Position Signal Research Articles

Adaptive fixed-time fuzzy fault-tolerant control for robotic manipulator with unknown friction and composite actuator faults

This paper exploits the issue of fixed-time (FT) tracking control (TC) for uncertain robotic manipulator systems (RMS) with composite actuator faults (CAFs), unknown disturbances and joint friction. The fuzzy logic system (FLS) algorithm is introduced to fit the unknown uncertainties including unmodeled parameters, CAFs and joint friction of the RMS, after which a relevant updating algorithm is employed to generate the unidentified boundary of the plant disturbance and FLS approximation error. Then a novel adaptive fuzzy sliding mode (SM) fault-tolerant controller is synthesized, which not only guarantees the boundedness of estimation errors of the involved adaptive parameters, but also actualizes the reachability of the predevised sliding surface. Furthermore, the proof that the system trajectories can achieve the FT convergence to the expected position signals, is then provided on account of the FT stability theory. Ultimately, simulation experiments exhibit the rationality based upon the current control algorithm.

An Output Cooperative Controller for a Hydraulic Support Multi-Cylinder System Based on Neural Network Compensation

The straightness control of a fully mechanized working face is the key technology used in intelligent coal mining, so the position control of a hydraulic support multi-cylinder moving system is of great significance. However, due to the harsh environment of coal mines, complex friction, external disturbances, and the coupling relationship between adjacent cylinders, the accuracy of position control is restricted. Therefore, an output cooperative controller is proposed in this paper for a multi-cylinder system. A high-order sliding mode observer is utilized to estimate the system states with the only available output position signal. A neural network disturbance observer is applied to estimate the lump disturbance of the strict-feedback model, including the system uncertainty, disturbance force and the coupling force between adjacent cylinders. Then, continuous motion position tracking simulation is conducted and the estimation performance of the state observer and neural network is analyzed. Furthermore, a multi-cylinder collaborative control test rig is designed, and experiments based on the actual actions of the hydraulic support are conducted. The results show that the proposed output cooperative controller has an excellent position control performance compared with the traditional proportional–integral controller.

Single-Source VLCP System Based on Solar Cell Array Receiver and Right-Angled Tetrahedron Trilateration VLP (RATT-VLP) Algorithm

A significant deployment limitation for visible light communication and positioning (VLCP) systems in energy- and light-source-restricted scenarios is the reliance of photodetectors (PDs) on external power supplies, compromising sustainability and complicating receiver charging. Solar cells (SCs), capable of harvesting and converting environmental light into electrical energy, offer a promising alternative. Consequently, we first propose an indoor VLCP system that utilizes an SC array as the receiver, alongside a right-angled tetrahedron trilateration visible light positioning (RATT-VLP) algorithm based on a single light source and multiple receivers. The proposed system uses an SC array in place of PDs, utilizing binary phase shift keying (BPSK) signals for simultaneous communication and positioning. In experiments, we verified the system’s error-free communication rate of 1.21 kbps and average positioning error of 3.40 cm in a 30 cm × 30 cm area, indicating that the system can simultaneously satisfy low-speed communication and accurate positioning applications. This provides a viable foundation for further research on SC-based VLCP systems, facilitating potential applications in environments like underwater wireless communication, positioning, and storage tank inspection.

Combining finite-time control and prescribed tracking performance for uncertain PMSM driven steer-by-wire system with unknown disturbance

To solve the uncertainties in permanent magnet synchronous motor (PMSM) driven steer-by-wire (SbW) system, a prescribed-performance-control (PPC) based finite-time fuzzy controller is proposed. Specifically, the dynamics of SbW system driven by PMSM is analyzed, and combine PPC technology to transform the tracking error coordinates. Then, the velocity signal is obtained from the sigmoid function-based tracking differentiator by using the position signal. To solve the problem of mismatched disturbance, fuzzy logic system (FLS) are used as a universal approximator to estimate the lumped nonlinear friction and other unknown functions, the boundary of lumped disturbance is estimated by the designed adaptive updating law. By fusing the designed virtual controller and the intermediate control law, the controller needs no precise parameters of system, under which the effect of uncertainty of control gain can be compensated completely. Finally, rigorous theoretical analysis based on the Lyapunov stability theory is provided to demonstrate the finite-time stability of the closed-loop system under consideration. Simulation and experiment results are presented to show the effectiveness of the developed control approach, and some comparisons are given to show the rapid and accurate position tracking control performance.

Design of the offline test electronics for the nozzle system of proton therapy.

