Position Control Of Actuator Research Articles

Online Task Switching and Scheduling Method for Attitude–Orbit–Shape-Distributed Control of Large Space Ring Structures

The attitude, orbit, and shape control actuators of large space structures on orbits are functional overlapping and task coupled, resulting in actuator redundancy and high-energy consumption. This paper explores the integrated attitude–orbit–shape control problem of large space ring structures and proposes an online task switching and scheduling method for multiple actuators of space structures (OTSS-MASS). An adaptive allocation framework is proposed for the task switching of distributed actuators. The objectives of attitude–orbit–shape control are decomposed into attitude adjustment, orbit change, and shape maintenance tasks through a task decomposition algorithm. Task value estimation models are designed to guide a greedy task allocation of actuators. The adaptive allocation framework achieves online task reconfiguration for multiple distributed actuators, thereby enhancing the maneuverability of large space structures, improving actuator work efficiency, and reducing energy consumption in orbit. Simulation results demonstrate that OTSS-MASS outperforms the conventional actuator distribution methods. The overall effectiveness is improved by over 24% in terms of the comprehensive index, time index, trajectory index, and shape index.

Novel bio-inspired soft actuators for upper-limb exoskeletons: design, fabrication and feasibility study.

Soft robots have been increasingly utilized as sophisticated tools in physical rehabilitation, particularly for assisting patients with neuromotor impairments. However, many soft robotics for rehabilitation applications are characterized by limitations such as slow response times, restricted range of motion, and low output force. There are also limited studies on the precise position and force control of wearable soft actuators. Furthermore, not many studies articulate how bellow-structured actuator designs quantitatively contribute to the robots' capability. This study introduces a paradigm of upper limb soft actuator design. This paradigm comprises two actuators: the Lobster-Inspired Silicone Pneumatic Robot (LISPER) for the elbow and the Scallop-Shaped Pneumatic Robot (SCASPER) for the shoulder. LISPER is characterized by higher bandwidth, increased output force/torque, and high linearity. SCASPER is characterized by high output force/torque and simplified fabrication processes. Comprehensive analytical models that describe the relationship between pressure, bending angles, and output force for both actuators were presented so the geometric configuration of the actuators can be set to modify the range of motion and output forces. The preliminary test on a dummy arm is conducted to test the capability of the actuators.

Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system

Combined Energy and Attitude Control System (CEACS) reduces the size and mass budgets of typical satellites and consequently, increases their payload capacity. CEACS uses flywheels for a dual purpose, i.e., as both energy storage and attitude control device. This maiden work attempts to introduce a novel Deep-Learning capability of the fuzzy-Model Predictive Control (FMPC) controller for CEACS. The design approach for the fuzzy-MPC controller uses the Takagi-Sugeno (T-S) fuzzy model of satellite attitudes and computes the control torque through a parallel distribution compensation (PDC) approach. However, the MPC controller offers a high computational burden, and it becomes a significant problem for smaller satellites having limited computational power. Therefore, in this research work, a novel Deep-Learning-based fuzzy-MPC controller (D-FMPC) is designed for the CEACS attitude regulation subject to higher initial angles, actuator constraints, parametric uncertainties, and external disturbance torques. Here, the deep-layer neural network is trained offline with the MPC controller data to replicate the FMPC controller, thus ensuring its controllability. Numerical results validate that the D-FMPC controller successfully mimics the FMPC controller and produces the desired pointing accuracy effectively with smooth transient response and without violating the attitude control actuator constraints. The results also validate that the D-FMPC controller offers significantly reduced computational burden than the FMPC controller. Therefore, the novel Deep-Learning solution provides a feasible platform for applying more complicated and sophisticated attitude control techniques for the CEACS attitude regulation in small satellites as an example.

Development of a Ferrofluid-Based Attitude Control Actuator for Verification on the ISS

Ferrofluid-based systems provide an opportunity for increasing the durability and reliability of systems, where mechanical parts are prone to wear and tear. Conventional reaction control systems are based on mechanically mounted rotating disks. Due to inherent friction, they suffer from degradation, which may eventually lead to failure. This problem is further intensified due to the limited possibility for repair and maintenance. Ferrofluid-based systems aim to replace mechanical components by exploiting ferrofluidic suspended motion. Ferrofluids consist of magnetic nanoparticles suspended in a carrier fluid and can be manipulated by external magnetic fields. This paper describes the working principle, design, and integration of a working prototype of a ferrofluid-based attitude control system (ACS), called Ferrowheel. It is based on a stator of a brushless DC motor in combination with a rotor on a ferrofluidic bearing. The prototype will be verified in a microgravity environment on the International Space Station, as part of the Überflieger 2 student competition of the German Aerospace Center. First ground tests deliver positive results and confirm the practicability of such a system.

