Actuator Control Research Articles

Experiment and analysis of drop-weight damping control of MR semi-active impact resistance-based hydraulic actuator

The impact vibration caused by sudden changes in the external load of hydraulic actuators reduces the service life of hydraulic components and limits the high-precision applications of hydraulic actuators. Therefore, to improve the underdamped characteristics and impact resistance of valve-controlled cylinder systems, a semi-active impact resistance control of hydraulic actuator based on magnetorheological damper (MRD) is proposed. Firstly, based on the shear force characteristics of magnetorheological fluid, a multi-stage flow channel MRD is designed, which is connected in series with the hydraulic actuator to form a hydraulic damping actuator (HDA). Meanwhile, based on the mechanical experimental data, the parameters of MRD dynamic model are identified to ensure the accuracy of MRD output force. Secondly, a drop weight impact model is established, and a sliding mode controller which can track the command current in real time is designed, so as to realize the resistance force coupling between the MRD and the hydraulic actuator for improving the impact resistance control effect. Then the dynamic performance test and impact resistance simulation program are established to verify the excellent dynamic performance and impact resistance effect of the HDA with the sliding mode control. Finally, the drop weight impact experimental platform is built. The experimental results show that under the impact with a height of 200 mm, the dynamic offset of the HDA is only −1.25 mm, and the time to return to the original position is 0.08 s, which validate the excellent performance of the proposed scheme in improving the underdamped characteristics of the valve-controlled cylinder system and the dynamic response performance of the hydraulic actuator.

Research on Lateral Maneuverability of a Supercavitating Vehicle Based on RBFNN Adaptive Sliding Mode Control with Rolling Restriction and Planing Force Avoidance

This paper addresses the lateral motion control of a supercavitating vehicle and studies its ability to maneuver. According to the unique hydrodynamic characteristics of the supercavitating vehicle, highly coupled nonlinear 6-degree-of-freedom (DOF) dynamic and kinematic models are constructed considering time-delay effects. A control scheme utilizing radial basis function (RBF) neural-network-(NN)-based adaptive sliding with planing force avoidance is proposed to simultaneously control the longitudinal stability and lateral motion of the supercavitating vehicle in the presence of external ocean-induced disturbances. The online estimation of nonlinear disturbances is conducted in real time by the designed NN and compensated for the dynamic control laws. The adaptive laws of the NN weights and control parameters are introduced to improve the performance of the NN. The least squares method is utilized to solve the actuator control efforts with rolling restriction in real-time online. Rigorous theoretical proofs based on the Lyapunov theory prove the globally asymptotic stability of the proposed controller. Finally, numerical simulations were performed to obtain maximum maneuverability and verify the effectiveness and robustness of the proposed control scheme.

Experimental Investigation and Controllability Study of Electrochemical Actuators Based on Si/CNTs Composite Material

Abstract Electrochemical actuators can convert electrical energy into mechanical energy directly and have been applied widely. With a large volume expansion in the electrochemical reaction, silicon material demonstrates enormous potential in the manufacture of electrochemical actuators. Here, we propose a new electrochemical actuator based on Si/CNTs composite electrode. A mathematical model is developed to analyze the relationship among material parameters, structural changes, and bending deformation. The curvature changes of the cantilever beam are captured by a charge-coupled device (CCD) camera during electrochemical cycling. Combining the model and bending curvatures, the modulus and swell strain are extracted and analyzed in detail. Here, the elastic modulus of the composite electrode softens and decreases from 9.59 GPa to 4.78 GPa, while the swell strain increases from 0.12% to 2.97% when arriving 6% normalized concentration of lithium. These results show that the composite material possesses excellent bending resistance and deformation ability. Also, the curvature changes under different thickness ratios are predicted successfully, the evolution of stress in the working electrode is simulated, and the loading experiment of the actuator is carried out. This work provides a new way to realize the controllability of the electrochemical actuators.

Latent Representation-Based Learning Controller for Pneumatic and Hydraulic Dual Actuation of Pressure-Driven Soft Actuators.

The pneumatic and hydraulic dual actuation of pressure-driven soft actuators (PSAs) is promising because of their potential to develop novel practical soft robots and expand the range of soft robot applications. However, the physical characteristics of air and water are largely different, which makes it challenging to quickly adapt to a selected actuation method and achieve method-independent accurate control performance. Herein, we propose a novel LAtent Representation-based Feedforward Neural Network (LAR-FNN) for dual actuation. The LAR-FNN consists of an autoencoder (AE) and a feedforward neural network (FNN). The AE generates a latent representation of a PSA from a 30-s stairstep response. Subsequently, the FNN provides an individual inverse model of the target PSA and calculates feedforward control input by using the latent representation. The experimental results with PSAs demonstrate that the LAR-FNN can meet the requirements of dual actuation control (i.e., accurate control performance regardless of the actuation method with a short adaptation time) with a single neural network. The results suggest that a LAR-FNN can contribute to soft dual-actuation robot development and the field of soft robotics.

