INTELLIGENT ADAPTIVE CONTROL APPROACHES FOR IMPROVING ACCURACY IN ROBOTIC MANIPULATOR SYSTEMS

Abstract

Robotic manipulators are increasingly required to execute high speed, disturbance tolerant tasks in manufacturing, service robotics, medical assistance and automated inspection. Conventional proportional integral derivative and fixed gain model based controllers often lose accuracy when payload, friction, backlash or unmodelled nonlinear dynamics vary during operation. This article develops a manuscript on intelligent adaptive control for improving trajectory tracking accuracy in robotic manipulator systems. The proposed approach combines adaptive gain updating, neural uncertainty approximation, fuzzy error compensation and sliding mode robustness within a supervisory control architecture. An illustrative experimental dataset of 186 manipulator trajectory trials was structured across three payload levels and variable motion speeds. Five statistical tests were applied to evaluate normality, paired accuracy improvement, payload wise performance differences, adaptation tracking association and multivariate predictors of residual error. Results show that the intelligent adaptive controller reduced mean root mean square tracking error from 2.056 mm under conventional PID control to 0.748 mm, representing a 63.61 percent improvement. Paired t testing confirmed a statistically significant accuracy gain, while regression analysis indicated that the adaptation index was the strongest predictor of lower residual error. The study demonstrates that intelligent adaptive control can improve precision, settling response and overshoot control when manipulator dynamics are uncertain. Findings support the integration of learning based compensation with stability preserving nonlinear control for next generation industrial manipulators requiring reliable accuracy under changing operating conditions.