Control Method For Actuator Research Articles

Model Predictive Control of Aero-Mechanical Actuators with Consideration of Gear Backlash and Friction Compensation

To address the issues of low positional accuracy and significant torque pulsation caused by gear backlash and nonlinear friction in the mechanical transmission mechanism of aeronautical flap electromechanical actuators, we propose a model predictive control method for flap electromechanical actuator considering gear backlash and friction compensation. Firstly, we model the gear backlash in the electromechanical actuator’s mechanical transmission mechanism and design a corresponding torque current compensation method using a simplified dead zone model. Secondly, the LuGre compensation friction model is introduced, and a friction torque current compensation method is developed to address the nonlinear friction torque generated during system operation. Finally, the proposed current compensation strategies are employed to mitigate the adverse effects of gear backlash and nonlinear friction on system control performance. The simulation results demonstrate that the proposed method enhances position tracking accuracy, reduces torque pulsation, and significantly improves the overall control performance of the system.

Position control of a soft pneumatic actuator based on the pressure parameter feedback model (PPFM)

Abstract. Soft pneumatic actuators have been one of the cores of soft robotics research and play a key role in driving the development of soft robots. Due to its high degree of internal nonlinearity and unpredictable deformation caused by environmental influences, the control model established for soft robots is still a difficult problem in terms of improving accuracy. This paper proposes a new positional control method for soft pneumatic actuators that are suitable for independent 3D deformation at any position and are the core units of continuous robots. The pressure parameter feedback model (PPFM) of the airbag is obtained by adjusting the pressure input through a proportional valve, collecting the air pressure inside the airbag and obtaining the airbag expansion height. The pressure input signal is changed according to the PPFM of the airbag to control the position of the soft pneumatic actuator. A modular experimental platform is built to validate the PPFM-based control strategy, which is able to adjust the position of the end center point of the soft pneumatic actuator in space with the discussed characteristics. It is demonstrated that the theoretical model can significantly improve the stability and accuracy of the soft pneumatic actuator motion.

Fractional order modeling and internal model control method for dielectric elastomer actuator

Fractional order modeling and internal model control method for dielectric elastomer actuator

Active Fault Tolerant Nonsingular Terminal Sliding Mode Control for Electromechanical System Based on Support Vector Machine

Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper. By borrowing the advantages of model-driven and data-driven methods, a fault tolerant nonsingular terminal sliding mode control method based on support vector machine (SVM) is proposed. A SVM is designed to estimate the fault by off-line learning from small sample data with solving convex quadratic programming method and is introduced into a high-gain observer, so as to improve the state estimation and fault detection accuracy when the fault occurs. The state estimation value of the observer is used for state reconfiguration. A novel nonsingular terminal sliding mode surface is designed, and Lyapunov theorem is used to derive a parameter adaptation law and a control law. It is guaranteed that the proposed controller can achieve asymptotical stability which is superior to many advanced fault-tolerant controllers. In addition, the parameter estimation also can help to diagnose the system faults because the faults can be reflected by the parameters variation. Extensive comparative simulation and experimental results illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.

Quadrotors’ double-loop controller design with tensor product model transformation and partial fully actuated method

In the existing work of tensor product (TP) model transformation, the TP model transformation-based work on the quadrotor’s control system design is scarce, the direct TP model transformation control strategy that applied to the quadrotor fails due to the calculation complexity, infeasibility of the huge amount of linear matrix inequalities, and the complexity of solving the linear matrix inequalities. To solve this problem, a partial TP model transformation-based double loop fuzzy controller has been studied in this work, the double-loop hybrid control scheme combines the fully actuated control method and the TP model transformation, while the fully actuated control method is used to the position subsystem control loop, and the TP model transformation based fuzzy controller is applied to the attitude control of the quadrotor. Moreover, for comparison purpose, a varying-input method based on TP model transformation is extended to the quadrotor’s system control. The double-loop hybrid control scheme could also be extended with other TP model transformation based tensor sampling methods, such as, uniform sampling method and varying-input method. At last, the proposed algorithms are evaluated and compared on Parrot Mambo Minidrone, MFP450 and CUAV V5+ based hexarotor.

