Low Tracking Error Research Articles

Robust Path Planning via Learning from Demonstrations for Robotic Catheters in Deformable Environments.

Navigation through tortuous and deformable vessels using catheters with limited steering capability underscores the need for reliable path planning. State-of-the-art path planners do not fully account for the deformable nature of the environment. This work proposes a robust path planner via a learning from demonstrations method, named Curriculum Generative Adversarial Imitation Learning (C-GAIL). This path planning framework takes into account the interaction between steerable catheters and vessel walls and the deformable property of vessels. In-silico comparative experiments show that the proposed network achieves a 38% higher success rate in static environments and 17% higher in dynamic environments compared to a state-of-the-art approach based on GAIL. In-vitro validation experiments indicate that the path generated by the proposed C-GAIL path planner achieves a targeting error of 1.26 ±0.55mm and a tracking error of 5.18 ±3.48mm. These results represent improvements of 41% and 40% over the conventional centerline-following technique for targeting error and tracking error, respectively. The proposed C-GAIL path planner outperforms the state-of-the-art GAIL approach. The in-vitro validation experiments demonstrate that the path generated by the proposed C-GAIL path planner aligns better with the actual steering capability of the pneumatic artificial muscle-driven catheter utilized in this study. Therefore, the proposed approach can provide enhanced support to the user in navigating the catheter towards the target with greater accuracy, effectively meeting clinical accuracy requirements. The proposed path planning framework exhibits superior performance in managing uncertainty associated with vessel deformation, thereby resulting in lower tracking errors.

Robust Control Based on Adaptative Fuzzy Control of Double-Star Permanent Synchronous Motor Supplied by PWM Inverters for Electric Propulsion of Ships

This study presents the development of an adaptive fuzzy control strategy for double-star PMSM-PWM inverters used in ship electrical propulsion. The approach addresses the current and speed tracking challenges of double-star permanent magnet synchronous motors (DSPMSMs) in the presence of parametric uncertainties. Initially, a modeling technique employing a matrix transformation method is introduced, generating decoupled and independent star windings to eliminate inductive couplings, while maintaining model consistency and torque control. The precise DSPMSM model serves as the foundation for an unknown nonlinear backstepping controller, approximated directly using an adaptive fuzzy controller. Through the Lyapunov direct method, system stability is demonstrated. All signals in the closed-loop system are ensured to be uniformly ultimately bounded (UUB). The proposed control system aims for low tracking errors, while also mitigating the impact of parametric uncertainties. The effectiveness of the adaptive fuzzy nonlinear control system is validated through tests conducted in hardware-in-the-loop (HIL) simulations, utilizing the OPAL-RT platform, OP4510.

Pilot performance during simulated point and boundary avoidance tracking tasks

Abstract Helicopters are used in complex and harsh operational environments, such as search and rescue missions and firefighting, that require operating in ground proximity, tracking targets while avoiding impacting obstacles, namely a combination of point tracking (positive) and boundary avoidance (negative) objectives. A simulation task representing simplified helicopter dynamics is used to investigate point tracking and boundary avoidance tasks. The variance and regression analysis are used to study the effects of task conditions on participants’ tracking errors and input aggression. The overall tracking error shows a negative correlation with input aggression. The participants tend to have higher input aggression and lower tracking error near the boundaries, exposing the switching of manipulation input strategies under different task conditions. It also suggests a potential way of designing simulation tasks for human operators manipulating helicopters and a trigger for investigating pilots’ biodynamic feedthrough.

A Hybrid Control-Oriented PEMFC Model Based on Echo State Networks and Gaussian Radial Basis Functions

The goal of increasing efficiency and durability of fuel cells can be achieved through optimal control of their operating conditions. In order to implement such controllers, accurate and computationally efficient fuel cell models must be developed. This work presents a hybrid (physics-based and data-driven), control-oriented model for approximating the output voltage of proton exchange membrane fuel cells (PEMFCs) while operating under dynamical conditions. First, a physics-based model, built from simplified electrochemical, membrane dynamics and mass conservation equations, is developed and validated through experimental data. Second, a data-driven, neural network (echo state network) is trained, fitted and tested with the same dataset. Then, the hybrid model is formed as a parallel structure, where the simplified physics-based model and the trained data-driven model are merged through an algorithm based on Gaussian radial basis functions. The merging algorithm compares the output of both single models and assigns weights for computing the prediction of the hybrid result. The proposed hybrid model structure is successfully trained, validated and tested with an experimental dataset originating from fuel cells within an automotive PEMFC stack. The hybrid model is assessed through the mean square error index, with the result of a low tracking error.

