3D Control Research Articles

LSTM-Inversion-Based Feedforward–Feedback Nanopositioning Control

This work proposes a two-degree of freedom (2DOF) controller for motion tracking of nanopositioning devices, such as piezoelectric actuators (PEAs), with a broad bandwidth and high precision. The proposed 2DOF controller consists of an inversion feedforward controller and a real-time feedback controller. The feedforward controller, a sequence-to-sequence LSTM-based inversion model (invLSTMs2s), is used to compensate for the nonlinearity of the PEA, especially at high frequencies, and is collaboratively integrated with a linear MPC feedback controller, which ensures the PEA position tracking performance at low frequencies. Therefore, the proposed 2DOF controller, namely, invLSTMs2s+MPC, is able to achieve high precision over a broad bandwidth. To validate the proposed controller, the uncertainty of invLSTMs2s is checked such that the integration of an inversion model-based feedforward controller has a positive impact on the trajectory tracking performance compared to feedback control only. Experimental validation on a commercial PEA and comparison with existing approaches demonstrate that high tracking accuracies can be achieved by invLSTMs2s+MPC for various reference trajectories. Moreover, invLSTMs2s+MPC is further demonstrated on a multi-dimensional PEA platform for simultaneous multi-direction positioning control.

DS based 2-DOF PID controller for various integrating processes with time delay

This study proposes a direct synthesis-based two-degree-of-freedom (2-DOF) controller for various types of integrating processes with time delays. This 2-DOF controller includes a proportional-integral-derivative (PID) controller to enhance load disturbance rejection performance and a set-point filter to improve servo response performance. The main PID controller parameters are expressed as process model parameters and a single adjustment variable, while the set-point filter is composed of PID controller parameters with weighted factors. The adjustment variable is tuned to achieve an optimal balance between response performance and robustness, based on the maximum magnitude of the sensitivity function (Ms). Controller parameters for various Ms values and guidelines for setting these parameters are provided in a consistent formulaic form using a curve-fitting method. These parameter-setting formulas facilitate the accurate implementation of PID controllers with specified Ms values and allow the controller design to be extended to processes with larger dimensionless time delays for a given Ms value. Although a 2-DOF controller was proposed, the adjustment variable for setting the parameters of the main PID controller and the set-point filter was solely the desired time constant. The proposed method was applied to various integrating processes with time delays, and its performance was compared with existing methods reported in the literature, based on performance indices such as settling time, overshoot, integral of absolute error, total variation in input usage, and global performance index. Simulations were conducted using six examples of various integrating processes with time delays to verify the effectiveness and applicability of the proposed controller.

From PID to ADRC and back: Expressing error‐based active disturbance rejection control schemes as standard industrial 1DOF and 2DOF controllers

AbstractIn this paper, we uncover a new connection between standard proportional‐integral (PI), proportional‐integral‐derivative (PID) controllers and active disturbance rejection control (ADRC), from which we establish formal conditions of equivalence between the two control schemes. Using the equivalence, we devise a step‐by‐step procedure of transitioning from PI/PID to error‐based ADRC. We also show how to go from one degree‐of‐freedom (1DOF) to two degree‐of‐freedom (2DOF) ADRC while retaining a standard 2DOF PI/PID structure. Both procedures facilitate expressing error‐based ADRC schemes as standard industrial 1DOF and 2DOF controllers. This allows the designed controller to have the desired characteristic of ADRC (i.e., strong robustness against internal and external uncertainties) while still being expressed in a form that is familiar to industrial practitioners, where PI/PID structures are still the workhorse of modern control systems. The paper's results ensure backward compatibility of future ADRC‐based solutions and foster the adoption of active disturbance rejection‐based methods in industrial practice as a viable alternative to standard controllers. To further support the findings, a set of tests is conducted in time and frequency domain.

