Search Results (87)

To address the safety concerns of incapacitated patients caused by changes in vehicle pose during the operation of an autonomous rescue vehicle on an unstructured road surface, this paper proposes an active leveling control scheme based on the Stewart platform. First, a complete kinematic and dynamic model of the Stewart platform and a double-layer platform leveling control model were established. Subsequently, a non-singular terminal sliding-mode control (NTSMC) algorithm based on a radial basis function (RBF) neural network was designed. By using the neural network to approximate aggregate uncertainties online, high-precision control of the Stewart platform was achieved. Additionally, to enhance perception capabilities in dynamic environments, an ORB-SLAM3 algorithm was proposed that integrates the YOLO11n-Seg instance segmentation algorithm. This approach effectively filters out dynamic feature points, enabling robust vehicle pose estimation. Finally, a physical double-layer Stewart platform experimental system was constructed to comprehensively validate the proposed control and vision algorithms. Full article

The Stewart–Gough Platform (SGP) is a spatial parallel manipulator offering high accuracy, rigidity, and adaptability, with applications spanning medical systems, marine engineering, agriculture, manufacturing, entertainment, aerospace, and architectural installations. This paper presents a comparative analytical and computational study of three SGP configurations: the regular SGP, with regular hexagonal base and top platforms; the Irregular-Parallel SGP, derived from the regular SGP by a novel graphical decomposition-and-modification procedure and characterized by similar symmetric hexagonal platforms with limbs preserved parallel; and the Irregular-Skewed SGP, in which the irregular hexagonal platforms of the Irregular-Parallel SGP are retained, but the limbs are connected in an inclined, alternating clockwise (or anticlockwise) topology. The Irregular–Skewed SGP is free from the constraint singularity that persists in the first two configurations and requires the shortest maximum actuator stroke. Static force analysis shows that the regular SGP and the Irregular–Parallel SGP both exhibit a rank-deficient rigidity matrix (rank = 3) across the geometric scaling range tested (radius ratios 1:2 to 1:10; inter-platform distances 100–1000 mm), whereas the Irregular-Skewed SGP achieves full rank (rank = 6) through inclined limb connectivity and is the only configuration capable of sustaining static equilibrium under the loading conditions examined. The forward kinematics of the Irregular-Parallel SGP is verified against a SolidWorks model: under a 9 mm uniform limb extension, the MATLAB and SolidWorks positions of node 7 agree to within 1.27 mm. The rotational workspace volume is equivalent across the three configurations, but the density of valid solution points within that workspace differs. The workspace within joint limits, alternating compression–tension force partition, and asymmetric stroke economy of the Irregular-Skewed SGP indicate applicability to kinetic facades and transformable interiors in architectural-robotics deployment. Full article

Offshore wind turbine operation and maintenance in complex sea states is influenced by the coupled effects of low-frequency vessel drift and high-frequency wave-induced disturbances. In practical operations, the ship dynamic positioning system primarily regulates low-frequency motion through vessel position control, whereas a boarding compensation system is required to attenuate high-frequency six-degrees-of-freedom motions to ensure safe personnel transfer. This study establishes coupled kinematic mapping among the ship dynamic positioning system, the Stewart platform, and a three-degrees-of-freedom gangway and proposes a hierarchical cooperative control architecture. At the upper layer, an extended Kalman filter and an exponential moving average low-pass filter are employed for online state estimation and for separating low-frequency and high-frequency components. A Kalman filter lookahead predictor is then used to generate a short-horizon prediction of the high-frequency component and to construct a feedforward reference signal. At the middle layer, the feedforward reference and the gangway end error feedback are coordinated at the velocity level, and a quadratic programming-based allocation strategy distributes compensation tasks between the Stewart platform and the gangway under safety-related constraints, including actuator stroke limits and singularity avoidance. At the lower layer, a robust feedback controller is designed for the gangway to mitigate modeling uncertainties and environmental disturbances and to ensure stable tracking. MATLAB R2024a-based simulations under representative wave conditions demonstrate that the proposed architecture improves end effector tracking accuracy and closed-loop stability compared with baseline strategies, providing a feasible engineering solution for shipboard boarding operations in complex sea states. Full article

This paper presents a novel hybrid learning-based control scheme for position control of robot manipulators whose structure is based on a closed-kinematic-chain mechanism (CKCM). The developed control scheme integrates two complementary control components: the feedback controller and the learning controller. The feedback controller is designed using linearization about a desired trajectory and a PID control law whose gains are selected by a tuning algorithm to guarantee semi-global stability of the linearized closed-loop feedback system. The learning controller incorporates PID-type iterative learning strategy to generate additional control inputs to compensate for modeling uncertainties and unmodeled dynamics. By updating the control input iteratively from trial to trial, the learning controller progressively improves the overall control performance. The effectiveness of the developed control scheme is demonstrated through computer simulations conducted on a six-degree-of-freedom CKCM robot manipulator. Simulation results are presented and discussed to evaluate the tracking accuracy of the developed approach. Full article