Selection/control concurrent optimization of BLDC motors for industrial robots.

This paper aims to concurrently select and control off-the-shelf BLDC motors of industrial robots by using a synergistic model-based approach. The BLDC motors are considered with trapezoidal back-emf, where the three-phase (a,b,c) dynamics of motors are modeled in a mechatronic powertrain model of the robot for the selection and control problem, defining it as a multi-objective dynamic optimization problem with static and dynamic constraints. Since the mechanical and electrical actuators' parameters modify the robot's performance, the selection process considers the actuators' parameters, their control input, operational limits, and the mechanical output to the transmission of the robot joints. Then, three objective functions are to be minimized, the motor's energy consumption, the tracking error, and the total weight of installed motors on the robot mechanism. The control parameterization approach via a cascade controller with PI controllers for actuators' voltage and a PID controller for actuators' torque is used to solve the multi-objective dynamic optimization problem. Based on simulations of the closed-loop system, a Pareto front is obtained to examine trade-offs among the objective functions before implementing any actuators in the existing robotic system. The proposed method is tested on an experimental platform to verify its effectiveness. The performance of an industrial robot with the actuators originally installed is compared with the results obtained by the synergic approach. The results of this comparison show that 10.85% of electrical power can be saved, and the trajectory tracking error improved up to 57.41% using the proposed methodology.

A novel ESO-based adaptive RISE control for asymptotic position tracking of electro-hydraulic actuator systems

This paper is focused on asymptotic tracking control of electro-hydraulic actuator (EHA) systems subject to matched and mismatched time-varying disturbances. To counteract the matched disturbance, a novel extended state observer (ESO) is proposed to achieve asymptotic convergence of the estimation error, by incorporating the strictly positive real (SPR) Lyapunov design method and a Nussbaum function. To further suppress the mismatched disturbance, an adaptive robust integral of the sign of the error (RISE) controller is formulated in the backstepping framework based on the proposed ESO. Asymptotic tracking performance is theoretically achieved via closed-loop system stability analysis. The efficacy of the proposed control scheme is verified through comparative experiments executed on an EHA test rig. In this study, a priori bounds of the disturbances and their higher-order derivatives are no longer needed, and only one auxiliary error signal is introduced. This approach loosens the restrictions on the disturbances and reduces the design conservativeness, thus making it promising in practice.

Modeling and control of the linear actuator for the lower limb rehabilitation device

In recent years, the number of stroke patients has increased, which has led to an increased need for motor rehabilitation exercises. The application of automation devices in the rehabilitation of patients is becoming increasingly developed. These devices help reduce the patient training time and workload. Each type of equipment uses different actuators such as electric motors, pneumatic cylinders, and hydraulic cylinders. Different from previous studies, in this paper, a research model of lower extremity rehabilitation devices using a simple actuator which is a linear actuator is presented. The main contribution of this study is that a dynamic model of the linear actuator associated with the action dynamics of the lower extremities of the studied model with three degrees of freedom was established. A mathematical model for this actuator is also presented, simulating the response of the mechanism using the MATLAB/Simulink software. A lower extremity rehabilitation device using a linear actuator was manufactured and tested. The test results for 10 volunteers show that the actuator works and causes miniature angle errors. This experiment demonstrates that the linear actuator can be used in stroke patient rehabilitation equipment and contributes to the diversification of the mechanism in automation equipment.

Constrained Dynamic Matrix Control under International Electrotechnical Commission Standard 61499 and the Open Platform Communications Unified Architecture.

This paper focuses on the implementation of a constrained Dynamic Matrix Control (DMC) approach within the level processes of the FESTO™ MPS-PA Compact Workstation plant in the context of the Industrial Internet of Things (IIoT) paradigm. The goal is to develop an industrial control application with decentralized logic that optimizes the operation of the plant while adhering to specific constraints. The implementation is carried out using the IEC-61499 standard and the OPC-UA protocol, enabling seamless communication between devices and systems. The authors utilize the 4diac-IDE and 4diac-FORTE as the development and runtime environments, respectively, to enable the execution of the control application on low-cost devices. The Beagle Bone Black (BBB) card is used for data acquisition and actuator control. Three types of constraints are considered: control increment (Δu(k)), output (ym(k)), and control (u(k)) constraints, to prevent unnecessary stress on the actuator and avoid damage to the plant. The QP algorithm is employed to optimize the objective function and address these constraints effectively. By integrating advanced control strategies into industrial processes in the IIoT paradigm and implementing them on low-cost devices, this paper demonstrates the feasibility and effectiveness of improving system performance, resource utilization, and overall productivity while considering system limitations and constraints.