RL-TEE: Autonomous Probe Guidance for Transesophageal Echocardiography Based on Attention-Augmented Deep Reinforcement Learning

Ultrasound image acquisition in conventional transesophageal echocardiography (TEE) requires complex manual operation of the probe in the esophagus based on the interpretation of ultrasound images and in-depth knowledge of the cardiac anatomy. In this work, we formulate the TEE probe guidance task as a reinforcement learning (RL) problem, and present the first learning-based solution to 3-DOF control of a TEE probe based on the ultrasound image feedback, named RL-TEE, in order to mimic the visual search and navigation strategies of expert echocardiographers. The probe-tissue interaction in TEE is carefully modeled in our framework by considering both the requirements for navigation towards the standard views and compliance in the esophageal environment. Furthermore, we propose a hybrid deep Q-network model that augments a convolutional neural network backbone with self-attention mechanisms to better capture spatial information in ultrasound images to guide navigation decisions. The presented methods are preliminarily validated in a TEE simulation environment built with data from 25 subjects to acquire four standard views of the heart. Our results show that the proposed method can effectively learn to accurately and compliantly guide the probe movement for TEE standard view acquisition tasks and has a good generalization ability to unseen patient data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to realize 3-DOF movement guidance of a TEE probe to acquire the standard views of the heart based on the real-time images, which can be applied to existing robotic control systems or used to assist novice echocardiographers in TEE examination, thereby relieving operator workload and improving ease of use. This paper suggests a novel approach that uses the deep RL technique to achieve automatic interpretation of TEE images and intelligent guidance of the probe movement. The RL framework is designed to take into account both the navigation efficiency and compliance with the esophageal environment for the targeted intracorporeal application. A hybrid deep Q-network model that augments a convolutional neural network with attention mechanisms is designed to better capture spatial information from ultrasound images to predict the probe movement. The effectiveness of the framework is preliminarily validated in extensive experiments in a simulation environment built with real patient data. The proposed method can be applied in clinical use to provide real-time TEE probe guidance for novice echocardiographers, and can be integrated with a robotic system to fully automate the TEE acquisition, thereby relieving the doctors from tedious manual operation to focus on the diagnosis and treatment.

Design of 2DOF control system fused with artificial intelligence for power enhancement and mitigation of degradation in fuel cell systems

The commercialization of Fuel Cell Systems (FCSs) faces a key obstacle in the form of limited cell lifespan and resultant cell degradation. This paper introduces a new approach, leveraging an artificial intelligence-based 2DOF control system to regulate the Oxygen Excess Ratio (OER) with two main goals: mitigating cell degradation due to oxygen starvation and optimizing net power output. The proposed control system incorporates a data-driven feedforward controller in conjunction with a feedback controller, facilitating the tracking of desired OER values generated by a data-driven reference generator. We develop fuzzy models and neural networks as the reference generator and feedforward controller to capture the complex FCS behavior by processing the stack current w/wo the temperature of FCS (i.e. Single input or Double input models). By exploring the effects of the structural settings of the models, this study provides a comprehensive understanding of their impact on the representation performance of the FCS characteristics. Although the fitting performances of all models are quite satisfactory, their actual performance gain is evaluated on a realistic FCS model at various operation points. The findings and comparative analysis emphasize the efficacy of incorporating stack temperature in fuzzy-model-based 2DOF control systems, showcasing their potential to maximize net power output through enhanced OER control loop performance while also extending the lifespan of FCSs, as confirmed by results from a developed degradation model.

A Novel Control Method for Permanent Magnet Synchronous Linear Motor Based on Model Predictive Control and Extended State Observer

Permanent magnet synchronous linear motor (PMSLM) is widely used to meet the requirement of high dynamic accuracy positioning, such as in machine tools and devices of semiconductor manufacturing. A new 2-DOF control structure is proposed in this paper to improve the dynamic performance of the positioning servo system with PMSLM. Aiming at the position tracking performance, a control algorithm based on the model predictive control (MPC) is developed with position and speed as the feedback state variables. In addition, an extended state observer (ESO) is designed for the rejection of various disturbances, which are not involved in the control model and are regarded as the lumped disturbance to be estimated and compensated by the ESO. The experimental results show that, compared with the commonly used PPI controller (proportional position controller and proportional–integral speed controller), the proposed method enhances the position bandwidth and servo stiffness effectively.