Satellite actuator balancing based on the disturbance measurement table data

Reaction wheel is one of primary actuators for satellite attitude control. Since it uses electrical power supplied by the solar panels and thus consumes no invaluable fuel of satellite, it has been used as primary actuator. However, rotation of reaction wheel induces torque and force disturbances which degrade the satellite attitude stability quality. The disturbances are mainly due to static and dynamic imbalances of the wheel. These imbalances shall be reduced on the stage of manufacturing. In this his paper a method for the imbalance reduction of the wheel is suggested. Torque/force measurement table is used to measure the disturbances of the reaction wheel and the measured data are used for the identification of location and magnitude of the imbalances. The corrections of the imbalances are performed by adding proper counter masses on the wheel. Through iterative balancing steps, the static and dynamic imbalances are remarkably reduced.

Analysis on the Dynamic Behavior of Space Shafting under Combined Load

The space shafting is the core component of the momentum exchange attitude control actuator for spacecraft.The dynamic behavior of space shafting has an important impact on the performance of the actuators. Based on the dynamic theory of rolling bearing, this paper presents a dynamic analysis model of space shafting for the interaction between bearing balls and oil-containing nonmetallic cage under combined loads. Also, the accuracy of the analysis model was verified through a high-speed camera system to conduct a cage speed test. In addition, the dynamic behavior of balls and cage under combined loads and the interaction between them is also analysed. The results show that the axial displacements of balls fluctuate periodically under combined loads, and the rotation speeds of balls and cage are easily affected by the load, presenting as the oscillation of speed. Also, the force between balls and cage increases as the load increases. The dynamic behavior of balls and cage could be effectively improved by avoiding excessive torque loads and limiting the axial preload to 40 N. The wear failure caused by unstable operation of bearings cannot be ignored. This model is more practical in completing simulation analysis of different operating conditions and structural parameters of the shafting system. It provides a theoretical reference for the structural design and performance analysis of space shafting.

Design of Backstepping Sliding Mode Control for a Polishing Robot Pneumatic System Based on the Extended State Observer

Due to advantages such as a high power-to-weight ratio, a simple structure, and low cost, pneumatic systems are widely applied in automation. However, precise position control of pneumatic actuators is challenging because of factors such as friction, compressibility, and external disturbances. This paper presents a backstepping sliding mode control (BSMC) strategy based on the extended state observer (ESO) for pneumatic cylinder position tracking. A nonlinear model of the pneumatic system is first established, then system states and disturbances are estimated by an ESO, next the BSMC approach is developed using backstepping method and sliding mode control theory, and the stability of the ESO and controller is analyzed using Lyapunov theory. Finally, simulations and experiments on a pneumatic testbed are performed to compare the effectiveness of the proposed approach with PID control. The results show that the proposed strategy improves tracking accuracy and robustness against disturbances, with a 77.04% reduction in root mean square error (RMSE). This research provides a promising control solution for automated pneumatic polishing robots.

A Novel Model Calibration Method for Active Magnetic Bearing Based on Deep Reinforcement Learning

Active magnetically suspended control moment gyro is a novel attitude control actuator for satellites. It is mainly composed of rotor, active magnetic bearing (AMB) and motor. As a crucial supporting component of control moment gyro, the performance of AMB is directly related to the stability of the rotor system and pointing precision of the satellites. Therefore, calibrating the parameters of AMB is essential for the realization of super-quiet satellites. This paper proposed a model calibration method, known as the deep reinforcement learning-based model calibration frame (DRLMC). First, the dynamics of magnetic bearing with damage degradation over its life cycle are modeled. Subsequently, the calibration process is formulated as a Markov Decision Process (MDP), and reinforcement learning (RL) is employed to infer the degradation parameters. In addition, experience replay and target network update mechanism are introduced to guarantee stability. Simulation results demonstrate that the proposed method identifies force-current factor of AMB during its degradation process effectively. Furthermore, additional experiments confirm the robustness of the DRLMC approach.

Closed cervical traction techniques: moving into the 21st century.