Dynamic cyclic loading tests for investigating the influence of loading rate and axial force on the lateral response of RC columns

AbstractThe material properties of steel and concrete vary depending on the loading rate to which they are subjected. To realistically evaluate the seismic performance of reinforced concrete (RC) columns, it is necessary to simulate the loading rate that structures will experience during seismic events as accurately as possible. Real‐time dynamic testing is preferred over quasi‐static testing for this purpose. On the other hand, the seismic performance of RC columns also depends on the axial force applied to the column. However, due to the large axial stiffness of RC columns, accurately controlling the axial force during the test, particularly in real‐time testing, is challenging. Consequently, only a few component‐level dynamic tests have been conducted for RC columns. This study aims to explore the influence of axial force and loading rate on the seismic performance of RC columns. To this end, both slow and fast cyclic loading tests were conducted on square cross‐section RC columns subjected to four different axial forces. By utilizing a robust actuator control method along with a specially designed load transfer element, the axial force was successfully controlled, while the lateral displacements were imposed on the columns in real‐time. The results include lateral strengths, postyield responses, damage patterns, rebar strains, and their rates, all of which constitute unique experimental data that can contribute to a deeper understanding of the actual seismic response of RC columns.

Dynamic resonance frequency control for a resonant-type smooth impact drive mechanism actuator

Multimodal piezoelectric actuators combine multiple vibration modes to achieve specific motions for precise actuation, or to enhance the output performance. For such a combination, the operating frequency ratio is necessarily an integer constraint. However, the resonance frequencies of target modes can be mismatched in assembly, and shift easily during operation, limiting the actuator applications. Therefore, we proposed a dynamic resonance frequency control method for multimodal piezoelectric actuators and applied it to a resonant-type smooth impact drive mechanism actuator. Passive piezoelectric parts were deployed on a Langevin transducer and connected with a field effect transistor (FET) switch. As a pulse width modulation (PWM) signal continuously adjusting the electrical boundary of these passive parts, the first longitudinal L1 mode’s resonance frequency was dynamically controlled to match the third L3 mode’s frequency with a ratio of two, generating a quasi-sawtooth displacement to drive a rotor. Experimental results indicated that the prototype’s L1 frequency can be continuously tuned from 25.715 to 24.76 kHz at 27 ℃, and the corresponding frequency ratio was adjusted from 1.953 to 2.028. Multimodal resonance frequency static matching was confirmed by changing the switching signal’s duty ratio. The L1 and L3 mode driving voltages were 50 Vp-p, and 23 Vp-p, respectively, and the speed of the rotor was 133.7 r/min. Dynamic resonance frequency control was realized with a feedback control system. As the driving PZT temperature increased to 70.5 ℃, both modes were matched and maintained resonance for 400 s. The results demonstrate that this method is conducive to improving actuator performance.

Double duty ratio PWM control method for a novel HSV actuator with low power consumption

This paper presents a novel high speed valve (HSV) actuator with low power consumption. The structure design of tapered armature and control method of double duty ratio pulse width modulation (PWM) are proposed in the HSV actuator to improve steady and transient characteristics. The electric-magneto-mechanical model of the HSV actuator is established and investigated by theoretical analysis. Parameters improvement of the HSV actuator is analyzed by the model, and optimized results for a prototype actuator have been obtained. The experimental and simulation curves agree well with the changing trend, and show that the opening time of the HSV actuator is 7.3 ms, the closing time is 20 ms, and power consumption in maintenance stage is 1.35 W, within the stroke of 2.5 mm. Compared with normal driving method, the proposed structure and method have the advantages of opening time reduced by 37%, closing time reduced by 29%, and steady-state power consumption reduced by 93.5%. The proposed structure and method in the HSV actuator are available to meet the driving requirements of both steady and transient performance in fluid control systems better, especially in power shortage or power supply inconvenient engineering.

A new model-free control method for SMA wire actuators based on DRL

Shape memory alloy (SMA) has been widely used in different applications due to its unique shape memory property. However, when used as an actuator, it exhibits a hysteresis behavior in its relation between temperature and strain, which is highly nonlinear and difficult to control. Although studies have been conducted on establishing various constitutive models of SMA, it is still difficult to achieve the precise control of the SMA wire with the existing models. In this work, a new promising approach regarding the SMA control task as a reinforcement learning (RL) problem is proposed to address this issue, which does not require accurate mathematical models. Both RL and an improved method named deep reinforcement learning (DRL) are used to solve the problem of precise control of a 1D SMA wire actuator, respectively. The simulation results indicate that with the DRL method, the agent can precisely control the output deformation of the SMA wire after only ten episodes of training. Compared with the DRL method, the RL agent can also achieve the same training target but with hundreds of training.

A simple time-predicting-based pressure control method for soft pneumatic actuators using on/off solenoid valves

The study of soft pneumatic actuator control techniques is an important research topic in soft robotics. However, much of the research has concentrated on upper-level control algorithms, with little attention paid to the bottom-level pneumatic controller. This work aims to develop a simple, low cost, and quick-response pressure controller for soft pneumatic actuators. The controller is designed based on an on/off valve-driven pneumatic supply system, which is high speed, economical, and easy to set up. Using the actual pneumatic properties of a controlled object to estimate the switching time of the valves, the controller avoids complex nonlinear pneumatic computation and can achieve a high control frequency. Experiments demonstrate that the controller performs well in terms of speed and precision, having practical value in the control of soft actuators.