How Do Low Tracking Error, Multifactor ETFs Fit into the Factor Investment Landscape?

How Do Low Tracking Error, Multifactor ETFs Fit into the Factor Investment Landscape?

Force Tracking Control of Lower Extremity Exoskeleton Based on a New Recurrent Neural Network

The lower extremity exoskeleton can enhance the ability of human limbs, which has been used in many fields. It is difficult to develop a precise force tracking control approach for the exoskeleton because of the dynamics model uncertainty, external disturbances, and unknown human–robot interactive force lied in the system. In this paper, a control method based on a novel recurrent neural network, namely zeroing neural network (ZNN), is proposed to obtain the accurate force tracking. In the framework of ZNN, an adaptive RBF neural network (ARBFNN) is employed to deal with the system uncertainty, and a fixed-time convergence disturbance observer is designed to estimate the external disturbance of the exoskeleton electrohydraulic system. The Lyapunov stability method is utilized to prove the convergence of all the closed-loop signals and the force tracking is guaranteed. The proposed control scheme’s (ARBFNN-FDO-ZNN) force tracking performances are presented and contrasted with the exponential reaching law-based sliding mode controller (ERL-SMC). The proposed scheme is superior to ERL-SMC with fast convergence speed and lower tracking error peak. Finally, experimental tests are conducted to verify the efficacy of the proposed controller for solving accurate force tracking control issues.

Learning and Outperforming the Model Predictive Control With a Linear ϵ-Support Vector Machine for Power Converter

This paper presents a linear ϵ error insensitive support vector machine (ϵ-SVM) learned from the model predictive control (MPC) with very limited training samples. Unlike the existing learning-based MPC which takes the same inputs as the MPC, the ϵ-SVM learns from historical data as well. Such structure is found to have both feedforward and feedback elements that improve performance. Besides, using the ϵ error-insensitive tube also increases the noise cancellation capability. The experiment is carried out on a three-phase inverter with an L-C filter and shows that the linear ϵ-SVM-MPC not only has better performance than the artificial neural networks (ANN) MPC, but also outperforms the online MPC in both low THD values and small tracking errors regardless the load conditions. Moreover, the use of the linear kernel significantly reduces the time complexity to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1), which is much less than <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ⁿ</i> ) in either online MPC or ANN-MPC. Therefore, the linear ϵ-SVM-MPC has provided an effective way to implement the MPC and it is preferable for the DSP. This article is accompanied by a video demonstrating the real-time operation.

Optimal Control for a Three-Rotor Unmanned Aerial Vehicle in Programmed Flights

In this study, we propose a new approach to selecting PID controller parameters for a UAV tricopter during programmed flights. The approach uses optimization techniques to determine the optimal values of the PID controller parameters based on the desired performance of the UAV. The proposed method is particularly effective for repeated programmed flights in which the UAV follows a defined flight path. The use of a PID control provides a reliable and robust control system that can cope with various disturbances and uncertainties in UAV dynamics. The proposed method provides an alternative to adaptive control, which requires a significant amount of system identification and parameter tuning. The effectiveness of the proposed method has been verified in simulation studies, and the results show its ability to achieve satisfactory performance with low tracking error and fast response time. The result of the work is an improvement in control for a specific object at a specific mission. We present how the recipients can perform this procedure for their object and their mission to be able to improve the control gain in the physical controller.

Robot assisted knee joint RoM exercise: A PID parallel compensator architecture through impedance estimation

Knee joint rehabilitation exercise refers to a therapeutic procedure of a patient having dysfunctions in certain abilities to move knee joint due to some medical conditions like trauma or paralysis. The exercise is basically a series of repeated assistive physical movements within the range of motion (RoM) of the joint. Reflex action of limbs during RoM exercise causes inappropriate balance of load which may cause secondary injuries, such as damages of muscle or tendon tissues. Establishing correlation between impedance data and limb motions is important to solve this problem. This paper aims to design and modeling of a robotic arm with an original approach in control strategy which is developed based on the correlation in between the joint-impedances and joint-motion characteristics during exercise. The knee joint impedances are estimated based on the internal feedback of the system dynamics, that lead to design the torque compensator to improve the overall control signals in real time. This paper also demonstrates the characteristics of various responses of the system during exercise with human subject. Results have reflected good performances with low position and velocity tracking errors, ±0.02∘ and 0.04rad.sec−1 during hold phase; and ±0.14∘ and 0.17rad.sec−1 during motion phse. Though, the limitation of the prototype is its current RoM (limited to 0∘–25∘), the system has potential in the application of RoM exercise for paraplegic or monoplegic patients.