Flatness-based control in successive loops for robotic manipulators and autonomous vehicles

ABSTRACT The control problem for the multivariable and nonlinear dynamics of robotic manipulators and autonomous vehicles is solved with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of these robotic systems is separated into two subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearised flat systems. The state variables of the second subsystem become virtual control inputs for the first subsystem. In turn, exogenous control inputs are applied to the first subsystem. The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in two case studies: (a) control of a 3-DOF industrial rigid-link robotic manipulator and (b) control of a 3-DOF autonomous underwater vessel. The novel control method can simplify significantly the solution of the nonlinear control problem for robotic manipulators and vehicles. Unlike global linearisation-based control schemes, the proposed flatness-based method in successive loops does not need any changes in state variables of complicated state-space model transformations.

A 2-DOF drag-free control ground simulation system based on Stewart platform

Space-based gravitational wave detection missions use multiple satellites to form a very large scale Michelson laser interferometer in space. This requires extremely high precision displacement measurements at the picometer level between test masses even millions of kilometers apart. Drag-free control is a key technology to ensure the ultra-static and ultra-stable space experiment platform for space-based gravitational wave detection. This paper proposes an innovative ground simulation scheme for drag-free control principle based on the Stewart platform. The kinematics and dynamics modeling of the Stewart platform used in the experiment is presented. A drag-free ground simulation experimental equipment is designed and built. A two-degree-of-freedom (2-DOF) drag-free controller is designed based on the H∞ loop shaping algorithm which outperforms a PID controller in Simulink simulation. A semi-physical simulation experiment is conducted to verify the controller designed using rapid control prototyping technology. The experimental results show that the control performance reaches the limit accuracy of the hardware device, thus verifying the effectiveness of the drag-free control algorithm.

Two-Degrees-of-Freedom PID Control with Kalman Filter for Engraving Machine System

For an engraving machine system with input dynamic disturbance and output random measurement noise, a two-degrees-of-freedom proportional integral derivative (2-DOF PID) control method based on the Kalman filter is firstly proposed in this paper, which can effectively reject the input disturbance and ensure the set point tracking performance of the controller. The 2-DOF controller consists of a disturbance rejection controller and a set point tracking controller. The disturbance rejection controller is composed of a PID controller based on a disturbance observer and expectation model. The parameters of the set point tracking controller are tuned using a differential evolution algorithm (DE), and the cumulative absolute error value (IAE) is used as the fitness function of the DE algorithm, which can improve the rationality of intelligent parameter tuning. In addition, the Kalman filter is also applied to deal with the output noise to suppress the influence of the output measurement uncertainty. Finally, compared with existing algorithms, the feasibility and superiority of the proposed algorithm are verified using numerical simulation and an experimental test.

A two-degree-of-freedom controller for a high-precision air temperature control system with multiple disturbances

The key equipment to prevent precision machinery such as lithography from being influenced by air temperature fluctuation is a high-precision air temperature control (ATC) system. The ATC system developed for a lithography immersion testing platform suffers from the difficulties of multiple disturbances, large inertia and time delay. However, seldom have control methods can solve these problems systematically. A two-degree-of-freedom (2DOF) controller is proposed in this paper for this system, where model predictive control (MPC) is developed to overcome large inertia and time delay in the main control loop, and the measurable disturbance from the inlet air is compensated; based on the loop shaping approach, the controller in the disturbance rejection control loop is designed to attenuate the load disturbance; a Kalman filter is designed to reduce the influence of noise having no stable frequency characteristic. The experimental results show that the MPC controller and Kalman filter can effectively avoid the system oscillation, and the disturbance rejection control loop can significantly attenuate the load disturbance in the frequency range from 0.01 to 0.02 rad/s. Therefore, the 1σ precision is increased by 3.3 mK to 8.7 mK by the 2DOF controller when the system is influenced by the load disturbance with different fluctuation amplitudes.

