Addie Irawan

This paper addresses the challenge of maintaining steering precision and yaw stability in rack steering vehicles (RSVs) operating under varying inertial loads and aerodynamic disturbances. Conventional control methods often struggle to ensure bounded tracking performance in the presence of actuator saturation and nonlinear dynamics. To overcome these limitations, a Finite-Time Prescribed Performance Anti-Windup PI Controller with Nonlinear Error Transformation (FPPC-API-NET) is proposed. The outer loop employs a finite-time prescribed performance framework with a nonlinear transformation to constrain the error trajectory within predefined decay envelopes, while the inner loop incorporates antiwindup compensation to mitigate actuator saturation effects. Simulation studies benchmarked against the FTPPC-API and API controllers demonstrate that FPPC-API-NET achieves up to a 95% reduction in steady-state error, a 70% reduction in settling time, and a 87% reduction in transient duration. The proposed method also enhances yaw stability, reduces lateral displacement by 35%, minimizes sideslip magnitude, and maintains tire-friction utilization below saturation, resulting in smoother lateral acceleration with up to 48% lower peaks. These results confirm that FPPC-API-NET offers superior robustness and precision during cornering maneuvers across varying inertia levels. ABSTRAK: Kajian ini membincangkan cabaran dalam mengekalkan ketepatan kawalan stereng dan kestabilan sudut gerakan sisi bagi kenderaan jenis rak stering (RSV) yang beroperasi di bawah beban inersia berubah serta gangguan aerodinamik. Kaedah kawalan konvensional sering menghadapi kesukaran mengekalkan prestasi penjejakan apabila berlaku ketepuan aktuator dan kesan dinamik tak linear. Bagi mengatasi kekurangan ini, satu pengawal tirus berasaskan Finite-Time Prescribed Performance dengan Transformasi Ralat Tak Linear dan Antiwindup PI (FPPC-API-NET) telah dicadangkan. Gelung luar menggunakan rangka kerja prestasi terpiawai had masa dengan transformasi ralat tak linear bagi mengehadkan trajektori ralat dalam sempadan peluruhan yang ditetapkan, manakala gelung dalam aplikasi pampasan antiwindup bagi mengawal kesan ketepuan aktuator. Kajian simulasi berbanding pengawal FTPPC-API dan API menunjukkan bahawa FPPC-API-NET mencapai pengurangan ralat keadaan tunak sehingga 95%, masa penetapan 70% lebih pantas, dan tempoh peralihan 87% lebih singkat. Kaedah ini turut meningkatkan kestabilan gerakan sisi, mengurangkan anjakan lateral sebanyak 35%, meminimumkan gelinciran, serta mengekalkan penggunaan geseran tayar di bawah had ketepuan, sekali gus membuktikan keteguhan dan ketepatan unggul FPPC-API-NET dalam manuver membelok di bawah variasi inersia.

This paper presents the control signal rectification and actuation mapping (CSRAM) framework, developed to improve the reliability and precision of real-time robot joint control. The framework integrates three modules, namely the drive signal rectifier (DSR), the signal pole detector (SPD), and the rising/downstream detector (RDD), which ensure signal compatibility, dynamic mapping consistency, and directional stability during actuation. Unlike conventional control converters, CSRAM effectively compensates for nonlinearities, latency, and synchronization issues in closed-loop systems. Experimental validation using a hexapod-to-quadruped (Hexaquad) robot showed that the proposed method, when combined with an anti-windup PI controller, reduced steady-state error from 14% to below 1%, improved transient and settling times by 0.3 to 0.4 seconds, and decreased three-dimensional trajectory RMSE by 63.7%. These results confirm that CSRAM provides a low-complexity and computationally efficient preprocessing layer for improving real-time performance in multi-joint and legged robotic systems, with strong potential for adaptive and industrial robotic platforms.

This paper presents an optimal Adaptive Convergence Prescribed Performance control cascaded with Anti-Windup PI (ACPPC-API) controller for steering-position control of rack steering vehicles (RSV) operating on cornering paths, optimized using the proposed Enhanced Evolutionary Mating Algorithm Lite (EMAPlus). The ACPPC framework regulates steering-error evolution through dynamically shaped convergence envelopes, while the Anti-Windup PI (AW-PI) inner loop stabilizes actuator behavior under saturation constraints. EMAPlus is employed to jointly tune the ACPPC and AW-PI parameters, enabling fast, stable, and computationally efficient optimization compared with the original Evolutionary Mating Algorithm (EMA), Ant Lion Optimizer (ALO), and Grasshopper Optimization Algorithm (GOA). Simulation results demonstrate that the EMAPlus-tuned ACPPC–API controller achieves the highest steering-tracking accuracy, reducing overshoot by up to 64%, suppressing residual ripple to within ±0.02 radians, and improving settling time by 20%–35% relative to the benchmark optimizers. These performance gains translate into superior vehicle-level responses, including 30%–60% lower curvature-tracking error, 45%–65% smaller sideslip deviation, and smoother lateral–yaw coordination during cornering maneuvers. Actuator-level and ride-quality indicators further reveal 35%–60% reductions in peak road-wheel rate and lateral jerk. Energy analysis confirms that more than 80% of the lateral–yaw kinetic energy is effectively directed into productive lateral-velocity motion with a shortened transient duration. The results establish the EMAPlus-optimized ACPPC–API controller as an efficient and robust steering solution for high-precision RSV cornering applications.