Mechatronics最新文献

Quasi-zero stiffness (QZS) vibration isolators have attracted extensive attention because of their excellent performance in low-frequency vibration isolation and high bearing capacity. However, its practical engineering application remains challenges due to the magnitude and adaptability of the negative stiffness. This paper proposes a permanent magnet type variable stiffness (PMVS) mechanism with a wide stiffness range from negative to positive. Based on the semi-analytical model of magnetic force, the QZS isolator composed of the spiral spring in parallel with the PMVS mechanism is developed, which has both stiffness adjustment and load adaptability. The nonlinear dynamic equation with 5th polynomial restoring force is established then solved by the harmonic balance method (HBM). The dynamic behaviours is investigated and validated through the experimental prototype and good isolation performance is observed. Building upon the wide stiffness adjustment and large magnitude of the PMVS mechanism. the proposed isolator achieves a stiffness range of 4.15–153.45 N/mm under a safe load up to 120 kg, which implies that the natural frequency of the isolator can reach an ultralow value of 0.93 Hz within the safe load (120 kg).

In this study, we have undertaken the design and implementation of a pneumatic-driven translational pyramidal manipulator (PTPM) with the primary objective of achieving reliable and precise motion control, even under conditions where the PTPM may be exposed to disturbances arising from coupling effects and internal uncertainties. To achieve this purpose, the ALADRC, an adaptive controller that integrates a linear active disturbance rejection controller (LADRC) with a reduced-order linear extended state observer (RLESO) and a radial basis function neural network optimizer (RFNNO), is presented in this paper. This ALADRC has the following advantages in the view of practical applications: (1) it rejects total disturbances and coupling effect, (2) it reduces the order of the extended state observer, (3) it selects observer gains according to system frequencies, and (4) it optimizes controller gains in real time. Two trajectory tracking experiments and three disturbance rejection experiments were conducted to verify the trajectory tracking and disturbance compensation performance of the designed PTPM, respectively. The experimental results indicated that the PTPM controlled using the ALADRC increased the robustness in external disturbance rejection and provided accurate trajectory tracking.

This study introduces a novel robust adaptive repetitive learning controller (RARLC) designed for periodic tasks in vision-based robot systems. The controller eliminates the requirement for prior knowledge of the camera's intrinsic and extrinsic parameters, depth information of image features, and robot dynamic parameters. By using a depth-independent Jacobian matrix, the controller can estimate depth and unknown system parameters online. The key aspect of the approach is the fast learning ability from experience and outstanding robustness against noise and initial disturbance. This is accomplished by introducing two repetitive learning terms for the visual servo and nonlinear dynamic systems. Additionally, the method involves incorporating filtered error information and utilizes a projection mapping function to constrain estimated parameters within upper and lower bounds, thereby enhancing robustness against noise. The experimental and simulation results demonstrate that the robot's tracking errors significantly decrease after only three periods, indicating excellent performance of the controller in terms of convergence speed and servoing precision. Finally, we compared the developed controller with other controllers and examined the effectiveness of control gains in the context of tracking accuracy and robustness to image noise using simulation techniques.

It is a huge challenge for autonomous underwater vehicles (AUVs) to determine heading autonomously without the signal of satellite or underwater acoustic positioning system. The underwater polarization navigation is a newly-developed solution for self-orientation. However, the polarization navigation depending on the Rayleigh scattering model is susceptible to the multiple scattering induced by water depth. To address this issue, the underwater light intensity field is exploited benefiting from its environment suitability. By means of calculating the gradient of light intensity, a vector-field model, with resemblance to the ideal Rayleigh model in geometry, can be established for solar-tracking. The integrated navigation model is built based on the solar vector estimated by light intensity gradient (LIG) and inertial navigation system for heading determination. The static test in water tank and dynamic sea trial were conducted to validate the effectiveness of the LIG-based solar-tracking and orientation method, respectively.

Accurate positioning of underwater targets is an important basis for many underwater tasks. As an imagery equipment, forward-looking sonar (FLS) can overcome the problem of poor brightness in underwater environment, and has a longer detection distance than the camera, which plays an important role in oceanic target positioning. However, there remain two challenging problems: (1) due to the complex underwater environment and the propagation characteristics of sound waves in the water, there are a lot of noises in the FLS images, which impair the accuracy of positioning; (2) FLS can only obtain two-dimensional information, which is insufficient for target positioning. Aiming at these two problems, a three-dimensional underwater target positioning algorithm based on dual FLSs is proposed in this paper. Firstly, a Threshold-Mask-Guided Filter (TMGF) image denoising algorithm is designed to deal with the obvious background noises and same-pitch noises in FLS images, which greatly improves the denoising effect compared with the original guided filter algorithm. Secondly, a target positioning algorithm based on orthogonal vertical placement of dual sonars is proposed to calculate the three-dimensional position of the target. Finally, pool experiments and sea trial tests are carried out respectively to verify the effectiveness of TMGF algorithm in denoising and the accuracy and stability of positioning algorithm.