Neural Network Adaptive Control of Magnetic Shape Memory Alloy Actuator With Time Delay Based on Composite NARMAX Model

Magnetic shape memory alloy (MSMA) based actuator plays an important role in the field of micro and nano-fabrication due to its large stroke. However, the inherent hysteresis of MSMA seriously affects the application prospects of MSMA-based actuator in the field of precision positioning. In this study, a composite model is proposed for hysteresis in the MSMA-based actuator by coupling an improved fractional-order Bouc-Wen model to a nonlinear auto-regressive moving average with exogenous inputs (NARMAX) model. Based on the proposed model, a neural network adaptive control method is then used to control the MSMA-based actuator, and the controller design takes into account the effect of time delay on the system performance to improve the positioning accuracy of the MSMA-based actuator. First, we fuse the composite NARMAX model with the canonical form of the nonlinear system to describe the MSMA-based actuator. Then, a control strategy is developed to address the impact of the unknown time delay for controller design and achieve the satisfactory positioning accuracy of the MSMA-based actuator. By using Lyapunov theory, the tracking error is proven to be asymptotic convergence. Experimental studies are provided to validate the effectiveness of the proposed modeling and control schemes.

Control of Linear Electric Actuator with Hybrid Fuzzy PID Controller

In this study, the actuator, which converts circular motion to linear motion, is controlled with a smart hybrid controller. The model includes the analysis of electrical and mechanical parts. The controller, on the other hand, has a design methodology that cleverly blends classical PID (Proportional Integral Derivative) and fuzzy controllers. In this design methodology, classical PID and fuzzy controller have an error parameter dependent blending mechanism. In order to compare the performance of the hybrid controller, the coefficients of the classical PI and PID controllers were obtained by tuning and the coefficients with the best response were used. For performance comparison of conventional controllers and hybrid controllers; rise time, settling time, maximum overshoot and common performance indices values are used. The hybrid controller showed higher performance in terms of maximum overshoot, settling time, rising time and performance indices. The potential benefits of using a hybrid controller for the control of linear electric actuators were demonstrated by the fact that it performed better than conventional PID controllers on the majority of evaluation criteria.

A Modified Model-Free Adaptive Control Method for Large-Scale Morphing Unmanned Vehicles

This paper investigates the attitude control problem for large-scale morphing unmanned vehicles. Considering the rapid time-varying and strong aerodynamic interference caused by large-scale morphing, a modified model-free control method utilizing only the system input and output is proposed. Firstly, a two-loop equivalent data model for the morphing unmanned vehicle is developed, which can better reflect the practical dynamics of morphing unmanned vehicles compared to the traditional compact form dynamic linearization data model. Based on the proposed data model, a modified model-free adaptive control (MMFAC) scheme is proposed, consisting of an external-loop and an inner-loop controller, so as to generate the required combined control torques. Additionally, in light of the aerodynamic uncertainties of the large-scale morphing unmanned vehicle, a rudder deflection actuator control scheme is designed by employing the model-free adaptive control approach. Finally, the boundedness of the closed-loop system and the convergence of tracking errors are guaranteed, based on the stability analysis. Additionally, numerical examples are presented to demonstrate the effectiveness and robustness of the proposed control scheme in the case of the effect of large-scale morphing.

Advanced polynomial trajectory design for high precision control of flexible servo positioning systems

This paper develops a new approach for the design of polynomial-based reference and feedforward control trajectories for the high precision control of flexible servo actuators. First, a robust frequency-domain controller is designed for reference tracking and disturbance rejection. This controller is designed to achieve a desirable loop shape with reasonable bandwidth and stability margins. Then, the polynomial trajectories are derived based on a simplified rigid body representation of the system. To avoid the excitation of the system's resonant modes, this paper proposes the design and implementation of a set of feedforward notch filters. We further investigate the main cause of tracking error resulted from the rigid body approximation, and propose an alternative model for the derivation of the polynomial input trajectories. This model accounts for the cumulative DC gain of the resonant modes ignored in the rigid body approximation. A method for deriving the new polynomial trajectories with the appropriate initial and final time conditions is developed and evaluated for a representative flexible servo system model. Simulation results indicate that the proposed scheme provides significant improvement in the performance of system compared to the conventional methods.