A set of nozzle equipment for proton therapy is currently under development at China Institute of Atomic Energy (CIAE). To facilitate the off-line commissioning of the whole equipment, a set of ionization chamber signal generation system, known as the test electronics, was designed. The results showed that the system can simulate the beam position, beam fluence (which exhibits a positive correlation with the dose), and other related analog signals generated by the proton beam when it traverses the ionization chamber. Moreover, the accuracy of the simulated beam position is within ± 0.33mm, and the accuracy of the simulated beam fluence signal is within ± 1%. The test electronics can output analog signals representing environmental parameters. The test electronics meets the design requirements, which can be used for the commissioning of the nozzle system as well as the treatment control system without the presence of the proton beam.

Robust Control of Motion in Presence of Uncertain Parameters and Control Constraints

The paper describes a novel, simple servo drive position controller, using only knowledge about the structure of the nonlinear model and the constraints met by individual components of the model. The desired behaviour of the position and velocity signals is obtained by imposing a time-varying constraint on the signal aggregating information about position and velocity tracking errors. The method allows you to determine the maximum control (servo drive current) necessary to achieve the control goal under the existing initial conditions and the selected reference trajectory. The control is constrained and consists in appropriate reaction when the trajectory approaches the barrier, the shape of which is responsible for the imposed properties of the transient and quasisteady state tracking error. In addition to the derivation of the control, a discussion of its possible variants and basic properties is presented. Control with time-varying constraints has been introduced, which allows the control objectives to be met with limited conservatism of the imposed constraints. The influence of technical factors related to actual speed and position measurements was discussed and the operation of the real drive on a laboratory stand was presented.

Seeing without a Scene: Neurological Observations on the Origin and Function of the Dorsal Visual Stream.

In all vertebrates, visual signals from each visual field project to the opposite midbrain tectum (called the superior colliculus in mammals). The tectum/colliculus computes visual salience to select targets for context-contingent visually guided behavior: a frog will orient toward a small, moving stimulus (insect prey) but away from a large, looming stimulus (a predator). In mammals, visual signals competing for behavioral salience are also transmitted to the visual cortex, where they are integrated with collicular signals and then projected via the dorsal visual stream to the parietal and frontal cortices. To control visually guided behavior, visual signals must be encoded in body-centered (egocentric) coordinates, and so visual signals must be integrated with information encoding eye position in the orbit-where the individual is looking. Eye position information is derived from copies of eye movement signals transmitted from the colliculus to the frontal and parietal cortices. In the intraparietal cortex of the dorsal stream, eye movement signals from the colliculus are used to predict the sensory consequences of action. These eye position signals are integrated with retinotopic visual signals to generate scaffolding for a visual scene that contains goal-relevant objects that are seen to have spatial relationships with each other and with the observer. Patients with degeneration of the superior colliculus, although they can see, behave as though they are blind. Bilateral damage to the intraparietal cortex of the dorsal stream causes the visual scene to disappear, leaving awareness of only one object that is lost in space. This tutorial considers what we have learned from patients with damage to the colliculus, or to the intraparietal cortex, about how the phylogenetically older midbrain and the newer mammalian dorsal cortical visual stream jointly coordinate the experience of a spatially and temporally coherent visual scene.

Position Correction Control of Permanent-Magnet Brushless Motor Based on Commutation-Interval Current Symmetry

With the needs of environmental protection and the adjustment of energy structure, new energy vehicles are playing an increasingly important role in the field of transportation today. The permanent-magnet brushless direct-current motor has the characteristics of high efficiency, and can be used in the drive system of new energy vehicles or other auxiliary equipment. In the control process of the permanent-magnet brushless direct-current motor, based on a three-Hall position sensor, due to various factors, there are some errors in the Hall position signal, which must be corrected by appropriate measures. In this paper, the relationship between the position deviation in the commutation interval and the non-commutation-phase current is analyzed, and the current expressions in three different states are given. A new closed-loop compensation strategy for correcting the inaccurate commutation caused by the Hall signal error is proposed. Taking the position of a 30° electrical angle before and after the phase-change point as the H point, realizing the current symmetry within the 30° interval around the H point as the target and the sum of the slopes of the tangent lines at the two points symmetrical within the β (0 < β < 30) electrical angle around the H point as the deviation, a proportional-integral regulator is designed to correct the phase error of the phase-change signal. Finally, it is verified by experiments that the closed-loop compensation strategy proposed in this paper can effectively compensate the phase deviation of the commutation signal at a speed of about 2000 r/min, which improves the working efficiency of the motor to a certain extent.