This paper proposes a novel method to improve the robustness of particle filtering (PF) in relatively flat terrain that lack sufficient features for accurate positioning. In terrain -aided navigation (TAN), similar terrain profiles lead to multimodality in the posterior distribution. It not only reduces the accuracy of current position estimation, but also affects the subsequent estimation of PF, and even causes filter divergence. When no additional information is available, the historical estimation is employed to correct ambiguous updates caused by multimodal posteriors. First, a strategy for identifying ambiguous updates is proposed by analyzing the particle set distribution using the clustering method and covariance. Inspired by out-of-sequence measurement (OOSM), once the ambiguous updates are detected, the ambiguous estimates are corrected by introducing high-quality previous information. Moreover, an efficient solution is provided for the storage and computation requirements of OOSM within the PF framework. To verify the effectiveness of the proposed algorithm, simulation and experimental validation are designed. By comparing with PF, mixture particle filtering (MPF), and OOSMPF algorithms, the proposed algorithm demonstrates better estimation accuracy and robustness in terrain flat areas.

Lower limb exoskeletons have garnered significant attention for their effectiveness in gait training for paraplegic patients. For patients with insufficient trunk or upper limb strength to maintain balance, employing an unpowered robotic walker with an exoskeleton for gait training is an effective approach. The energy efficiency of the actuation system is a pivotal consideration in the design of the exoskeleton–walker system due to its significant influence on the system’s durability and service efficiency. The main contribution of this paper is the development of a Parallel Compliant Leg (PCL) for the exoskeleton–walker system. The PCL consists of both powered legs of the exoskeleton and a passive flexible mechanism within the walker. This integration allows for the storage and release of energy during cyclic walking, resulting in reduced system energy consumption. To enhance energy efficiency, the support force optimization of the flexible mechanism is established. Based on this optimization, a design scheme and parameter optimization for the flexible mechanism are proposed. The effectiveness of the proposed flexible mechanism is verified through simulations on a robot simulation platform. Experimental results demonstrate a remarkable 67.6% reduction in system energy consumption achieved by the optimized mechanism.

In this work, the primary design constraints of a magnetorheological (MR) actuator and its stroke dimension have been found based on biomechanical requirements and anthropometric constraints of the ankle in a transtibial prosthesis. Based on the inverted slider-crank mechanism models, the force controller parameters of the MR damper are identified. Parameters of the MR dampers are evaluated through optimization that minimises the error between the prosthetic ankle moment and the desired ankle moment for normal level ground walking from experimental data. Furthermore, an artificial neural network (ANN) framework for the MR valve is developed where a three-layered ANN model has been utilised to forecast the magnetic flux density (MFD) across different regions of the MR valve. The data have been generated from an ANSYS-APDL software package using finite element magnetostatic analysis (FEMS). The ANN model outcomes match the FEMS results reasonably well. Finally, the ANN model is employed to find MFD and is used to optimize the MR valve. Optimal solutions are obtained that satisfy the goal function of maximising the damper force and minimising the energy consumption and weight of the MR damper. Subsequently, the optimized MR damper has been fabricated and tested experimentally and it has been found to produce enough force to act as an actuator in a prosthetic ankle.

To enhance the effectiveness of active safety control, the tire–road friction coefficient (TRFC) must be precisely estimated, and the longitudinal tire stiffness coefficient is an important vehicle dynamic parameter to estimate TRFC. In this research, we present an observer that improves the performance of longitudinal tire stiffness coefficient estimation by applying tire dynamics that were previously applied in the lateral direction to the longitudinal direction. To begin, we model longitudinal tire dynamics using the relaxation length concept and validate the model using vehicle braking tests. We develop an observer that estimates the longitudinal tire stiffness coefficient by integrating the proposed tire dynamics and vehicle dynamics. The observer, which is based on an extended Kalman filter, can be applied to nonlinear systems and successfully removes noise from wheel speed measurement. The observer’s estimation performance is verified using CarSim simulation and vehicle tests, and the results are compared to existing approaches that do not account for longitudinal tire dynamics. Even in the transient section when the vehicle begins accelerating, the difference between the estimate and the reference value is about 0.3% using the proposed method, but if tire dynamics are not taken into account, the estimate is 6.5% lower than the reference value.

This paper presents a new method to increase odometric sensor accuracy by systematic and non-systematic errors processing. Mobile robot localization is improved combining this technique with a filter that fuses the information from several sensors characterized by their covariance. The process focuses on calculating the odometric speed difference with respect to the filter to implement an error type detection module in real time. The correction of systematic errors consists in an online parameter adjustment using the previous information and conditioned by the filter accuracy. This data is also applied to design a variable odometric covariance which describes the sensor reliability and determines the influence of both errors on the robot localization. The method is implemented in a low-cost autonomous wheelchair with a LIDAR, IMU and encoders fused by an UKF algorithm. The experimental results prove that the estimated poses are closer to the real ones than using other well-known previous methods.