Passively addressed robotic morphing surface (PARMS) based on machine learning.

Reconfigurable morphing surfaces provide new opportunities for advanced human-machine interfaces and bio-inspired robotics. Morphing into arbitrary surfaces on demand requires a device with a sufficiently large number of actuators and an inverse control strategy. Developing compact, efficient control interfaces and algorithms is vital for broader adoption. In this work, we describe a passively addressed robotic morphing surface (PARMS) composed of matrix-arranged ionic actuators. To reduce the complexity of the physical control interface, we introduce passive matrix addressing. Matrix addressing allows the control of N2 independent actuators using only 2N control inputs, which is substantially lower than traditional direct addressing (N2 control inputs). Using machine learning with finite element simulations for training, our control algorithm enables real-time, high-precision forward and inverse control, allowing PARMS to dynamically morph into arbitrary achievable predefined surfaces on demand. These innovations may enable the future implementation of PARMS in wearables, haptics, and augmented reality/virtual reality.

New actuation planning method for the analysis and design of active tensegrity structures

This paper deals with actuation planning and control for active tensegrity structures. Within actuation planning, a specific actuation in the form of adjustments of cables’ rest lengths is assigned to the resulting structure to follow a prescribed complex trajectory. The methodology is based on the desired trajectory discretisation and following consecutive actuation optimisation, which employs the dynamic relaxation method. Since the actuation planning problem is treated as static, a controller is designed to suppress possible structural vibrations. With regard to the need to test dynamical properties and simulate the structure’s motion, a methodology for automatic creation of tensegrity computational models in Simscape software is developed in the MATLAB scripting environment. Numerical examples of particular tensegrity structures show the whole algorithm’s efficiency.

Integrated Modelling and Control of Linear Actuator Based Automatic Pedal Pressing Mechanism for Low-Speed Driving in a Road Traffic Delay

Sitting in traffic congestion for hours in a posture that requires recurrent actions of manually pressing the pedal and braking excessively can result in fatigue, especially on the driver's leg and back. This fatigue can have long-term implications and adversely affect the driver's health. Thus, this paper aims to model and develop a control system that utilizes a linear actuator to replace the leg activities involved in pressing and releasing the brake pedal. This approach, combined with the implementation of a PID controller, offers a novel solution to control the vehicle speed by integration with the linear actuator that focus on low-speed driving condition. The design process begins with creating a 3D model using SolidWorks to visualize the movement of the linear actuator and Pedal subsystem. This model is then connected to Matlab-Simulink, where a PID controller is implemented and integrated into the electrical circuit to control the actuator's movement. Integration with the vehicle dynamic model enables a comprehensive analysis of the system's behavior on the vehicle dynamics. This research compares the trial and error method with the Matlab tuner for implementing the PID controller. The performance of the system will be evaluated based on the steady state error, overshoot, rise time, and settling time. The results demonstrate that the Matlab tuner outperforms trial and error method by achieving a faster response and significantly reducing steady state error during robustness testing. With the integration of the linear actuator, the system is capable of tracking the desired speed and has the potential to replace the leg activities involved in pressing and releasing the brake pedal. For future work, validating the proposed mechanism with a physical prototype of the linear actuator and pedal using hardware-in-the-loop techniques poses a challenge, as hardware constraints may vary with different environments.

Non-contact actuated snap-through buckling of a pre-buckled bistable hard-magnetic elastica

Snap-through buckling of bistable structures is a classic topic in mechanics which has been widely studied and applied in various fields such as mechanical meta-materials and soft robotics. Obstacles that hinder broader applications of conventional bistable structures include the requirement of contact actuation to trigger instability and difficulty to control post-buckling configurations. In contrast, hard magnetic elastica (HME), a composite made of hard ferromagnetic particles and soft elastomer that deforms in response to an externally applied magnetic field, exhibits great potential to bring major advances in this field by allowing non-contact actuation and programmable control of snap-through buckling via magnetization distribution (M−distribution). Here, we develop a theoretical framework to trace the instability and post-buckling evolution process of snap-through buckling of a bistable HME. In contrast to the conventional snapping through end-end shortening, the design space for bistable HME includes two key parameters: the remanent magnetization density after pre-magnetization and the external magnetic field. We focus on two simple yet practical cases: a fixed amplitude of magnetization density along the HME with direction reversed at the magnetization interface (M−interface), and a uniform magnetic field with varied direction. We identify an optimal position for the single M−interface and direction for the uniform actuation field for pre-buckled beams with two-ends fixed, which can reduce the required actuation field for snapping to nearly half in comparison with the symmetric cases. Experiments and finite element analysis are performed to validate the model predictions. Our work may stimulate further studies on utilizing snap-through buckling in applications where fast and large shape transitions from one stable state to another can be actuated in a low-energy, non-contact mode through a remotely applied stimulus field.