Closed cervical traction for reducing dislocating cervical injuries, deformity correction, or discectomy distraction has been implemented in its modern form since the 1930s. Cervical traction state of the art has not changed significantly since the 1960s, with most reductions performed by using Gardner-Wells tongs or halo traction; however, there are many limitations of traditional weight-pulley traction, including limited reduction efficacy and patient safety shortcomings. In this paper, the authors review the history of cervical traction in the 20th century and the limitations of current traction techniques and describe a novel traction device developed at the University of Utah with robotic actuator load or position control and real-time force-sensing capabilities. Preliminary biomechanical testing results using the novel device in an extension spring loading model, with intact cadavers, and in iatrogenic facet injury cadaveric models demonstrated preliminary safety and efficacy of the device. The authors believe this and future research efforts aimed toward improving the efficacy and safety of cervical traction will help advance the field into the 21st century.

Charge Estimation of Piezoelectric Actuators: A Comparative Study

This article first reviews the position control of piezoelectric actuators, particularly charge-based sensorless control systems, which often include a charge estimator as a key component. The rest of the paper is about charge estimators for piezoelectric actuators. Two of the most recent/effective types of these estimators utilise either a sensing capacitor (type I in this paper) or a sensing resistor (type II); the latter (and the newer) type is broadly known as a digital charge estimator. Some experimental results in the literature show that, with the same loss in excitation voltage, a considerably higher amount of charge can be estimated with a type II estimator in comparison with a type I estimator; therefore, the superiority of type II estimators was acknowledged. In order to re-assess this conclusion, this paper equitably compares type I and II estimators through analytical modelling and experimentation. The results indicate that type II estimators have only a slight advantage in estimating higher amounts of charge, if both type I and II estimators are designed appropriately. At the same time, type II estimators have disadvantages; e.g., the resistance of type II estimators has to be tuned to suit different excitation frequencies. This research concludes that capacitor-based (type I) charge estimators for piezoelectric actuators, with pertinent design and implementation, can be still the prime solution for many charge estimation problems despite claims in the literature in the last decade.

Optimal Position Control of Nonlinear Muscle Based on Sliding Mode and Particle Swarm Optimization Algorithm

Rehabilitation is a way to care muscle and skeleton disorders and disease, using mechanical equipment. The aim of rehabilitation is that help disable persons who they reach to minimum physical activities. Since pneumatic actuators have high respect of power to weight, are cleanliness, reachable fluid, are to repair and low cost that can use to implement in robot which interact with human body parts. The other features that use on pneumatic actuators that this is comprisable fluid and variable stiffness traits. Due to importance and performance of these actuators in medical science and also they have high security and exact control which can preserve bodies. Therefore in this project, we search pneumatic actuators with high precision controller that can manufacture various mechanisms for rehabilitation process. Pneumatic actuators mathematical modeling is presented in this thesis. According to modeling of pneumatic actuator, differential equations of actuators are presented. In this research, we used solenoid valves, because proportional valves are expensive. We used sliding mode theory for position control of actuator. There are many importance on these systems and the main aim of this project, optimal path of 2 links mechanism for rehabilitation problems. So optimal tracking robot, we use particle swarm optimization for finding optimal gain controller in sliding mode control because we want to achieve minimum tracking errors in path planning of mechanism.

Application of Least-Squares Support-Vector Machine Based on Hysteresis Operators and Particle Swarm Optimization for Modeling and Control of Hysteresis in Piezoelectric Actuators

Nanopositioning systems driven by piezoelectric actuators are widely used in different fields. However, the hysteresis phenomenon is a major factor in reducing the positioning accuracy of piezoelectric actuators. This effect makes the task of accurate modeling and position control of piezoelectric actuators challenging. In this paper, the learning and generalization capabilities of the model are efficiently enhanced to describe and compensate for the rate-independent and rate-dependent hysteresis using a kernel-based learning method. The proposed model is inspired by the classical Preisach hysteresis model, in which a set of hysteresis operators is used to address the problem of mapping, and then least-squares support-vector machines (LSSVM) combined with a particle swarm optimization (PSO) algorithm are used for identification. Two control schemes are proposed for hysteresis compensation, and their performance is evaluated through real-time experiments on a nanopositioning platform. First, an inverse model-based feedforward controller is designed based on the LSSVM model, and then a combined feedback/feedforward control scheme is designed using a classical control strategy (PID) to further enhance the tracking performance. For performance evaluation, different datasets with a variety of hysteresis loops are used during the simulation and experimental procedures. The results show that the proposed method is successful in enhancing the generalization capabilities of LSSVM training and achieving the best tracking performance based on the combination of feedforward control and PID feedback control. The proposed control scheme outperformed the inverse Preisach model-based control scheme in terms of both positioning accuracy and execution time. The control scheme that uses the LSSVM based on nonlinear autoregressive exogenous (NARX) models has significantly less computational complexity compared to our control scheme but at the expense of accuracy.