Optimal tracking in two-degree-of-freedom control systems: Coupled tank system

This article presents an optimal design of a two-degree-of-freedom (2-DoF) controller that will lead to zero asymptotic steady-state tracking error. The reference inputs are chosen from the set of steps, ramps, and other persistent signals used currently. The main idea is to transform the tracking 2-DoF problem into an equivalent state-space feedback-control synthesis one. Where, an internal model of the reference input is introduced. Then, through the linear quadratic regulator (LQR) technique, the desired performance objectives are addressed by minimizing a quadratic cost function. Finally, the computed state-feedback optimal gains are linked to the polynomials used within the 2-DoF formalism. The fundamental aspect of the design is that it only utilizes the measurable information of the plant provided by its inputs and outputs and take advantage of efficient state-space numerical algorithms. The proposed method is applied to a coupled-tank system, the results achieved confirm the effectiveness of the approach.

Development and Tuning of a Nonlinear Six DOF Model and Controllers for a Large UAV

This paper presents development of non-linear six-degree-of-freedom model for a large UAV and simulation of its dynamics during its landing phase. A non-linear six-degree-of-freedom aircraft model was developed using the block available in the Aerosim Aeronautical Simulation Blockset in MATLAB / SIMULINK environment. The aircraft parameter data for a large UAV was taken. The developed model could be used for simulating the dynamics of the UAV with single piston engine and fixed pitch propeller. The following controllers were tuned using the above model to simulate its landing phase. ‘Bank to Aileron’, ‘Airspeed error to Pitch command’, ‘Pitch error to Elevator deflection’, ‘Engine rpm error to Throttle’. The first approach was by conventional techniques using PI and PID controllers. In the next stage fuzzy logic controllers were designed for the above cases. All fuzzy controllers were designed using Mamdani inference system.

Discrete-Time Frequency-Domain Disturbance Observer to Mitigate Harmonic Current in PMSM Drives and the Implementation With Reduced Delay

The disturbance observer (DOB) can be employed in electric drives to improve disturbance rejection. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -filter of classical frequency-domain DOBs to attenuate multiple harmonic disturbances is high-order and faces issues including discretization, numerical precision, and frequency adaption, thus its realization in variable-speed drives would be challengeable. This paper proposes a discrete-time multi-frequency DOB (MFDOB) to address these issues. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -filter of the proposed design is constructed with an integrator and several parallel resonators in a closed-loop structure. It uses a single parameter per harmonic to control the disturbance rejection bandwidth and the performance is guaranteed under various fundamental frequencies. The impact of the computation delay on the MFDOB-based current control is studied, and the system with less computation delay exhibits a higher stability margin and a wider range of low sensitivity. To eliminate the computation delay, a real-time pulse-width-modulation (PWM) update scheme is also developed for the DOB-based two-degree-of-freedom (2DOF) controller. The proposed method is evaluated on a laboratory permanent magnet synchronous machine platform.

PrintShear

Most touch-based input devices, such as touchscreens and touchpads, capture low-resolution capacitive images when a finger touches the device's surface. These devices only output the two-dimensional (2D) positions of contacting points, which are insufficient for complex control tasks, such as the manipulation of 3D objects. To expand the modalities of touch inputs, researchers have proposed a variety of techniques, including finger poses, chording gestures, touch pressure, etc. With the rapid development of fingerprint sensing technology, especially under-screen fingerprint sensors, it has become possible to generate input commands to control multiple degrees of freedom (DOF) at a time using fingerprint images. In this paper, we propose PrintShear, a shear input technique based on fingerprint deformation. Lateral, longitudinal and rotational deformations are extracted from fingerprint images and mapped to 3DOF control commands. Further DOF expansion can be achieved through recognition of the contact region of the touching finger. We conducted a 12-person user study to evaluate the performance of PrintShear on 3D docking tasks. Comparisons with other input methods demonstrated the superiority of our approach. Specifically, a 19.79% reduction in completion time was achieved compared with conventional touch input in a full 6DOF 3D object manipulation task.