Dynamic Performance Evaluation of a Brushless AC Motor Drive Using Different Sensorless Schemes

The presented study concerns with evaluating the dynamic performance of an isotropic sinusoidal brushless motor drive while utilizing different sensorless schemes. Three estimation algorithms are considered: the first depends on extracting the speed and position via comparing two values of motor's voltage in two co-ordinate systems; the second extracts the speed and position signal via comparing two different values of motor's current defined in two co-ordinates; while the third depends on estimating the motor's flux and use it to get the speed and position. The vector control is adopted to manage the drive dynamics. The detailed mathematical derivations for all system components are presented to facilitate the performance analysis. The theoretical base of each sensorless scheme is also described in detail. The target of the provided comparative analysis is to outline the weakness and strength points of each adopted sensorless schemes while estimating the speed and rotor position for a wide operating speed range. The judgment is measured in terms of the speed and rotor position estimation errors and the dynamic response as well. The performance evaluation process is carried out using MATLAB/Simulink software in which all system parts are simulated using their mathematical models. The findings from the study state that when it comes to dynamic speed behaviour, the voltage-based sensorless technique dominates, while the current-based sensorless approach gives stability in speed estimate priority. Alternatively, the third adopted sensorless scheme offers an acceptable high-speed performance and respectable performance at lower speeds. Statistically, it is found that the voltage-based estimation technique gives respectively lower speed and position estimation errors with percentages of 35% and 10% lower than their values under the current-based estimation technique, and with percentages of 35% and 30% lower than their values under the third adopted scheme.

Mechanistic regulation of planarian shape during growth and degrowth.

Adult planarians can grow when fed and degrow (shrink) when starved while maintaining their whole-body shape. It is unknown how the morphogens patterning the planarian axes are coordinated during feeding and starvation or how they modulate the necessary differential tissue growth or degrowth. Here, we investigate the dynamics of planarian shape together with a theoretical study of the mechanisms regulating whole-body proportions and shape. We found that the planarian body proportions scale isometrically following similar linear rates during growth and degrowth, but that fed worms are significantly wider than starved worms. By combining a descriptive model of planarian shape and size with a mechanistic model of anterior-posterior and medio-lateral signaling calibrated with a novel parameter optimization methodology, we theoretically demonstrate that the feedback loop between these positional information signals and the shape they control can regulate the planarian whole-body shape during growth. Furthermore, the computational model produced the correct shape and size dynamics during degrowth as a result of a predicted increase in apoptosis rate and pole signal during starvation. These results offer mechanistic insights into the dynamic regulation of whole-body morphologies.

A Pseudo-Satellite Fingerprint Localization Method Based on Discriminative Deep Belief Networks

Pseudo-satellite technology has excellent compatibility with the BDS satellite navigation system in terms of signal systems. It can serve as a stable and reliable positioning signal source in signal-blocking environments. User terminals can achieve continuous high-precision positioning both indoors and outdoors without any modification to the navigation module. As a result, pseudo-satellite indoor positioning has gradually emerged as a research hotspot in the field. However, due to the complex and variable indoor radio propagation environment, signal propagation is interfered with by noise, multipath, non-line-of-sight (NLOS) propagation, etc. The geometric relation-based localization algorithm cannot be applied in indoor non-line-of-sight environments. Therefore, this paper proposes a pseudo-satellite fingerprint localization method based on the discriminative deep belief networks (DDBNs). The method acquires the model parameters of pseudo-satellite multi-carrier noise density signal strength in non-line-of-sight indoor spaces through a greedy unsupervised learning method and gradient descent-supervised learning method. It establishes a mapping relationship between the implied features of the pseudo-satellite multi-carrier noise density signal strength and indoor location, enabling pseudo-satellite fingerprint matching localization in indoor non-line-of-sight environments. In this paper, the performance of the positioning algorithm is verified in dynamic and static scenarios through numerous experiments in a laboratory environment. Compared to the commonly used localization algorithms based on fingerprint library matching, the results demonstrate that, in indoor non-line-of-sight test conditions, the system’s 2D static positioning has a maximum error of less than 0.24 m, an RMSE better than 0.12 m, and a 2σ (95.4%) positioning error better than 0.19 m. For 2D dynamic positioning, the maximum error is less than 0.36 m, the average error is 0.23 m, and the 2σ positioning error is better than 0.26 m. These results effectively tackle the challenge of pseudo-satellite indoor positioning in non-line-of-sight environments.

