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The usage of the Electro-Hydraulic Actuator (EHA) system in industrial machinery has increasingly attracted public attention nowadays by providing a highly stable output performance due to its incompressible hydraulic fluid. However, the accuracy and positioning control of the hydraulic cylinder are always become issues to engineers or researchers that utilized that actuator. In this paper, a suitable control strategy with a combination of a controller and optimization technique is proposed to control the EHA system. Three types of control strategies are proposed and studied in this paper which are PSO-PID, PSO-SMC, and MOPSO-SMC. This study is conducted through a simulation environment using MATLAB/Simulink with an established EHA system model. This paper focuses on three types of simulation studies which are tracking point analysis, mass variation simulation and pressure variation simulation. The overshoot percentage and steady-state error are used to evaluate and compare the effectiveness of the controller to the EHA system. The simulation results show that the EHA system controlled by MOPSO-SMC having the best output performance with the least overshoot percentage and steady-state error.

In this paper, the Continuous Motion (CM) NCTF controller is proposed for a point-to-point (PTP) positioning system of a one mass rotary system. For this system, the CM-NCTF controller is designed based on the conventional structure of NCTF controller and improved. The CM-NCTF controller consists of Nominal Characteristic Trajectory (NCT) and Proportional Integral (PI) compensator. It is designed without knowing exact system modeling and parameter evaluation. The NCT is constructed from open loop responses while the PI compensator is designed based on the constructed-NCT itself. The effectiveness of the CM NCTF controller is evaluated and compared with the PID controller for positioning and tracking motion performances experimentally. The robustness of the CM NCTF controller is validated by varying the mass load. Overall, the CM-NCTF controller demonstrates promising positioning and tracking performances, and low sensitivity towards the change of mass load in comparison with the PID controller.

This paper discusses an experimental setup (mechanical testing) & modeling result of pneumatic muscle actuator. In recent times, the Pneumatic Muscle Actuator (PMA) has achieved popularity in the field of research, robotics and industrial application due to its advantages such as light weight actuator, dynamic, powerful and resistant to dust and clean. Due to its nonlinear dynamics, the muscle actuator is difficult to be controlled. In this paper a pneumatic muscle actuator system is developed for motion control purpose. The dynamic system modeling is discussed. Open loop test and static load experiment have been conducted and validated with the mathematical model. Finally, a brief conclusion of the pneumatic muscle actuator experimental setup and dynamic system modeling is made.

This paper presents vertical motion control of a one-legged hopping robot using Central Pattern Generator (CPG). The hopping dynamic of the hopping robot is produced by controlling the motor speed to produce force through the crank to the mass, spring, and damper. The classical controllers; PI and PID controller are designed to control the vertical motion of a one legged hopping robot. The parameters for each PI and PID controller are determined by using Ziegler-Nichols method. However, the outputs produced by the controllers do not reach the desired input. Therefore, a CPG model is designed to integrate with these controllers in order to control the hopping robot. The CPG implementation in a mass-spring-damper model is extended to investigate on its dynamic mechanism and vertical motion during hopping. All of integration controller performances are analyzed based on the dynamic response of the hopping robot to reach the desired height. Statistical method is used to analyze the dynamic response. The statistical features that analyzed are mean, mod, error, minimum and maximum boundary of the hopping height produced. As a result, the integration CPG with PI controller is the best controller for the designed one legged hopping robot in term of the mean of the hopping height and error in achieving the desired height.

This paper discusses about the development of output feedback Sliding Mode Control (SMC) and demonstrates that this controller is able to reject disturbance in a nonlinear system. The designed SMC controller works well to ensure system stability and eliminates disturbance in linear and nonlinear dynamics systems. The approach to develop output feedback controller is by implementing High Gain Observer and the output performance from this controller is compared with the output response from the state feedback SMC. Both designed controllers are constructed in simulation by using MATLAB SIMULINK tool. The required gains involve in the designed control laws are tuned heuristically by trial and error approach. The state response results obtained from the simulations are analyzed. Based on the observation and analysis to the results it shows that the output feedback SMC illustrates smooth state response and reduces the effect of disturbance as provided by the state feedback (SMC) without disturbance.

This project develops an algorithm to optimize the dynamic performance of an industrial robot with aid of vision system technology. It consists of three major phases; (1) theoretical, simulation and practical of forward and inverse kinematics for Fanuc LR Mate 200iB robot to determine their D-H parameters, (2) optimization of robot movements, and (3) implementation of practical tasks. The camera is used as a vision sensor to incorporate an intelligent algorithm for the recognition of coordinates of target objects' and to guide the robot to perform certain tasks. The optimization process involves the control of two parameters known as position (Theta) and speed (omega) of the 3 main axes of the robot. Its forward and inverse kinematics problems will be simulated using Robotic tools of Matlab 7.0 and Roboguide V2.3.2. The simulation results will be compared with the real practical movements. This paper will describe the technique, calculation and method on how the first phase was done.