Tuning path tracking controllers for autonomous cars using reinforcement learning.

This article proposes an adaptable path tracking control system, based on reinforcement learning (RL), for autonomous cars. A four-parameter controller shapes the behaviour of the vehicle to navigate lane changes and roundabouts. The tuning of the tracker uses an 'educated' Q-Learning algorithm to minimize the lateral and steering trajectory errors, this being a key contribution of this article. The CARLA (CAR Learning to Act) simulator was used both for training and testing. The results show the vehicle is able to adapt its behaviour to the different types of reference trajectories, navigating safely with low tracking errors. The use of a robot operating system (ROS) bridge between CARLA and the tracker (i) results in a realistic system, and (ii) simplifies the replacement of CARLA by a real vehicle, as in a hardware-in-the-loop system. Another contribution of this article is the framework for the dependability of the overall architecture based on stability results of non-smooth systems, presented at the end of this article.

Full-body Human Motion Reconstruction with Sparse Joint Tracking Using Flexible Sensors

Human motion tracking is a fundamental building block for various applications including computer animation, human-computer interaction, healthcare, and so on. To reduce the burden of wearing multiple sensors, human motion prediction from sparse sensor inputs has become a hot topic in human motion tracking. However, such predictions are non-trivial as (i) the widely adopted data-driven approaches can easily collapse to average poses, and (ii) the predicted motions contain unnatural jitters. In this work, we address the aforementioned issues by proposing a novel framework which can accurately predict the human joint moving angles from the signals of only four flexible sensors, thereby achieving the tracking of human joints in multi-degrees of freedom. Specifically, we mitigate the collapse to average poses by implementing the model with a Bi-LSTM neural network that makes full use of short-time sequence information; we reduce jitters by adding a median pooling layer to the network, which smooths consecutive motions. Although being bio-compatible and ideal for improving the wearing experience, the flexible sensors are prone to aging which increases prediction errors. Observing that the aging of flexible sensors usually results in drifts of their resistance ranges, we further propose a novel dynamic calibration technique to rescale sensor ranges, which further improves the prediction accuracy. Experimental results show that our method achieves a low and stable tracking error of 4.51 degrees across different motion types with only four sensors.

Model-Free Robust Backstepping Adaptive Cruise Control

This paper studies the model-free robust adaptive cruise control problem of a vehicle with unknown nonlinear dynamics and disturbances. First, under backstepping control framework, the position tracking errors with different spacing strategies are used to design a virtual control law, which provides a velocity reference. Then, a novel data-driven sliding surface whose parameters are updated by designing estimation algorithm is developed to handle the unknown uncertainties and disturbances. Finally, the model-free robust backstepping adaptive cruise control (MFRB-ACC) method including PI control, model-free control, and robust control is designed. The novelty of the proposed control technique lies in its strong robustness, which is not based on the precise vehicle model. The designed data-driven sliding surface releases the necessity for the accurate mathematical model of the vehicle and guarantees the inherent robustness of the controller, in particular to uncertainties, modelling error, or external disturbance. Moreover, the designed controller contains three terms such that it has an effective decoupling ability and strong robustness. The effectiveness and superiority of the designed MFRB-ACC method are validated on MATLAB, and the simulation results show that compared to the PID algorithm, the designed MFRB-ACC algorithm can track its preceding vehicle with lower tracking error under different spacing strategies, different operating conditions, and low sampling frequencies. Especially at a sampling frequency of 0.1 s, the error under the PID-ACC increases from 0.2 m at a sampling frequency of 0.01 s to 2 m, and the error under MFRB-ACC has little change compared to the error at a sampling frequency of 0.01 s.

Time‐varying state constrained estimators‐based command filtered quantized consensus control for nonlinear multiagent systems

AbstractThis article investigates the command filtered backstepping quantized consensus control for the time‐varying full‐state constrained nonlinear multiagent systems based on unknown system dynamics estimators (USDE). First, different from the intelligent control and the USDE‐based control methods, a novel time‐varying state constrained (TVSC)‐USDE is developed to estimate the unknown nonlinear dynamics and disturbances exactly with fewer parameters, where the states in TVSC‐USDE satisfy the state constraints requirements. The proposed TVSC‐USDE can reduce the computational burdens that caused by the online estimation of weights. Second, distinguish from current state constraints methods, by designing the integral barrier Lyapunov functions (IBLFs), the conservatism feasibility conditions are removed, which is more reasonable for the physical multiagent systems. Furthermore, the explosion of complexity (EOC) is a vital issue to tackle under the IBLFs backstepping control schemes. For this purpose, this article designs the command filters to gain the derivatives and handle the EOC problem. Further, to counterbalance the filtering errors, the error compensation signals are first developed in IBLFs‐based control method. Finally, the input quantized signals are introduced for the limited bandwidth problem in multiagent network systems, and the proposed control approach can attain consensus tracking under low communication rates and small tracking errors.