Modeling and Position Control of the HASEL Actuator via Port-Hamiltonian Approach

This paper deals with the modeling and control problem of a Hydraulically Amplified Self-healing Electrostatic (HASEL) actuator based on the port-Hamiltonian framework. A nonlinear spring-damper system is used to approximate the mechanical deformation of the actuator due to the motion of the fluid while a nonlinear capacitance is used to approximate the electric behavior of the system. The actuator position control strategy is investigated based on the Interconnection Damping Assignment-Passivity Based Control (IDA-PBC) method, with further Integral Actions (IA) added to cope with load uncertainties. The proposed model and control laws are validated on an experimental benchmark. The experimental tests demonstrate that the proposed model is accurate up to 94% of fitness. The controllers allow assigning the actuator position with ramp and sinusoidal references with the relative error less than 5%. At last, the robustness of the proposed IDA-PBC controller with IA has been shown with the experimental result for the unknown load disturbance rejection.

A Preliminary Top-Down Parametric Design of Electromechanical Actuator Position Control

A top-down process is proposed and virtually validated for the position control of electromechanical actuators (EMA) that use conventional cascade controllers. It aims at facilitating the early design phases of a project by providing a straightforward mean that requires simple algebraic calculations only, from the specified performance and the top-level EMA design parameters. This makes it possible to include realistic control considerations in the preliminary sizing and optimisation phase. The position, speed and current controllers are addressed in sequence. This top-down process is based on the generation and use of charts that define the optimal position gain, speed loop second-order damping factor and natural frequency with respect to the specified performance of the position loop. For each loop, the control design formally specifies the required dynamics and the digital implementation of the following inner loop. A noncausal flow chart summarises the equations used and the interdependencies between data. This potentially allows changing which ones are used as inputs. The process is virtually validated using the example of a flight control actuator. This is achieved with resort to the simulation of a realistic lumped-parameter model, which includes any significant functional and parasitic effects. The virtual tests are run following a bottom–up approach to highlight the pursuit and rejection performance. Using low-, medium- and high-excitation magnitudes, they show the robustness of the controllers against nonlinearities. Finally, the simulation results confirm the soundness of the proposed process.

Attitude dynamics and control of a large flexible space structure by means of a minimum complexity model

A technique for deriving a low-order model of a large, deformable space vehicle, with a configuration resembling that of the International Space Station, is proposed. The modeling approach is based on a hybrid Newtonian–Lagrangian approach, where a generalized Euler equation is written for rotational degrees of freedom, whereas the dynamics of states associated to deformation by means of a Galërkin approach is derived by means of a Lagrangian formulation. The assumed modes method is adopted, where modal characteristics of the main deformable structure are estimated on the basis of its real characteristics, estimated by means a finite element model of the actual truss configuration. A cluster of control moment gyroscopes is considered as the actuator for attitude control. Open- and closed-loop maneuvers are considered, in order to highlight coupling between rotational and deformation degrees of freedom. The modeling tools allows for a quick search of gains which minimizes structural excitations during large angle slews.

Design and Simulation of a Flexible Bending Actuator for Solar Sail Attitude Control

This paper presents the design of a flexible bending actuator using shape memory alloy (SMA) and its integration in attitude control for solar sailing. The SMA actuator has advantages in its power-to-weight ratio and light weight. The bending mechanism and models of the actuator were designed and developed. A neural network based adaptive controller was implemented to control the non-linear nature of the SMA actuator. The actuator control modules were integrated into the solar sail attitude model with a quaternion PD controller that formed a cascade control. The feasibility and performance of the proposed actuator for attitude control were investigated and evaluated, showing that the actuator could generate 1.5 × 10−3 Nm torque which maneuvered a 1600 m2 CubeSat based solar sail by 45° in 14 h. The results demonstrate that the proposed SMA bending actuator can be effectively integrated in attitude control for solar sailing under moderate external disturbances using an appropriate controller design, indicating the potential of a lighter solar sail for future missions.

