Hybrid Control Strategies for High-Accuracy Robotic Manipulators

Advanced Control Strategies for Autonomous Engineering Systems

Autonomous engineering systems are a major technological advancement in modern industry and science, operating with minimal human intervention while ensuring high accuracy, reliability, adaptability, and efficiency. Before 2019, developments in control engineering, artificial intelligence, robotics, and embedded systems led to advanced autonomous control architectures. Applications such as intelligent transportation, industrial automation, aerospace systems, smart manufacturing, underwater exploration, and unmanned aerial vehicles increased the demand for advanced control strategies capable of handling nonlinearities, uncertainties, and environmental disturbances. Traditional methods like PID control were effective for linear systems but limited in dynamic environments. Therefore, advanced techniques such as adaptive control, robust control, model predictive control, fuzzy logic, neural network-based control, sliding mode control, and hybrid intelligent control became widely used. These methods improved stability, trajectory tracking, fault tolerance, and decision-making. This study reviews advanced control strategies for autonomous engineering systems up to 2019, covering theoretical concepts, mathematical models, architectures, and practical applications in robotics, aerospace, industrial automation, and intelligent transportation. It also highlights challenges including sensor uncertainty, communication delays, computational complexity, and environmental disturbances. Comparative analysis shows that advanced control methods outperform traditional techniques in accuracy, robustness, energy efficiency, and disturbance rejection. The study concludes that these strategies are the foundation of next-generation autonomous systems, with future research focusing on adaptive optimization, distributed intelligence, deep reinforcement learning, and collaborative robotics.

Evolution of control strategies for servo systems under nonlinearities and uncertainties: towards model-data fusion

Autonomous Robotic Arm Control Using Hybrid Kinematic Optimization

The autonomous robotic manipulators have become inevitable in the contemporary industrial automation, medical robotics, space exploration, and service robots. But it is an inherent challenge to have accurate, active and strong control of robotic arms under dynamic and uncertain conditions. Conventional control techniques utilizing only forward or inverse kinematics have drawbacks of singularities, local minima, sluggish convergence and lack of computational efficiency. In order to solve these problems, the current paper will cover a new autonomous robotic arm control system (ARCs) by relying on a Hybrid Kinematic Optimization(HKO) approach that combines analytical inverse kinematics, numerical optimization, intelligent constraint management. The suggested framework will integrate classical DenavitHartenberg (D-H) kinematic modeling with the use of the gradient-based and evolutionary optimization to produce the optimal joint trajectories in real-time. There is the introduction of a hybrid cost function that involves position accuracy, orientation error, joint smoothness, and energy efficiency. Collision avoidance and workspace constraints are also factored in the control architecture to be used to ensure safe and reliable operation. The hybrid optimizer is a dynamical algorithm that changes between fast analytical solvers and the global numerical optimizers based on the complexity of the task and the environmental conditions. Experiments involving simulation experiments on a 6-DOF model of industrial robotic arm on different task settings, such as pick-and-place settings, obstacle avoidance, and tracking in a trajectory are carried out. Convergence rate, tracking accuracy, joint torque efficiency and computational load are among the performance metrics considered and compared to the more traditional inverse kinematics and pure optimization-based methods. Findings indicate that the suggested hybrid structure attains a maximum speed of convergence is 35 percent, trajectory error is 28 percent and energy use is 22 percent. The Hybrid Kinematic Optimization framework presented provides a robust, scalable and intelligent framework applicable to solve the needs of next generation autonomous robotic manipulators to work within dynamic environments.

Intelligent robust control design for a 3-DoF industrial manipulator

Precise control of multi-degree-of-freedom (DoF) robotic manipulators is essential in industrial settings, where high tracking accuracy, fast convergence, and robustness to uncertainty are critical. However, many existing methods continue to suffer from high chattering, slow response, and limited adaptability to disturbances or abrupt input variations. This paper presents a control framework for a 3-DoF industrial manipulator that integrates nonsingular terminal sliding mode control (NTSMC), backstepping design, and a clustering-enhanced radial basis function neural network (RBFNN). The NTSMC component provides finite-time convergence and strong robustness to matched uncertainties, leading to superior tracking performance with a root mean squared error (RMSE) of 0.000202 under ideal conditions and 0.002634 under disturbances, both significantly better than those achieved by Fuzzy-sliding mode control (SMC) and SMC-neural network (NN) baselines. Backstepping enables smooth coordination of the control input across dynamic subsystems, which reduces settling time to 0.0375 seconds in the ideal case and 0.2808 seconds under disturbance. The clustering-enhanced RBFNN allows structured and efficient initialization of network parameters, identifying effective centers and widths in fewer than 500 iterations and improving generalization to both periodic and abrupt input signals. The combined design eliminates the common trade-off between chattering suppression and trajectory smoothness and demonstrates practical potential for real-time, high-precision control in uncertain industrial environments.

High Dimensional Modeling and Motion Control Technology for Soft Robotic

Soft robots made of low Young's modulus flexible materials have advantages over traditional rigid robots in terms of adaptability and safety in unstructured environments, and show great application potential in fields such as industrial grasping, medical assistance, and special operations. However, the inherent nonlinearity, viscoelasticity, and infinite degrees of freedom of soft robots make precise control extremely challenging, becoming a major obstacle to their practical application. This paper reviews the mainstream control strategies in the field of soft robotics, analyzes in detail the principles, advantages and limitations of four core control methods based on motion models, machine learning, morphological computation and sensor feedback, and point out the key technical difficulties such as underactuated system dynamics, nonlinear hysteresis effect and flexible sensor integration. The single control method is insufficient to fully address existing challenges. In the future, soft robot control will develop towards a hybrid strategy that integrates physical modeling accuracy, machine learning data processing capabilities, and passive adaptation through morphological computation.

Neural-Augmented Torque Control for Robotic Manipulators: Modeling, Learning, and Real-Time Performance

This thesis investigates advanced modeling and control strategies for robotic manipulators, focusing on the DLR-HIT II robotic hand and the KUKA LBR iiwa. It presents three core contributions that integrate simulation, model-based control, and data-driven methods to improve torque and position control under uncertainties and disturbances. First, a dual-platform simulation framework is developed using MATLAB Simscape Multibody and CoppeliaSim. The system accurately models the DLR-HIT II hand’s kinematics and dynamics, enabling both control validation and realistic interaction with virtual environments. The use of Unified Robot Description Format (URDF)-based modeling sup-ports reusability and modular analysis. Second, a physics-informed neural network (PINN) is proposed for direct torque and position control. This method uses only time and joint position inputs, internally computes derivatives, and generalizes well across various trajectory types. It eliminates the need for separate feedback controllers and shows strong robustness under disturbances, while maintaining low computational cost. Third, a Spike-Aware Hybrid Torque Control (SA-HTC) architecture is enhanced with a feedforward neural network (NN) trained offline. The network learns to improve Computed Torque Control (CTC) for unmodeled effects, friction, and external forces by refining the CTC torque output in real time. Simulation results across diverse trajectories and noise levels demonstrate that the SA-HTC method significantly improves tracking accuracy and robustness compared to classical CTC. To support deployment on position-controlled hardware, a physics-informed torque-to-position interface is introduced and compared with virtual stiffness and admittance wrappers, yielding lower steady-state bias, reduced phase lag, lower tracking error, and robust cross-trajectory generalization. Thus, these contributions advance the integration of learning-based and model-based control strategies in robotics. The results highlight scalable and efficient methods for accurate trajectory tracking and torque control, offering practical potential for robotic manipulation and automation applications.