Adaptive Fuzzy Fault Tolerant Control for Robot Manipulators With Fixed-Time Convergence

This paper aims to resolve the three major issues of fault tolerant control (FTC) for robot manipulators: (i) the faster response, lower tracking errors, lower chattering and higher robustness of the FTC, (ii) the requirement of partial or full knowledge of robot dynamics for the design of model-based FTC, and (iii) the global fixed-time convergence of the system. First, a fixed-time controller based on a backstepping control is designed and its disadvantages are analysed. Then, an adaptive fuzzy backstepping control is developed to enhance the tracking performance of the system. The proposed approach does not require the full prior knowledge of robot dynamic model, thus facilitating implementation of the controller in practical applications. In addition, the tracking errors of the system will be practically convergent within a fixed-time, which provides additional system information in advance. The fixed time convergence of the system is mathematically proved and the performance of the system is demonstrated for FTC of a PUMA560 robot.

Fractional order neural sliding mode control based on the FO-Hammerstein model of piezoelectric actuator

Piezoelectric actuators (PEA) are extensively utilized in high-precision micro-measurement and operation. However, owing to the rate-dependent nature of its hysteresis, its accuracy in certain applications would suffer significantly, and the system would become unstable. To address this issue, a new method for developing a feedback control system that can reduce the rate-dependent impacts of PEA on the positioning system is provided using PEA as the research object. This strategy is based on the fractional order Hammerstein model (FO-Hammerstein). For the fractional order model, a novel fractional order integral sliding mode surface is proposed here that can accurately delineate the dynamic characteristics of PEA. This sliding mode surface is composed of a fractional polynomial and an integral term, which can better minimize static errors and monitor reference signals, and it is built using a fractional neural sliding mode control (BP-FSMC) method. The control technique can be extensively utilized in various systems, such as FO-Hammerstein and those described by the fractional transfer function. The research introduces a neural network and an artificial bee colony algorithm (DeC-ABC) that are used to alter the system’s parameters. The study’s findings reveal that a system with high resilience can follow the signals from both composite and single input sources. Compared with the fractional order sliding mode control approach on the basis of extended state observer, the fractional order sliding mode control method based on single parameter adaptive law and the proportional integral sliding mode control method on the basis of feedforward compensation, this method has a quicker response time and lower tracking error.

Is anti-herding always a smart choice? Evidence from mutual funds

Recent empirical studies document a negative relation between herding behaviour and the skill of mutual fund managers. We explore this relationship further by focusing on fund managers' contrarian buy and sell behaviour against the market. Our study reveals an asymmetry in the performance of mutual funds with contrarian buy behaviour and contrarian sell behaviour. The contrarian-buy behaviour reflects skill by positively predicting the cross-section of next period's mutual fund returns, while the contrarian-sell behaviour reflects a lack of skill associated with a negative prediction. These findings are robust to various risk-adjusted performance measures. Contrarian-buy funds outperform momentum-buy funds by 3% per year, while contrarian-sell funds underperform momentum-sell peers by about 4%. These findings are robust across different sizes and styles of mutual funds. Further analysis indicates that the asymmetric effect is reversed during recessions and disappears when market sentiment is high. We also study how mutual fund characteristics relate to contrarian buy and sell practices. We find that mutual funds with larger size, higher flow, lower tracking error, and no manager ownership are more likely to buy against the crowd but sell with the crowd.

Impact of installation error and tracking error on the thermal-mechanical properties of parabolic trough receivers

As the solar energy utilization technology for renewable clean energy, actual parabolic trough receivers are subject to tracking errors and absorber tube installation errors, and the bellows and Kovar ring under these errors have a significant impact on the receiver's safety. Based on the MCRT-FVM-FEM principles in this paper, a receiver's optical-thermal-stress mathematical model is modeled and the thermal-stress performance and safety of the receiver's key components under two errors are evaluated. The correlation between the bellows and Kovar ring's performance and the two errors is analyzed. The results demonstrate that the lower tracking errors and larger installation errors markedly increment the absorber's deformation and have a negative impact on the receiver safety. Bellows and Kovar ring's asymmetrical temperature distribution causes variable swell and uneven stress under tracking and installation errors. Temperature gradient variation of bellows is more sensitive under errors than Kovar rings. Kovar ring's compressive stress rises substantially as the Y-directional installation error varied from −15 mm to 15 mm compared to other errors. With tracking errors exceeding 8 mrad, bellows' tensile stress and Kovar ring's compressive stress diminish under installation error. Thus, larger tracking errors significantly reduce the threat to the receiver when installation errors exist.

