This paper proposes a fuzzy LQR PID control for a two-legged wheeled balancing robot for keeping stability against uncertainties and variant heights. The proposed control includes the fuzzy supervisor, LQR, PID, and two calibrations. The fuzzy LQR is conducted to control the stability and motion of the robot while its posture changes with respect to time. The fuzzy supervisor is used to adjust the LQR control according to the robotic height. It consists of one input and one output. The input and output have three membership functions, respectively, to three postures of the robot. The PID control is used to control the posture of the robot. The first calibration is used to compensate for the bias value of the tilting angle when the robot changes its posture. The second calibration is applied to compute the robotic height according to the hip angle. In order to verify the effectiveness of the proposed control, a practical robot with the variant height is constructed, and the proposed control is embedded in the control board. Finally, two experiments are also conducted to verify the balancing and moving ability of the robot with the variant posture.

This paper presents a synchronous proportional derivative (PD) control method using a time delay estimator (SPD-TDE) for a four-degree-of-freedom (DOF) parallel robot in practice. The proposed control is a method that is developed from a synchronous PD control method combined with a time delay estimator to guarantee the tracking objectives and synchronous requirements of the robot. Firstly, the synchronous PD control method is designed by defining cross-coupling errors. A cross-coupling error is determined by incorporating the tracking error and deviation of tracking error among two adjacent joints or synchronous errors. Then, the asynchronous problem between the kinematic chains is solved and guarantees that the goal of synchronicity is achieved. Consequently, to improve the tracking performance of the robot, a time delay estimator is used to estimate and eliminate the uncertainty components of the system, such as modeling errors and actuator faults. In addition, the Lyapunov theory is also used to demonstrate the stability and robustness of the proposed control method. Finally, a testbench 4-DOF parallel robot is built, and the controllers are embedded in the control board from MATLAB Simulink using the Waijung block set library to operate the robot preset trajectory tracking. The experimental results of the proposed control method for the 4-DOF parallel robot are compared with those obtained using other controllers to prove its effectiveness.

This paper presents a proportional-derivative (PD) control strategy based on time delay estimation (TDE) for an upper limb rehabilitation robotic arm. The proposed controller combines trajectory tracking and the approximation of nonlinear system components, including system dynamics, lumped uncertainties, external disturbances, and friction. For trajectory tracking, a PD controller is employed to regulate the robot joints, ensuring they follow predefined trajectories in the joint space. Meanwhile, the nonlinear components are approximated using TDE, which relies on past values of the control torque and joint angular acceleration signals. These estimated components are subsequently compensated within the control law. The integration of PD control with TDE enhances tracking accuracy and improves the system’s robustness. Moreover, the stability of the proposed method is theoretically proven. The effectiveness of the approach is validated through MATLAB Simulink simulations using a Simscape 3D model derived from SolidWorks, as well as through experimental results on a self-developed upper limb rehabilitation robotic arm.

This paper proposes a synchronous sliding mode control (SSMC) method for a 4-DOF parallel robot model in practice with the influence of uncertain dynamics such as friction, external noise, and model error. A closed kinematic chain structure characterizes the parallel robot. The moving platform is controlled by four kinematic chains working together to follow a predetermined trajectory. However, traditional control methods only control each joint individually; if one of the actuators fails or has an external force, it can lead to system breakdown. Therefore, synchronous control to coordinate the operation of the arms is necessary. The synchronization algorithm is characterized by the cross-coupling error, which is the combination of tracking and synchronization errors for the system to achieve the synchronization goal. Cross-coupling error is used to design the sliding surface of the SSMC. The advantages of sliding mode control (SMC) are that it resists the influence of uncertain dynamics and it is combined with the synchronization algorithm. A control law is developed based on the sliding surface to ensure simultaneous convergence of both tracking and synchronization errors towards zero. The stability of the system is proven using the Lyapunov theory. In order to verify the effectiveness of the proposed approach, a 4-DOF parallel robot testbench is constructed. The SSMC controller results are compared with the testbench’s proportional-integral-derivative (PID), synchronous proportional-integral-derivative (SPID), and SMC controllers.

This paper presents an enhanced teleoperation system and visual-force feedback with obstacle avoidance for a Car-like mobile robot. The proposed system includes a local station, a remote station, and a communication channel. The local station allows the operator to give acceleration, orientation, and driving mode commands. It generates the haptic effect of the obstacles in the remote station for the operator due to the visual-force feedback. The remote station is a Car-like mobile robot executing control commands from the local station and providing feedback on the working status of the robot. Moreover, the robot has the ability of obstacle avoidance through the Potential Field (PF) algorithm with input signals being the distance from the robot to obstacles and a virtual repulsive force that influences both the steering angle of the robot and the haptic steering wheel system. The communication channel will connect the local station and the remote station wirelessly based on Wide Area Network (WAN) architecture with the Message Queuing Telemetry Transport (MQTT) to resolve complex problems such as control distance, latency, etc. Several case studies are used to evaluate the efficacy of providing the operator with haptic and visual feedback at any control distance.