing many control method. A cart must occupy the desired position and the angle of the pendulum must be in an equilibrium point. System modeling of a cart inverted pendulum is important for controlling this system, but modeling using assumptions from state-feedback control is not completely valid. To minimize unmeasured state variables, state estimators need to be designed. In this paper, the state estimator is designed to complete the state-feedback control to control the cart inverted pendulum system. The mathematical model of the cart inverted pendulum system is obtained by using the Lagrange equation which is then changed in the state space form. Mathematical models of motors and mechanical transmissions are also included in the cart inverted pendulum system modeling so that it can reduce errors in a real-time application. The state gain control parameter is obtained by selecting the weighting matrix in the Linear Quadratic Regulator (LQR) method, then added with the Leuenberger observer gain that obtained by the pole placement method on the state estimator. Simulation is done to determine the system performance. The simulation results show that the proposed method can stabilize the cart inverted pendulum system on the cart position and the desired pendulum angle.

This paper presents the nonlinearity compensation of low-frequency loudspeaker response. The loudspeaker is dedicated to measuring the response of Electret Condenser Microphone which operated in the arterial pulse region. The nonlinearity of loudspeaker has several problems which cause the nonlinearity behaviour consists of the back electromagnetic field, spring, mass of cone and inductance. Nonlinearity compensation is done using the Internal Model Controller with voltage feedback linearization. Several signal tests consist of step, impulse and sine wave signal are examined on different frequencies to validate the effectiveness of the design. The result showed that the Internal Mode Controller can achieve the high-speed response with a small error value.

The Segway Human Transport (HT) robot, it is dynamical self-balancing robot type. The stability control is an important thing for the Segway robot. It is an indisputable fact that Segway robot is a natural instability framework robot. The case study of the Segway robot focuses on running balance control systems. The roll, pitch, and yaw balance of this robot are obtained by estimating the Kalman Filter with a combination of the pole placement and the Linear Quadratic Regulator (LQR) control method. In our system configuration, the mathematical model of the robot will be proved by Matlab Simulink by modelling of the stabilizing control system of all state variable input. Furthermore, the implementation of this system modelled to the real-time test of the Segway robot. The expected result is by substitute the known parameters from Gyro, Accelero and both rotary encoder to initial stabilize control function, the system will respond to the zero input curve. The coordinate units of displacement response and inclination response pictures are the same. As our expected, the response of the system can reach the zero point position.

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Ketersediaan bahan bakar fosil berbanding terbalik dengan pertumbuhan jumlah kendaraan bermotor. Penggunaan kendaraan bermotor yang menggunakan bahan bakar fosil seperti minyak juga menyebabkan peningkatan polisi udara oleh emisi gas buangnya. Oleh karena itu, banyak dilakukan pengembangan alat transportasi yang ramah lingkungan. Sepeda motor listrik menjadi salah satu alat transportasi yang banyak dikembangkan. Dengan menggunakan motor Brushless Direct Current (BLDC), sepeda motor listrik ini memiliki efisiensi yang tinggi dalam mengubah energi listrik menjadi energi mekanik. Penelitian ini difokuskan pada kontrol kecepatan sepeda motor listrik dengan Field Oriented Control, dimana algoritma ini dapat mengendalikan motor BLDC dengan mengontrol torsi dan flux secara terpisah sehingga diharapkan efisiensi motor akan lebih baik. Hasil penelitian menunjukkan bahwa kecepatan motor dapat dikontrol dengan akurasi lebih dari 99 persen.