This paper presents the dynamic behaviour of a nonlinear single link inverted pendulum-on-cart system based on Lagrange Equation. The nonlinear model linearization was presented based on Taylor series approximation. LQR, double-PID and simple pole placement control techniques were proposed for upright stabilization and tracking controls of the system. Simulations results for the various control techniques subjected to a unity magnitude pulse input torque with and without disturbance were compared. The performances of the proposed controllers were investigated based on response time specifications and level of disturbance rejection. Thus, the performance of LQR is more reliable and satisfactory. Finally, future work suggestions were made.

A chatter mark is a result of irregular vibration that affects the milling process, which results in poor surface finish, reduced work quality, machine impairment, and high production cost. This work presents an active feedback controller design using a new response matrix to suppress the free vibration in the milling process. The proposed controller considers feed rate, tooth passing frequency, and time-varying dynamic milling force coefficients. A milling experiment verifies the effect of the proposed method. The method provides a reliable way of tackling chatter vibration in an industrial process. The procedure is technically and economically beneficial.

Effective control of the generator’s shaft speed will ensure maximum power is captured from the wind turbines. However, the parameters of the wind energy conversion system (WECS)-based generators, including stator resistance and inductance, could change over time due to power loss, winding degradation, or core saturation. These parametric uncertainties affect the performance of the designed controllers. Although the sliding mode controllers (SMCs) are robust to matched uncertainties, the unmatched parametric uncertainties were not effectively compensated for by the SMC. This study investigated the performance of the SMC and super-twisting SMC (ST-SMC) under unmatched uncertainties using variable wind speed. Initially, the controllers were designed with the nominal parameters of the WECS-based permanent magnet synchronous generator (PMSG). Then, the values of the stator resistance and inductance were changed without changing the control design to test the robustness of the controllers to unmatched uncertainties. Finally, the uncertainties were estimated by the disturbance observer and incorporated into the controllers as a compensation mechanism. The controllers were designed using the synthetic wind profile and validated with the real-wind data. The transient and overall response of the controllers were analyzed using mean-absolute error (MAE) and root-mean-square error (RMSE) of the shaft speed tracking. The results demonstrated that the uncertainty compensation-based SMC/ST-SMC approach provides satisfactory shaft speed tracking performance even under parametric uncertainties.

The overhead crane is required to operate fast and precisely with minimal sway. However, high-speed operations cause undesirable load sways, hazardous to operating personnel, the payload being handled, and the crane itself. Thus, a high-quality control is required. In this work, the nonlinear model of the overhead crane was established and the sliding mode control (SMC) was proposed that ensured the existence of sliding motion in the presence of payload and hoisting height variations, and viscous frictions. To maximize the benefits derived from the proposed control method, novel sliding slope-update based on intelligent neural-network and fuzzy algorithms were developed to tune the controller, guaranteeing precise tracking of the actuated variables as well as regulation of the unactuated variables. The proposed methods adjust predetermined value of the sliding manifold’s slope in response to variations in hoisting heights. Control applications were conducted, and results based on graphical, integral absolute error (IAE), and integral time absolute error (ITAE) proved the effectiveness of the proposed algorithms. It was observed that the response of the controller with back-propagation-trained neural-network was more effective relative to that of the fuzzy algorithm.