Fault Detection and Isolation in Simulated Batch Operation of Fine Motion Control Rod Drives

Control rods and elements manage the power distribution in nuclear reactors through the motion of banks of rods distributed throughout the core. These positional changes are achieved through the actuation of fine motion control rod drive (FMCRD) mechanisms. In the BWRX-300 design by GE-Hitachi, this mechanism is electrically driven by a servomotor that allows for high-precision control of power outputs. Under operational transients, such as load-following, accurate and precise operation of these servomotors is necessary over long periods of time, so they are key maintenance targets to maintain availability and operational flexibility. Swiftly and precisely identifying faults in the drive mechanisms will support predictive maintenance and reduced costs. This paper used three different types of simulated faults to test the fault detectability of principal component analysis (PCA) when considering the simulated operations of banks of control rods and their associated servomotors. These faults were stator short-circuit faults, ball screw jam faults, and ball screw wear faults. Torque and position were monitored in the simulation. The position signal was insufficient to detect mechanical faults. Torque signals for each servomotor in the bank of rods undergoing multiple position demand changes were projected to a reduced dimensional space via PCA. Q and T2 statistics were employed for anomaly detection. Using this approach, all faults were detected, and the anomalies were isolated to the faulty FMCRD mechanism.

GNSS and LiDAR Integrated Navigation Method in Orchards with Intermittent GNSS Dropout

Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method aims to enhance the research and application of autonomous operational equipment in orchards. Firstly, we design and integrate robot vehicles; secondly, we unify the positioning information of GNSS/INS and laser odometer through coordinate system transformation; next, we propose a dynamic switching strategy, whereby the system switches to LiDAR positioning when the GNSS signal is unavailable; and finally, we combine the kinematic model of the robot vehicles with PID and propose a path-tracking control system. The results of the orchard navigation experiment indicate that the maximum lateral deviation of the robotic vehicle during the path-tracking process was 0.35 m, with an average lateral error of 0.1 m. The positioning experiment under satellite signal obstruction shows that compared to the GNSS/INS integrated with adaptive Kalman filtering, the navigation system proposed in this article reduced the average positioning error by 1.6 m.

A PRECISE RESPIRATORY AND HEART RATE DETECTION METHOD FOR MILLIMETER-WAVE RADAR

Millimeter wave radar as a type of noncontact sensor can more conveniently and insensibly obtain breathing and heartbeat signals. However, due to the weak radar echo signal and the influence of noise, there may be phase ambiguity and separation distortion of breathing and heartbeat signals during the processing, which affects the accuracy of detection. In this paper, an enhanced extended differential and cross multiplication (EDACM) algorithm is presented to obtain chest position signals without phase ambiguity. A specific order finite impulse response (FIR) filter with a Blackman window is applied to extract breath signals without waveform distortion from phase signals, and the heartbeat signal without rate distortion is extracted from the phase signal by means of the designed Chebyshev II filter. The measured results show that the proposed method achieves 96.78% accuracy for heart rate detection and 94.44% accuracy for respiratory rate detection, and improves the accuracy of heart rate detection by 2.22% and respiratory rate detection by 4.06% compared to the band-pass filter method under the same parameter conditions.

Automatic design of gene regulatory mechanisms for spatial pattern formation

Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms—including the number of genes necessary for the formation of the target spatial pattern—we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns.

A synthetic position decoding technology for rotary transformers based rotor position measurement system.

This article proposes a rotary transformer position decoding scheme for motor drive control. In this scheme, a simple analog circuit is used to design the excitation circuit and signal conditioning circuit. Demodulate the modulated sine/cosine signal through software and decode it through a phase locked loop (PLL) to obtain the implemented position signal. This scheme completely avoids the use of traditional, specialized decoding chips. At the same time, in order to make the position decoding scheme applicable to different motor drive control platforms, a single-cycle partition average based pulse signal estimation method is proposed for converting the angle position into pulse signals similar to the output of an encoder's phases A, B, and Z, which are referred to as A/B/Z signals in the paper. In order to experimentally validate the proposed scheme, a software decoding platform based on the STM32F407 was built and compared with the decoding results of the chip. The effectiveness and accuracy of the proposed scheme were verified, and it can be effectively applied in the field of motor drive control.

Integrating Wearable Textiles Sensors and IoT for Continuous sEMG Monitoring.