The discrete-form control of the Stewart platform is essential for digital implementation in intelligent manufacturing and robotic systems under the context of Industry 4.0, yet its performance is often degraded by unavoidable discrete disturbances. This challenge motivates the development of algorithms with strong disturbance suppression capability. To address this issue, a continuous-form double-integration-enhanced recurrent neural network (CF-DIE-RNN) algorithm incorporating a novel double-integration-enhanced design concept is first developed to improve robustness against time-varying disturbances. For digital hardware applications, a discrete-form double-integration-enhanced RNN (DF-DIE-RNN) algorithm is then constructed by discretizing the CF-DIE-RNN algorithm using a general four-step discretization formula and a one-step forward difference formula based on Taylor expansion. Rigorous theoretical analysis establishes the convergence properties of the proposed algorithm and characterizes its steady-state residual bounds under different disturbance types, revealing its capability to suppress discrete quadratic time-varying disturbances. Numerical and simulation experiments demonstrate that the DF-DIE-RNN algorithm achieves superior disturbance suppression and more accurate trajectory tracking than existing discrete-form RNN algorithms, confirming its effectiveness for discrete-form Stewart platform control. Full article

Offshore operational environments are inherently stochastic, with waves, currents, and wind loads exerting a significant influence on vessel attitude and equipment stability. While Stewart platforms enable active motion compensation, conventional control strategies frequently suffer from time delays, actuator lag, and limited disturbance rejection, resulting in inadequate performance under complex sea conditions. To overcome these limitations, this paper presents a wave compensation control strategy for a Stewart platform that integrates deep learning-based prediction with active disturbance rejection control (ADRC). A bidirectional long short-term memory (BiLSTM) network is developed to predict vessel attitude in advance. The predicted attitude is transformed into actuator displacement commands through the inverse kinematics of the Stewart platform. An ADRC-based displacement controller is then designed to achieve fast and robust compensation under wave disturbances. Six-degree-of-freedom (6-DOF) dynamic models of a catamaran and a Stewart platform are established in Simulink and Simscape, and sea states 2, 4, and 6 are simulated using an enhanced Joint North Sea Wave Project (JONSWAP) wave spectrum. The simulation results show that, compared with Proportional–Integral–Derivative (PID) and ADRC methods, the proposed BiLSTM-ADRC strategy reduces the roll root mean squared error (RMSE) by 76.6% and 73.2%, and pitch RMSE by 64.1% and 58.1%, respectively, demonstrating an improved attitude stabilization performance. Full article

Parameter estimation plays an important role in improving the accuracy, control, and diagnostic performance of mechanisms, particularly in parallel mechanisms such as the Stewart platform, which are increasingly used in high-precision automation, advanced manufacturing, and machine-centric applications. This paper presents a multibody–based framework for generalized dynamic modeling and inertial parameter estimation of parallel robotic manipulators, demonstrated on the DeltaLab-SMT EX800 Stewart platform. A systematic constrained multibody dynamic formulation is developed using an iterative kinematic–dynamic coupling scheme to compute generalized coordinates and their time derivatives under prescribed motion trajectories. The proposed identification manifold is experimentally validated on the physical test rig, in which the platform motion is executed via the control/DAQ system, while inertial measurements are acquired using an external 6-axis motion sensor to obtain direct acceleration data from the moving platform. Platform acceleration measurements are mapped through the inverse dynamics of the multibody model to derive the corresponding generalized forces, providing a practical and cost-effective alternative to direct force measurement with transducers. A Kalman filter is subsequently employed to combine the measured and the model-predicted data, yielding optimally filtered estimates of the inertial coordinates for accurate parameter identification. Inertial parameters are estimated using particle swarm optimization and bench marked against a gradient-based Levenberg–Marquardt approach, with comparison in terms of convergence behavior, robustness, and estimation accuracy. The results support the proposed framework as a measurement-informed benchmark methodology for parameter estimation of parallel manipulators. Full article