Adaptive reinforcement learning interval type II fuzzy fractional nonlinear observer and controller for a fuzzy model of a wind turbine

This article presents an innovative control technique for doubly fed induction generator (DFIG) based wind turbine. A DFIG equation is initially presented using fractional order Takagi and Sugeno interval type II fuzzy modeling. Moreover, fault and disturbance are considered in the DFIG model. Then, to design an observer that estimates both fault and disturbance, the fuzzy model of DFIG is decoupled into three subsystems using a different linear coordinate transformation: the state subsystem without disturbance and fault, the subsystem of fault without disturbance, and the subsystem of disturbance without fault. In the following, using the state subsystem, an adaptive reinforcement learning TS interval type II fuzzy fractional sliding mode observer is introduced. The observer gains are adjusted by applying proximal policy optimization, which is a new application of reinforcement learning. Robustness against uncertainties, fast convergence speed of estimation, high degree of freedom and precise estimation are among features of the suggested observer. In addition, only by utilizing the estimated state subsystem and obtained mathematical relationships, the actuator faults and disturbance are estimated. A fractional order sliding mode controller is proposed for the DFIG speed controller. Low chattering characteristics and accurate response are achieved owing to the fractional speed controller. Also, to compensate the influence of disturbances and faults, an adaptive interval type II fuzzy fractional sliding mode controller is used for rotor current regulations, which employs the estimated fault and disturbance. High robustness, fast response time, low tracking error and chattering are some of the advantages of the proposed fault tolerant controller (FTC). The closed-loop stability of controllers is proved by using the Lyapunov theorem. Simulations is accomplished to evaluate the effectiveness of the proposed method. The proposed controller is compared with other controllers. Robustness against parameter uncertainty is also investigated.

Potential field-based dual heuristic programming for path-following and obstacle avoidance of wheeled mobile robots

AbstractPath-following control of wheeled mobile robots has been a crucial research topic in robotic control theory and applications. In path-following control with obstacles, the path-following control and collision avoidance goals might be conflicting, making it challenging to obtain near-optimal solutions for path-following control and obstacle avoidance with low tracking error and input energy consumption. To address this problem, we propose a potential field-based dual heuristic programming (P-DHP) algorithm with an actor–critic (AC) structure for path-following control of mobile robots with obstacle avoidance. In the proposed P-DHP, the path-following control and collision avoidance problems are decoupled into two ones to resolve the control conflict. Firstly, a neural network-based AC is constructed to approximate the near-optimal path-following control policy in a no-obstacle environment. Then, with the trained path-following control policy fixed, a potential field-based control policy structure is constructed by another AC network to generate opposite control forces as the robot moves toward the obstacle, which can guarantee the robot’s control safety and reduce the tracking error and input energy consumption in obstacle avoidance. The simulated and experimental results show that P-DHP can realize near-optimal path-following control with the satisfaction of safety constraints and outperforms state-of-the-art approaches in control performance.

Performance Analysis of Learning-Based Disturbance Observer for Pulsed Superconducting Cavity Field Control

The use of Disturbance Observer-based (DOB) control is widespread in stabilizing the electromagnetic field in superconducting radio frequency cavities to facilitate beam acceleration in particle accelerators. Repetitive disturbances such as beam loading and Lorentz force cavity detuning are compensated by DOB control, and their suppression is enhanced through the incorporation of a learning scheme into the conventional disturbance observer. This paper evaluated the performance of a learning-based disturbance observer for compensating beam loading and cavity detuning in pulsed superconducting radio frequency cavities and proposes modifications for better field stability. A superconducting cavity baseband model for π-mode was simulated in Matlab/Simulink with a trapezoidal beam pulse as the input disturbance and different cavity detuning values to analyze the controllers’ performance. The simulations were conducted for multiple observer filter bandwidths to evaluate the performance of the learning-based disturbance observer under plant model uncertainties and different detuning values. The results demonstrate that the learning-based disturbance observer yields faster convergence to the reference input and lower tracking errors during the flat top of pulse voltage in comparison to conventional disturbance observer control.