Study on control method for actuator of tire-suspension HILS system

In recent years, model-based development such as the HILS system has been used to save time and cost in the development of complex systems such as automobiles. Tire-suspension HILS system was a hybrid method with vehicle dynamics analysis and bench test of the tire-suspension system. The accuracy of an actuator control of the testing machine is significant for this system. In this paper, we proposed to apply inverse dynamics compensation via ‘simulation of feedback control system’ (IDCS) as an actuator control method for tire-suspension HILS. IDCS is a numerical method to obtain an approximate inverse dynamic via feedback control simulation, using only the forward dynamic model of the system. The performance of IDCS with a multibody model of the hardware part of the HILS system was examined in this paper.

Traffic Signal Control via Reinforcement Learning for Reducing Global Vehicle Emission

The traffic signal control problem is an extensively researched area providing different approaches, from classic methods to machine learning based ones. Different aspects can be considered to find an optima, from which this paper emphasises emission reduction. The core of our solution is a novel rewarding concept for deep reinforcement learning (DRL) which does not utilize any reward shaping, hence exposes new insights into the traffic signal control (TSC) problem. Despite the omission of the standard measures in the rewarding scheme, the proposed approach can outperform a modern actuated control method in classic performance measures such as waiting time and queue length. Moreover, the sustainability of the realized controls is also placed under investigation to evaluate their environmental impacts. Our results show that the proposed solution goes beyond the actuated control not just in the classic measures but in emission-related measures too.

Closed-LSTM neural network based reference modification for trajectory tracking of piezoelectric actuator

In this article, we propose a trajectory tracking control method for piezoelectric actuators (PEAs) based on long short-term memory neural network (LSTM-NN). Different from traditional control framework where neural network is used to approximate the open-loop PEA dynamics, LSTM-NN is used to establish the mapping between the actual trajectory and the reference trajectory of the closed-loop PEA, leading to a Closed-LSTM neural network control framework. With this framework, the trained LSTM-NN is used to modify the reference trajectory to compensate for the tracking error without changing the controller. First, we analyze and simplify the modeling of the linear and nonlinear characteristics of the PEA, and select the training input features of the LSTM-NN. Then, we use the actual trajectory and reference trajectory of the closed-loop PEA to train the LSTM-NN. The Closed-LSTM neural network control framework enables independent designs of the baseline feedback controller and feedforward compensator. In particular, the feedback controller is used to guarantee the system stability, and the LSTM-NN reference modification module is used as the feedforward compensator to achieve high-precision trajectory tracking, which does not affect the system stability and can be easily applied to off-the-shelf motion control systems. Its validity is experimentally verified on a PEA platform.

Individual deformability compensation of soft hydraulic actuators through iterative learning-based neural network

Robotic devices with soft actuators have been developed to realize the effective rehabilitation of patients with motor paralysis by enabling soft and safe interaction. However, the control of such robots is challenging, especially owing to the difference in the individual deformability occurring in manual fabrication of soft actuators. Furthermore, soft actuators used in wearable rehabilitation devices involve a large response delay which hinders the application of such devices for at-home rehabilitation. In this paper, a feed-forward control method for soft actuators with a large response delay, comprising a simple feed-forward neural network (FNN) and an iterative learning controller (ILC), is proposed. The proposed method facilitates the effective learning and acquisition of the inverse model (i.e. the model that can generate control input to the soft actuator from a target trajectory) of soft actuators. First, the ILC controls a soft actuator and iteratively learns the actuator deformability. Subsequently, the FNN is trained to obtain the inverse model of the soft actuator. The control results of the ILC are used as training datasets for supervised learning of the FNN to ensure that it can efficiently acquire the inverse model of the soft actuator, including the deformability and the response delay. Experiments with fiber-reinforced soft bending hydraulic actuators are conducted to evaluate the proposed method. The results show that the ILC can learn and compensate for the actuator deformability. Moreover, the iterative learning-based FNN serves to achieve a precise tracking performance on various generalized trajectories. These facts suggest that the proposed method can contribute to the development of robotic rehabilitation devices with soft actuators and the field of soft robotics.

A generalized model predictive control method for series elastic actuator driven exoskeleton robots

In order to optimize the control performance of the compliant actuator system, a generalized predictive control (GPC) method is introduced in the controller design. Compared with conventional predictive control methods which largely depend on the modeling, the proposed GPC method is almost model-free. The stability of closed-loop system has been rigidly proved. Various experimental tests of a series elastic actuator driven exoskeleton robot are verified the effectiveness of the proposed methods.

