This study presents a comprehensive evaluation of linear and non-linear control systems, specifically Proportion Integration Differentiation (PID) and fuzzy logic controllers, in the context of position control within double-link robotic manipulators. The effectiveness of these controllers was rigorously assessed in a simulated environment, utilizing MATLAB Simulink for the simulation and SOLIDWORKS for the model design. The PID controller, characterized by its Kp, Ki, and Kd components, was implemented both in the simulation and on the hardware. However, due to the constraints of the microcontroller's RAM and processor, which facilitate the hardware's connection with MATLAB, the application of the Fuzzy Logic concept to hardware was not feasible. In the simulated environment, the fuzzy logic controller demonstrated superior stability in comparison to the PID controller, evidenced by a lower settling time (1.0 seconds) and overshoot (2%). In contrast, the PID controller exhibited a settling time of 0.2 seconds and an overshoot of 32%. Additionally, the fuzzy logic controller showcased a 44% reduction in steady-state error relative to the PID controller. When applied to hardware, the PID controller maintained stable results, achieving a settling time of 0.6 seconds and an overshoot of 2%. The steady-state errors for Link 1 and Link 2 were recorded as 3.6° and 1.4°, respectively. The findings highlight the fuzzy logic controller's enhanced stability, rendering it more suitable for ensuring the accuracy and protection of the manipulator system. As a non-linear controller, the fuzzy logic controller efficiently addresses various potential errors through its intelligent control mechanism, which is embedded in its fuzzy rules. Conversely, the PID controller, a linear controller, responds rapidly but may lack flexibility in complex scenarios due to its inherent linearity. This study underscores the importance of selecting an appropriate controller based on the specific requirements of robotic manipulator systems, with a focus on achieving optimal performance and stability.
This study explores dynamic simulation and integrated control in a space robotic arm system characterized by a fully-flexible arm and an elastic base. The elastic base is modeled as a lightweight spring, and the modal shapes of a simply-supported beam are selected via the assumed mode method to represent the bending vibrations of the flexible arm. Dynamic equations for the system are formulated by integrating Lagrangian mechanics with momentum conservation principles. The approach involves reducing the system into two lower-order subsystems using a dual-time-scale singular perturbation method. The first subsystem, exhibiting slow variation, accounts for the joint's rigid motion, while the second, fast-varying subsystem addresses the vibrations of the base and arm. Estimation of joint velocities is conducted through a Luenberger observer, complemented by the use of an Radial Basis Function (RBF) neural network to approximate parameter uncertainties within the system. This facilitates the control of rigid motion in the slow-varying subsystem. Subsequently, the fast-varying subsystem's vibration is actively controlled based on linear system optimal control theory. Numerical simulations validate the integrated control approach's effectiveness in managing both motion and vibration, demonstrating its potential in enhancing the operational precision and stability of space robot systems.
LLC resonant converters own high power efficiency and density, and are widely used in electric vehicles, intelligent and communication power sources, and other fields. The converters cannot obtain accurate mathematical models and their nonlinear characteristics are complex. Therefore, traditional proportional-integral (PI) control cannot achieve control effect well. The dynamic matrix control (DMC) strategy was applied to the converter model, aiming to improve the system’s dynamic response and reduce overshoot. In addition, the DMC algorithm was used in this study to achieve precise system control. The algorithm is robust, and can improve the system’s stability and reliability. At the same time, the system can be flexibly controlled through parameter adjustment. Furthermore, a voltage prediction closed-loop controller was designed to enhance the system’s dynamic performance. In addition, a simulation model was built based on this to verify the feasibility and effectiveness of the scheme. The simulation results showed that the DMC algorithm suppressed overshoot and improved dynamic response effectively.
In the field of industrial motor control, the inherent design complexity and operational challenge of dual star induction motor (DSIM) have made it a focus of research for many scholars. This study attempts to innovatively propose a refined control approach for DSIM, by deploying two pulse width modulation (PWM) voltage sources combining with indirect field-oriented control (IFOC). Core of our innovation is the integration of a super twisting algorithm (STA) controller, which is a strategy specifically designed to enhance the motor's speed control capability. The paper introduced the technical details of DSIM, with the focus placed on the distinctive configuration of two isolated neutral three-phase windings, set apart by a 30-degree electrical phase shift. Such design has posed certain control challenges, and the STA approach has skillfully addressed these challenges. With the help of Matlab/Simulink simulations, the efficacy of STA controller is evaluated and compared with the common Proportional-Integral (PI) controller, and the simulation results are indicative of the STA controller's superiority, showing a significant improvement in reducing torque ripples and stator current fluctuations. The analysis given in the paper quantifies the improvement, showing substantial reductions in steady-state error and response time, as well as an enhanced disturbance rejection capability. These findings are instrumental in showcasing the STA controller's comparative advantage. Concludingly, the adoption of the STA-based control methodology in DSIM applications not only fosters enhanced speed control and efficiency but also holds the promise of broad applicability across various industrial scenarios. This research, therefore, marks a pivotal advancement in the field of DSIM control, potentially revolutionizing its application in diverse industrial settings. The consistency in the use of professional terminology throughout the paper ensures a coherent and comprehensive understanding of the subject matter.
This study presents the two-stage cubature Kalman filter (TSCKF), which is a sophisticated technique designed to address the issue of variations in system models in real-life scenarios, and utilises nonlinear two-stage transformations to reorganise covariance matrices into a block-diagonal structure, effectively overcoming the limitations of conventional augmented methods. This technique effectively eliminates the need to calculate the cross-covariance between state variables and biases. This leads to a substantial reduction in computational load and facilitates seamless operation of the filter. The TSCKF design is underpinned by a robust theoretical framework, which ensures optimal computational efficiency while also ensuring precise estimations. This work demonstrates the mathematical equivalence between the TSCKF and the standard cubature Kalman filter (CKF) by utilising updated information equivalent transformations, and empirically verifies the equivalence through trajectory tracking experiments conducted on two-wheeled robotic systems subjected to random perturbations, thus affirming the greater accuracy and dependability of the TSCKF in tracking scenarios. Moreover, comparison evaluations offer further proof of the same performance between both methodologies. This study introduces a highly efficient approach in the domain of nonlinear systems and provides a dependable remedy for scenarios where traditional filtering procedures may be inadequate due to deficiencies in the system model.
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