ARX, ARMAX, Box-Jenkins, Output-Error, and Hammerstein Models for Modeling Intelligent Pneumatic Actuator (IPA) System

A pneumatic actuator is highly nonlinear, which makes the precise position control of this actuator difficult to achieve. In order to achieve precise control, selecting a suitable model structure is a prerequisite before control estimation. This selection of the model structure is based upon an understanding of the physical systems. In this paper, the black-box model is chosen as a system identification model for modeling position control of an Intelligent Pneumatic Actuator (IPA) system and a variety of parametric model structures. The parametric model structure, such as ARX, ARMAX, Box-Jenkins, output-error structures, and Hammerstein available in the black-box model, is used to assist in modeling the IPA system. The results indicate that Hammerstein had the best performance for modeling position control of the IPA system with the best fit 94.95. Also, the results show that ARX, ARMAX, Box-Jenkins, and output-error structures had best fit more than 90%.

Differential resistance based self-sensing recurrent neural network model for position estimation and control of antagonistic Shape Memory Alloy actuator

The paper presents the development and experimental investigation of recurrent neural network (RNN) based self-sensing position estimation (SSPE) model for shape memory alloy actuator (SMA). RNN is used as an estimator in the position feedback control loop to replace the external additional position sensor. The model was inspired by the physics-based analogy of the Mass-Spring-Damper (MSD) system for the antagonistic SMA wire actuator. Actuator displacement presents the hysteresis and non-linear dynamic relationship with observed differential resistance (sensing signal) during phase transformation. The resistance variation causes due to dissimilar resistivity between primary phases of SMA material. The RNN based estimation model was considered because it consists of memory elements for storing the processed information and feedback connections for dynamic modeling of the system. RNN was trained with input sensing signal and target displacement datasets. The estimation accuracy of the model was real-time evaluated during trajectory tracking of reference signals in the feedback control loop. A quantitative performance analysis is assessed in terms of correlation (ℛ), mean absolute error (MEA), and root mean square error (RSME) of the learned model along with the developed actuator system. The tracking results confirm the close agreement between estimated and measured displacement at a reasonable accuracy.

Control of input‐affine nonlinear systems via linear programming

AbstractAn effective and user‐friendly method to control a large portion of input‐affine nonlinear systems is proposed. This method works based on transforming the nonlinear control problem to a linear programming (LP) in unknown controls, the state variables of plant, and possibly the constraints imposed on the controls and state variables. The solution of this LP provides the control to be applied to the plant. The main advantages of the proposed method are: simplicity of applying to multi‐input multi‐output systems, taking into account the actuator saturation and constraints on states, and robustness to uncertainties in the model. Two numerical examples are presented to verify the effectiveness of the proposed method. These are output voltage regulation of a two‐input two‐output dc–dc boost converter with actuator saturation and position control of a magnetic levitation system with a constraint on the speed of ball.

Open Loop Position Control of Soft Hydraulic Actuators for Minimally Invasive Surgery

Minimally invasive surgery (MIS) presents many constraints on the design of robotic devices that can assist medical staff with a procedure. The limitations of conventional, rigid robotic devices have sparked interest in soft robotic devices for medical applications. However, problems still remain with the force exertion and positioning capabilities of soft robotic actuators, in conjunction with size restrictions necessary for MIS. In this article we present hydraulically actuated soft actuators that demonstrate highly repeatable open loop positioning and the ability to exert significant forces in the context of MIS. Open loop position control is achieved by changing the actuator volume, which causes contraction. In one degree of freedom (DOF) configurations, root mean square error (RMSE) values of 0.471 mm, 1.506 mm, and 0.350 mm were recorded for a single actuator against gravity, a single actuator with a pulley, and a horizontal antagonistic configuration, respectively. Hysteresis values of 0.711 mm, 0.958 mm, and 0.515 mm were reported in these experiments. In addition, different numbers of soft actuators were used in configurations two and three DOFs to demonstrate position control. When deactivated, the soft actuators are low-profile and flexible as they are constructed from thin films. As such, a robot with a deployable structure and three soft actuators was constructed. The robot is therefore able to reversibly transition from low to high volume and stiffness, which has potential applications in MIS. A user successfully controlled the deployable robot in a circle tracing task.