Optimization Control of Canned Electric Valve Permanent Magnet Synchronous Motor

The traditional canned electric valve consists of an induction motor and a reducer, which need to be matched with the position sensor to achieve precise control of valve position. The position sensor and reducer are not only easily damaged in high-temperature liquids, but also the slip rate of the induction motor is greatly affected by the liquid temperature, which makes it difficult to achieve accurate control. To address the above problems, this paper introduces a new topology of canned electric valve permanent magnet synchronous motor (CEV-PMSM), and a new maximum torque per ampere (MTPA) model is proposed. The new MTPA control equation considering the canned sleeve parameters is derived theoretically. By comparing it with id = 0 control and ideal MTPA control strategy, it is proved that the new MTPA model reflects the electric valve operation characteristics more realistically. In order to achieve sensorless control of the electric valve, and to achieve fast response and high-precision control under external disturbances and parameter uncertainties, the proposed control scheme combines sensorless control and two-degree-of-freedom (2-DOF) control. Consequently, the proposed control scheme can effectively improve the static and dynamic performances of the CEV-PMSM, as well as adjust the tracking and anti-disturbance performances independently. Finally, a 2 kW 100 r/min prototype was manufactured and corresponding experiments were conducted to verify the accuracy of the analysis.

Control Mass-Spring-Damper Based on Tuning Trade-off PID Controller

This paper discusses control mass-spring-dumper (MSD) system used in vehicle suspensions. The vehicle suspension consists of mass, coil (spring), and shock absorber (dumper). MSD provided a shock effect when the vehicle was caused by the frictional force on the load. The dificulty to achive the stability of suspension and the following of set point tracking. Therefore, the proportional-integral-derivative (PID) based on tuning 1-degree of freedom (1-DOF) and 2-degree of freedom (2-DOF) were proposed to stability when the disturbance accours. The MSD equation was obtained by using the laplace transform and validated in Matlab Simulink. The result shows that the proposed PID control reduces disturbance rejection by the smaller set point tracking peak amplitude of 0.01, overshoot of 0.68%, rise time of 0.199 seconds. The 1-DOF tuning achieved set point tracking and disturbance rejection with a peak amplitude of 1.13, overshoot 12.9%, rise time 0.134 seconds, and the 2-DOF tuning achived set point tracking and disturbance rejection with peak amplitude of 1.07, overshoot 6.53%, rise time 1.28 seconds. The proposed PID control has the best performance than 1-DOF and 2-DOF controller.

Smith-predictor based enhanced Dual-DOF fractional order control for integrating type CSTRs

Abstract Continuously Stirred Tank Reactors (CSTR) are one of the widely used reactors in the chemical industry. Controlling such reactors is challenging because many times it demonstrates a model which is having a pole at the origin of the s-plane. Moreover, the presence of a dead time necessitates more effective control measures. This work presents a modified smith predictor-based control for integrating type CSTRs with time delay in order to provide adequate servo and regulatory closed-loop responses. Numerous researches on dual DOF control suggested different controller settings for outer and inner-loop controllers. But, in the current study, both the controllers are proposed to be the same which drastically reduces the complexity of the design. To offer good robustness in the closed-loop response, the controller is synthesized with a user-defined maximum sensitivity. Case studies on CSTRs for both the nominal and disturbed process models are conducted and the same is compared with recently developed control laws. Lastly, a performance comparison on ISE, ITAE, and IAE is provided.