High-precision dynamic torque control of high stiffness actuator for humanoids

The high stiffness actuator (HSA), applied to each joint of an electrical driven humanoid robot, can directly affect the motion performance of the torque-controlled humanoid robots. For high control performance of HSA, a high-precision dynamic torque control (HDTC) is proposed. The HDTC consists of two phases: (1) A novel dynamic current control is used to linearize high stiffness actuator torque control system, which can estimate and compensate the nonlinear coupling parts; (2) An enhanced internal model control is designed to ensure high tracking accuracy in the system containing noisy torque signal and even numerical differentiation signals. Benefitting from dynamic current control and the enhanced internal model control, the proposed HDTC is accurate and adaptable. Finally, the superiority of the HDTC is verified with comparative experiments.

Safe High Stiffness Impedance Control for Series Elastic Actuators using Collocated Position Feedback

Physical human-robot interaction applications requiring safety and compliance characteristics usually employ mechanical devices such as series elastic actuators (SEAs). So far, impedance control schemes for SEAs have been investigated to overcome the limited displayable stiffness at the end point, represented by the stiffness of a physical spring, in a passive manner. SEAs usually mount two encoders on both the motor and load sides for position measurements. However, in several applications, the installation of a load-side encoder is not feasible from both cost and manufacturing perspectives. In this scenario, a Kalman Filter based on micro electro mechanical system accelerometers has been proposed for estimating the external force and load side quantities, without the need for load-side encoders. Herein, a novel impedance control scheme leveraging this approach is proposed to effectively overcome the stiffness of the physical spring while maintaining passivity at the end point. Compared with conventional load-side encoder-based impedance control schemes, the proposed control scheme presents comparable performance, as demonstrated by simulation and experimental results.

Bending analysis of a soft pneumatic actuator using analytical and numerical methods

Soft bending actuators have applications in many areas where it is required to have compliant and safer interaction to deal with delicate objects. Soft bending actuators are being used in soft robots and in wearable medical devices for rehabilitation. Soft bending actuators are generally actuated using pneumatic air pressure because it provides cleaner, simple and safer operations while dealing with delicate objects and human. For efficient control of a soft pneumatic actuator (SPA), it is essential to have its mathematical model which directly affects the design criteria. The analytical modeling of such highly flexible and compliant SPA is challenging due to nonlinearity in the geometrical and material properties. This study focuses on the analytical modeling of a SPA for analyzing its bending behavior under application of the input pneumatic pressure. The estimated bending results of the analytical model are compared and verified with the numerical method i.e., finite element analysis (FEA). The bending results obtained from the analytical model showed high error when compared with the FEA results. Therefore, in this work, the analytical model is modified by using a linear correction term in the model. As a result of that, the error between the analytical and the numerical methods is significantly reduced.[copyright information to be updated in production process]

Requisites on material viscoelasticity for exceptional points in passive dynamical systems

Recent progress in non-Hermitian physics and the notion of exceptional point (EP) degeneracies in elastodynamics have led to the development of novel metamaterials for the control of elastic wave propagation, hypersensitive sensors, and actuators. The emergence of EPs in a parity-time symmetric system relies on judiciously engineered balanced gain and loss mechanisms. Creating gain requires complex circuits and amplification mechanisms, making engineering applications challenging. Here, we report strategies to achieve EPs in passive non-Hermitian elastodynamic systems with differential loss derived from viscoelastic materials. We compare different viscoelastic material models and show that the EP emerges only when the frequency-dependent loss-tangent of the viscoelastic material remains nearly constant in the frequency range of operation. This type of loss tangent occurs in materials that undergo stress-relaxation over a broad spectrum of relaxation times, for example, materials that follow the Kelvin–Voigt fractional derivative (KVFD) model. Using dynamic mechanical analysis, we show that a few common viscoelastic elastomers, such as polydimethylsiloxane and polyurethane rubber, follow the KVFD behavior such that the loss tangent becomes almost constant after a particular frequency. The material models we present and the demonstration of the potential of a widely available material system in creating EPs pave the way for developing non-Hermitian metamaterials with hypersensitivity to perturbations or enhanced emissivity.