A Neural Network Based Dynamic Control Method for Soft Pneumatic Actuator with Symmetrical Chambers

Dynamic modeling and control of the soft pneumatic actuators are challenging research. In this paper, a neural network based dynamic control method used for a soft pneumatic actuator with symmetrical chambers is proposed. The neural network is introduced to create the dynamic model for predicting the state of the actuator. In this dynamic model, the effect of the uninflated rubber block on bending deformation is considered. Both pressures of the actuator are used for predicting the state of the actuator during the bending motion. The controller is designed based on this dynamic model for trajectory tracking control. Three types of trajectory tracking control experiments are performed to validate the proposed method. The results show that the proposed control method can control the motion of the actuator and track the trajectory effectively.

Toward Safe Human–Robot Interaction: A Fast- Response Admittance Control Method for Series Elastic Actuator

Series elastic actuator (SEA) is a promising compliance device due to its lower output mechanical impedance, and it is widely applied to ensure safe human-robot interaction. Although some efforts have been made to achieve accurate stiffness tracking, the time-delay issue in SEA control has still not been well investigated. However, the time delay can cause an inaccurate response and increase the risk of injury. To overcome this problem, this article proposes a fast-response admittance control method for SEAs. First, an admittance control scheme considering the external force estimation is developed for a hydraulic SEA. Then, a parallel adaptive time-series (ATS) (P-ATS) compensator is proposed and further adopted in the admittance control scheme to compensate for the time delay and tracking error. The P-ATS compensator is a modification of the ATS compensator, which is enhanced with a unique parallel mechanism. Such a mechanism can save more computational resources on locating better parameters the for P-ATS compensator, thus improving its performance. Moreover, the parameter setting is converted to an optimization task, which is solved by the whale swarm algorithm (WSA) to achieve higher accuracy. The newly located parameters are compared to the current parameters based on a proposed evaluation criterion, thus guaranteeing the quality of the updated parameters. All the above strategies are employed to improve the SEA admittance control performance. The results obtained from both simulation and real-world experiments validate that, compared to conventional methods, the proposed method achieves a better performance in SEA stiffness tracking with lower time delay and tracking error.

Extended-State-Observer-Based Super Twisting Control for Pneumatic Muscle Actuators

This paper presents a tracking control method for pneumatic muscle actuators (PMAs). Considering that the PMA platform only feedbacks position, and the velocity and disturbances cannot be observed directly, we use the extended-state-observer (ESO) for simultaneously estimating the system states and disturbances by using measurable variables. Integrated with the ESO, a super twisting controller (STC) is design based on estimated states to realize the high-precision tracking. According to the Lyapunov theorem, the stability of the closed-loop system is ensured. Simulation and experimental studies are conducted, and the results show the convergence of the ESO and the effectiveness of the proposed method.

Multi-Agent Based Traffic Signal Optimization under Fully Actuated Control

This paper presents a multi-agent based distributed traffic control model to optimize the traffic signal for multiple intersections. Previous works in the area of traffic signal control suffer from a number of inadequacies, including the use of fixed cycle length, centralized mode of operations and dependency on historical data. Considering these, the aim of this work is to control the traffic signal timings by adjusting the phase sequence in order to minimize the delay in traffic at the intersections. To model the traffic network, a three-tier multi-agent system has been adopted in distributed mode. In addition, a fully actuated signal control algorithm is designed and it utilizes state-space equations to formulate the queue length at the green light phase and red light phase. The proposed model is simulated with SUMO simulator and a comparative analysis has been made between adaptive control method, multi-agent method based on collective learning and multi-agent based fully actuated control method on a similar platform. The results spectacle the proposed traffic control model outperforms that of other existing control methods in all condition; hence it can be deployed to control the tremendous traffic on the road network and to optimize the traffic signal in more effective manner.

Research on the RBF‐PID control method for the motor actuator used in a UHV GIS disconnector

To improve the reliability and intelligent level of the AC transmission system, the radial basis function-based proportional-integral-derivative (RBF-PID) control method for the motor actuator used in the 550 kV gas-insulated switchgear (GIS) disconnector is proposed. According to the dynamic mathematical model of the motor actuator, the main structure of the RBF neural network based on the gradient descent method for learning algorithm is constructed. An identification function is formed by taking values of the output error square, and then by gathering information on the real-time tuning parameters of PID. Based on that, the simulation of the control system is constructed. The comparative analyses of the tracking control characteristics and the servo following error of the disconnector's contact speed between double-loop PID and RBF-PID are done through the computational and experiment methods, respectively. The results show that 550 kV GIS disconnector with the motor actuator by the RFB-PID control method has better controllability, and the servo following error is controlled within 0.1 m/s.