Surface electromyography is a technique used to measure the electrical activity of muscles. sEMG can be used to assess muscle function in various settings, including clinical, academic/industrial research, and sports medicine. The aim of this study is to develop a wearable textile sensor for continuous sEMG monitoring. Here, we have developed an integrated biomedical monitoring system that records sEMG signals through a textile electrode embroidered within a smart sleeve bandage for telemetric assessment of muscle activities and fatigue. We have taken an "Internet of Things"-based approach to acquire the sEMG, using a Myoware sensor and transmit the signal wirelessly through a WiFi-enabled microcontroller unit (NodeMCU; ESP8266). Using a wireless router as an access point, the data transmitted from ESP8266 was received and routed to the webserver-cum-database (Xampp local server) installed on a mobile phone or PC for processing and visualization. The textile electrode integrated with IoT enabled us to measure sEMG, whose quality is similar to that of conventional methods. To verify the performance of our developed prototype, we compared the sEMG signal recorded from the biceps, triceps, and tibialis muscles, using both the smart textile electrode and the gelled electrode. The root mean square and average rectified values of the sEMG measured using our prototype for the three muscle types were within the range of 1.001 ± 0.091 mV to 1.025 ± 0.060 mV and 0.291 ± 0.00 mV to 0.65 ± 0.09 mV, respectively. Further, we also performed the principal component analysis for a total of 18 features (15 time domain and 3 frequency domain) for the same muscle position signals. On the basis on the hierarchical clustering analysis of the PCA's score, as well as the one-way MANOVA of the 18 features, we conclude that the differences observed in the data for the different muscle types as well as the electrode types are statistically insignificant.

Auxiliary active noise control system based on signal reconstruction

Faced with the problem of existing active control systems failing to obtain a reference signal and reducing or deteriorating the effectiveness of interior noise control, an Auxiliary Active Noise Control system (AANC) based on signal reconstruction technology is proposed, which main contains offline update of multi-network weights and online AANC controller. Firstly, considering the non-linear and non-stationary of the interior noise, and aiming to balance the modeling efficiency and accuracy of algorithms, based on the data fusion and compression sensing technology, a signal reconstruction model for multi-networks based on decomposition optimization is design; Then, in the online AANC controller, the offline calculation of the weight of signal reconstruction model is used to obtain the reconstruction reference signal of the control position, and based on the reconstructed signal, the controller achieves adaptive suppression of passenger ear-sides noise through the VSS-LMS algorithm. Finally, the effectiveness of the proposed AANC is verified using real vehicle data. The results show that the proposed system achieves the balance the modeling efficiency and accuracy, and is superior to existing ANC system in terms of robustness, guaranteeing the stable operation of the interior noise control.

Dual cerebella model neural networks based robust adaptive output feedback control for electromechanical actuator with anti‐parameter perturbation and anti‐disturbance

SummaryTo realize a high‐accuracy tracking control of an electromechanical actuator in which only position signal is available, a new robust adaptive output feedback control strategy based on dual CMAC neural networks is proposed in this article. A high‐gain observer and a neural network are combined to estimate the unmeasured system states, in which a neural network is designed to estimate and compensate the system parameter estimation error and other uncertain nonlinearity to improve the performance of the traditional observer. A robust adaptive controller and another neural network are combined to decrease the impact of parameter perturbation and external disturbance and strengthen the system robustness. Lyapunov theorem is used to derive an on‐line weight adaption law for the two neural networks and an on‐line parameter adaptation law for the uncertain parameters to stabilize the system. Thus the parameter uncertainty and disturbance can be compensated with feedforward compensation technique. A nonlinear robust feedback term is designed to suppress the model compensation deviation. Considering the stronger approximation ability of CMAC basis function against RBF basis function, an improved CMAC neural network is introduced in the proposed controller. A large number of simulations and experiments show that the proposed controller has better tracking performance than many main‐stream output feedback controllers in present.

An Acoustic Array Sensor Signal Recognition Algorithm for Low-Altitude Targets Using Multiple Five-Element Acoustic Positioning Systems with VMD

To solve the problem in target acoustic signal processing and recognition when the target flies at a low altitude based on the acoustic positioning system, which is often affected by external interference and brings false information, this paper proposes a target signal processing and recognition algorithm for low-altitude target acoustic positioning based on variational modal decomposition and the test method of multiple five-element acoustic arrays. This algorithm uses VMD to decompose the target signal into modal components with different central frequencies and then performs wavelet threshold processing on the low-frequency part of the signal. After determining the remaining signal components and the low-frequency part’s threshold, the residual component is reconstructed. Based on the test principle and calculation model of the five-element acoustic positioning system, following processing of the low-altitude target acoustic positioning signal using variational modal decomposition, the cross-correlation function method is introduced to perform correlation operations on the basic array of five acoustic sensors and then obtain the time value and time difference of the target acoustic information in each acoustic sensor, ultimately determining the spatial position of the target. Finally, we used the data fusion processing method for target coordinates in multi-acoustic basic arrays to determine the actual target position. By comparing the results obtained using the high-speed camera method with those of the proposed approach, it was found that the average error in the test area of 100 × 100 m was less than 1 m.