This paper presents the experimental validation of a computational kinematic model for a passive Stewart–Gough platform equipped with modified Cardan joints. The introduced joint geometry significantly increases structural stiffness but invalidates the standard spherical joint assumption commonly used in hexapod kinematic formulations. To address this, we develop an efficient numerical optimization-based framework that solves both the direct and inverse kinematics without relying on simplified joint models. Furthermore, to enable autonomous and absolute pose measurement, each cylindrical leg joint of the platform is equipped with a LinACE™ absolute linear encoder. This sensor integration transforms the passive mechanism into an IoT-enabled reconfigurable fixture capable of internally sensing, tracking, and recalling its own configuration. The direct kinematics are computed iteratively using a homogeneous transformation formulation and benchmarked against analytical models derived for ideal spherical joints. Experimental results demonstrate sub-millimeter accuracy in pose estimation, confirming the validity of the proposed kinematic model and highlighting the suitability of the sensor-equipped hexapod for industrial flexible fixturing applications. Full article

Driven by global initiatives to mitigate climate change, the offshore wind power industry is experiencing rapid growth. Personnel transfer between service operation vessels (SOVs) and offshore wind turbines under complex sea conditions remains a critical factor governing the safety and efficiency of operation and maintenance (O&M) activities. This study establishes a fully coupled dynamic response and control simulation framework for an SOV equipped with an active motion-compensated gangway. A numerical model of the SOV is first developed using potential flow theory and frequency-domain multi-body hydrodynamics to predict realistic vessel motions, which serve as excitation inputs to a co-simulation environment (MATLAB/Simulink coupled with MSC Adams) representing the Stewart platform-based gangway. To address system nonlinearity and coupling, a composite control strategy integrating velocity and dynamic feedforward with three-loop PID feedback is proposed. Simulation results demonstrate that the composite strategy achieves an average disturbance isolation degree of 21.81 dB, significantly outperforming traditional PID control. Validation is conducted using a ship motion simulation platform and a combined wind–wave basin with a 1:10 scaled prototype. Experimental results confirm high compensation accuracy, with heave variation maintained within 1.6 cm and a relative error between simulation and experiment of approximately 18.2%. These findings demonstrate the framework’s capability to ensure safe personnel transfer by effectively isolating complex vessel motions and validate the reliability of the coupled dynamic model for offshore operational forecasting. Full article

Motion cueing algorithm (MCA) aims to reproduce the dynamic motion experience of real vehicles for users of driving simulators. Under rough or irregular road conditions, vehicles are subjected to severe shocks and vibrations. However, due to the inherent response delay and limited capability of motion platforms in reproducing high-frequency components, conventional MCA often suffers from slow response and poor tracking accuracy. This mismatch leads to dynamic inconsistency between the visual feedback and the motion cues provided to the driver, which can easily induce discomfort or even aggravate simulator sickness. To address these issues, this study proposes a route-preview MCA based on adaptive model predictive control (RPAMPC). A CNN–LSTM-based vehicle trajectory prediction model is developed by integrating convolutional and recurrent neural networks to exploit forward terrain information. Subsequently, a motion cueing prediction model incorporating actuator stroke and velocity states is formulated, and an AMPC-based MCA is designed to optimize the simulator platform motion under physical constraints. Experimental results on a Stewart motion simulation platform demonstrate that, compared with traditional MCA, the proposed algorithm achieves higher-quality motion cues and significantly reduces sensory errors under complex road conditions. Full article

The structural parameters of Stewart platforms play a critical role in enhancing dynamic performance, improving motion accuracy, and enabling effective control strategies. However, practical applications face several key limitations, including the metric balancing for optimization, the limited singularity-free workspace, and low computational efficiency. To overcome those shortcomings, this work proposes a multi-objective optimal design of the structural parameters for Stewart platform based on Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Kolmogorov–Arnold Network (KAN). Firstly, under the stroke constraints of the Stewart platform, this work focuses on optimizing the platform’s key structural parameters. This approach enables both the optimization of existing equipment and the design of new devices. Secondly, this work employs KAN to establish a model that characterizes the relationship between the structural parameters and diverse postures within the maximum singularity-free workspace. This approach not only enhances computational efficiency but also ensures high precision. Finally, this study proposes six performance metrics and utilizes NSGA-III to optimize the structural parameters, thereby achieving a trade-off among these diverse objectives. Simulation and experimental results demonstrate that KAN significantly outperforms the Multi-Layer Perceptron (MLP) in predicting workspace postures. Compared with MLP, KAN achieves higher prediction accuracy and lower error rates across both training and test datasets. When comparing NSGA-III with NSGA-II, the proposed approach demonstrates modest improvements in most performance metrics while preserving acceptable trade-offs between the optimization objectives. Full article