Adaptive self-learning controllers with disturbance compensation for automatic track guidance of industrial trucks

This paper presents an extended control concept for automatic track guidance of industrial trucks in intralogistic systems. It is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI). The presented approach is able to adapt itself to different industrial truck variants and to the associated specific vehicle parameters. In order to avoid starting the whole training of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a simplified linear model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step using a more complex nonlinear model, representing the real industrial truck. In this way, the controller is adapted to the actual truck variant and the corresponding parameter values. By using the nonlinear model, it can be ensured that the forklift's dynamic is approximated within the entire operating range, even at high steering angles. Moreover, the influence of the disturbance variable of the system (path curvature) is compensated by considering this a priori knowledge within the control design. Therefore, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably adjusted. In this way, the occurring path curvatures can be considered in both training steps and the control parameters can be optimized accordingly. Thus, the influence of the disturbance variable can be compensated, which significantly improves the control quality. In order to demonstrate this, the new approach is compared to an RL control concept, which is not considering the disturbance variable and to a classical two-degrees-of-freedom (2DoF) control approach.

Adaptive Notch Filter in a Two-Link Flexible Manipulator for the Compensation of Vibration and Gravity-Induced Distortion

This paper presents a 2-link, 2-DOF flexible manipulator control using an inverse feedforward controller and an adaptive notch filter with a direct strain feedback controller. In the flexible manipulator, transient and residue vibrations inhibit the full potential of the manipulator. Vibrations caused by abrupt changes in the direction of the links are referred to as transient vibrations, whereas residual vibrations occur when the arm takes too long to settle after engaging in the intended task. The feedforward adaptive notch filter will reduce transient vibration caused by the manipulator arm beginning and halting suddenly, while the strain feedback will assure the quick decay of leftover vibrations. Maple, Maplesim, and MATLAB tools were used to model the manipulator and create the inverse controller and adaptive notch filter. The experiments took place in the dSPACE control desk environment. The experimental results of the spectral power of strain resulting from the two strategies are compared. From the results, the adaptive notch filter control had over an 80% improvement in the reduction in resonant frequencies that contribute to vibration. The results confirmed the feasibility of the approach, characterized by very minimal transient vibrations and a quick settling of the end effector.

Virtual regression-based myoelectric hand-wrist prosthesis control and electrode site selection using no force feedback

Most transradial prosthesis users with conventional “Sequential” myoelectric control have two electrode sites which control one degree of freedom (DoF) at a time. Rapid EMG co-activation toggles control between DoFs (e.g., hand and wrist), providing limited function. We implemented a regression-based EMG control method which achieved simultaneous and proportional control of two DoFs in a virtual task. We automated electrode site selection using a short-duration (90 s) calibration period, without force feedback. Backward stepwise selection located the best electrodes for either six or 12 electrodes (selected from a pool of 16). We additionally studied two, 2-DoF controllers: “Intuitive” control (hand open-close and wrist pronation-supination controlled virtual target size and rotation, respectively) and “Mapping” control (wrist flexion–extension and ulnar-radial deviation controlled virtual target left–right and up-down movement, respectively). In practice, a Mapping controller would be mapped to control prosthesis hand open-close and wrist pronation-supination. Eleven able-bodied subjects and 4 limb-absent subjects completed virtual target matching tasks (fixed target moves to a new location after being “matched,” and subject immediately pursues) and fixed (static) target tasks. For all subjects, both 2-DoF controllers with 6 optimally-sited electrodes had statistically better target matching performance than Sequential control in number of matches (average of 4–7 vs 2 matches, p < 0.001) and throughput (average of 0.75–1.25 vs 0.4 bits/s, p < 0.001), but not overshoot rate and path efficiency. There were no statistical differences between 6 and 12 optimally-sited electrodes for both 2-DoF controllers. These results support the feasibility of 2-DoF simultaneous, proportional myoelectric control.