The strong coupling of the six-degree-of-freedom (6-DoF) electro-hydraulic Stewart parallel mechanism manifests as adjusting the elongation of one actuator potentially inducing motion in multiple degrees of freedom of the platform, i.e., a change in pose; this pose change leads to time-varying and unbalanced load forces (disturbance inputs) on the six hydraulic actuators; unbalanced load forces exacerbate the time-varying nature of the acceleration and velocity of the six hydraulic actuators, causing instantaneous changes in the pressure and flow rate of the electro-hydraulic system, thereby enhancing the pressure–flow nonlinearity of the hydraulic actuators. Considering the advantage of artificial intelligence in learning hidden patterns within complex environments (strong coupling and strong nonlinearity), this paper proposes a reinforcement learning motion control algorithm based on deep deterministic policy gradient (DDPG). Firstly, the static/dynamic coordinate system transformation matrix of the electro-hydraulic Stewart parallel mechanism is established, and the inverse kinematic model and inverse dynamic model are derived. Secondly, a DDPG algorithm framework incorporating an Actor–Critic network structure is constructed, designing the agent’s state observation space, action space, and a position-error-based reward function, while employing experience replay and target network mechanisms to optimize the training process. Finally, a simulation model is built on the MATLAB 2024b platform, applying variable-amplitude variable-frequency sinusoidal input signals to all 6 degrees of freedom for dynamic characteristic analysis and performance evaluation under the strong coupling and strong nonlinear operating conditions of the electro-hydraulic Stewart parallel mechanism; the DDPG agent dynamically adjusts the proportional, integral, and derivative gains of six PID controllers through interactive trial-and-error learning. Simulation results indicate that compared to the traditional PID control algorithm, the DDPG-PID control algorithm significantly improves the tracking accuracy of all six hydraulic cylinders, with the maximum position error reduced by over 40.00%, achieving high-precision tracking control of variable-amplitude variable-frequency trajectories in all 6 degrees of freedom for the electro-hydraulic Stewart parallel mechanism. Full article

In recent years, with the rapid development of the unmanned aerial vehicle industry, aviation optoelectronic turrets have been widely applied in fields such as terrain exploration, disaster prevention and mitigation, and national defense. Vibration isolation systems play a critical role in ensuring their imaging performance. This paper proposes a novel eight-leg six-degree-of-freedom (6-DOF) passive vibration isolation system tailored to the characteristics of aviation optoelectronic turrets, addressing the limitations of traditional Stewart passive vibration isolation platforms. A static analysis of the system is conducted, deriving the general form of the mass matrix under application conditions for aviation optoelectronic turrets. Structural configuration conditions are established to ensure that the stiffness matrix and damping matrix are diagonal matrices. In dynamic analysis and simulations, the transmissibility in each direction is simulated, and the impact of leg failure on the vibration isolation performance of this redundant system is further investigated. Under random vibration excitation, the maximum rotational vibration angles of a specific aviation optoelectronic turret are simulated and analyzed, confirming its stable tracking capability and validating the effectiveness of the redundant leg design in the vibration isolation system. Full article

This paper presents the results of real-time experiments conducted to evaluate the performance of a developed adaptive control scheme applied to control the motion of a real closed-kinematic chain mechanism (CKCM) robot manipulator with two degrees of freedom (DOFs). The developed control scheme, referred to as the decentralized adaptive synchronized control scheme (DASCS), was the result of the combination of model reference adaptive control (MRAC) based on the Lyapunov direct method and the synchronization technique. CKCM manipulators were considered in the experimental study due to their advantages over their open-kinematic chain mechanism (OKCM) manipulator counterparts, such as higher stiffness, better stability, and greater payload. The conducted computer simulation study showed that the DASCS was able to asymptotically converge tracking errors to zero, with all the active joints moving synchronously in a prescribed way. One of the important properties of the DASCS is the independence of robot manipulator dynamics, making it computationally efficient and therefore suitable for real-time applications. The present paper reports findings from experiments in which the DASCS was applied to control the above manipulator and carry out various paths. The DASCS’s performance was compared with that of a traditional adaptive control scheme, namely the SMRACS, when both schemes were applied to track the same paths. Full article

This paper addresses the trajectory tracking control problem of a six-degree-of-freedom Gough–Stewart Platform (GSP) by proposing a control strategy that combines a sliding mode (SM) controller with a rapid forward kinematics solution algorithm. The study first develops an efficient forward kinematics method that integrates Support Vector Regression (SVR) with the Levenberg–Marquardt algorithm, effectively resolving issues related to multiple solutions and local optima encountered in traditional iterative approaches. Subsequently, a SM controller based on the GSP’s dynamic model is designed to achieve high-precision trajectory tracking. The proposed control strategy’s robustness and effectiveness are validated through simulation experiments, demonstrating superior performance in the presence of model discrepancies and external disturbances. Comparative analysis with traditional PD controllers and linear SM controllers shows that the proposed method offers significant advantages in both tracking accuracy and control response speed. This research provides a novel solution for high-precision control in GSP applications. Full article