Tracking Performance Research Articles

An Investigation of Classical Model Predictive Controller Path Tracking Performance of a Two-Wheel and Four-Wheel Steering Vehicle

Studies on self-driving vehicles have become a trend in recent years, and many systems have been developed to enable autonomous manoeuvre. Various methods have been used to improve path-tracking algorithms which increase vehicle performance, including tracking accuracy and stability. Path tracking is one of the primary problems for autonomous vehicles where the vehicle deviates from target paths, which leads to unnecessary counter-correction. Conventional front wheel steering is unable to satisfy the manoeuvre with a high lateral acceleration since the front steering angle is limited in accurately responding to vehicle dynamics. Moreover, the characteristics of front wheel steering vehicles affect handling stability due to the fact that the turning radius is larger than the vehicle itself. This disadvantageous can compromise safety during under-steer and over-steer situations. The main objective of this preliminary study is to investigate the performance of a four-wheel steering system (4WS) in path tracking for autonomous vehicles using a classical model predictive controller (MPC). Conventional two-wheel steering (2WS) tracking performance following the desired driving system with the same MPC controller is compared with 4WS vehicles. The driving system is developed using Driving Scenario Designer to extract the desired yaw angle and lateral position for controller references constructed in Matlab Simulink. Fixed MPC constraint, prediction, control horizon, yaw angle and lateral position weights were used to compare the performance between 2WS and 4WS vehicles. The simulation results show that 4WS is three times better than 2WS vehicles in tracking predetermined paths. 4WS vehicle show 74.55% better performance in lateral position tracking and 68.75% better performance in trailing predetermined yaw angle value. The simulation data from the preliminary study will be used as a guideline to develop an advanced controller of 4WS vehicles.

Improvement of tracking performance of SMA-actuated rotary actuator via thermocouple delay compensation using adaptive predictor

This paper investigates the impact of bounded and unknown delay of a J-type thermocouple sensor on controlling a Shape Memory Alloy (SMA) actuated rotary actuator. To compensate for the delay, a nonlinear adaptive predictor is utilized, which predicts the exact temperature of the present moment using the output of the sensor. A robust sliding mode control is then designed to track the reference input, which is the rotation angle of the pulley. In addition to temperature estimation, the inner system identification algorithm of the predictor identifies the heat transfer coefficients of the experimental setup. Experimental tests demonstrate the precise control of the proposed algorithm. The tracking error of the adaptive predictor with sliding mode control is compared to that of a regular sliding mode controller without temperature delay compensation consideration, showing the superior performance of the proposed control algorithm.

An Adaptive Sliding Mode Control Using a Novel Adaptive Law Based on Quasi-Convex Functions and Average Sliding Variables for Robot Manipulators

This paper proposes a novel adaptive law that uses a quasi-convex function and a novel sliding variable in an adaptive sliding mode control (ASMC) scheme for robot manipulators. Since the dynamic equations of robot manipulators inevitably include model uncertainties and disturbances, time-delay estimation (TDE) errors occur when using the time-delay control (TDC) approach. Further, the ASMC method used to compensate for TDE errors naturally causes a chattering phenomenon. To improve tracking performance while reducing or maintaining chattering, this paper proposes an adaptive law based on a quasi-convex function that is convex at the origin and concave at the gain switching point, respectively. We also adopt a novel sliding variable that uses previously sampled tracking errors and their time derivatives. Further, this paper proves that the sliding variable of the robot manipulator controlled by the proposed ASMC satisfies uniformly ultimately bounded stability. The simulation and experimental results illustrate the effectiveness of the proposed methods in terms of tracking performance.

Maximum power point tracking control of wind turbines based on speed hysteresis loop to reduce drive-train loads

By adding compensation torque based on the optimal torque, the torque compensation control method expands the unbalanced torque at varying wind speeds, thereby improving the maximum power point tracking (MPPT) performance of wind turbines (WT). However, while improving the wind energy capture efficiency, such methods will lead to drastic fluctuations of generator torque, resulting in significantly increased drive-train loads. To solve this problem, based on the amplitude-frequency characteristics analysis of the WT system transfer function, it is found rotor speed information can not only reflect the main trend of wind speed changes, but also has the characteristics of not being easily affected by the high-frequency component of wind speed. Therefore, setting compensation torque according to it can effectively alleviate the above phenomenon. On this basis, the MPPT control of WT based on speed hysteresis loop to reduce drive-train loads is proposed. The speed information is used to replace the torque information to set the compensation torque amplitude term, and the speed hysteresis loop is introduced to improve the design of the compensation torque symbol term, which can significantly decreased drive-train loads without compromising on power capture. Finally, the effectiveness of the proposed method is verified by the experiments.

An Adaptive Controller with Disturbance Observer for Underwater Vehicle Manipulator Systems

Dynamic control of underwater vehicle manipulator systems (UVMSs) is the key part of underwater intervention tasks. In this paper, we propose an adaptive controller with a disturbance observer that mainly consists of two parts: the first part is the adaptive control law that estimates the changes in the center of mass (COM) and the center of buoyancy (COB) of the vehicle, and the second part is the nonlinear disturbance observer that estimates the external disturbance and model uncertainties. To attenuate the overestimation problem, a damping term is introduced to the adaptive law. The stability of the proposed method is proven on the basis of Lyapunov theory. We develop three scenarios on the Simurv platform and illustrate the effectiveness of the proposed method with a short response time and high tracking performance.

Event-based adaptive formation and tracking control with predetermined performance for nonlinear multi-agent systems

The article focuses on the event-based predetermined performance formation tracking control for uncertain nonlinear multi-agent systems. Each agent is subject to actuator saturation constraint and full-state constraints. To meet the full-state constraints, a barrier function-based nonlinear mapping method is employed instead of the barrier Lyapunov function, such that the undesirable “feasibility conditions” are eradicated. To overcome actuator saturation, the auxiliary systems are utilized to let the controller be designed under the framework of backstepping. The approximation capacity of RBF NNs and adaptive methods are adopted, which can solve unknown uncertainties. An event-triggered predetermined performance formation tracking control strategy is designed, which can guarantee that the agents form the prescribed formation shape in a predetermined settling time and have a fine transient and steady-state tracking performance, while reducing the communication burden from the controller to the actuator. At last, an example of unmanned aerial vehicles is given to test the availability of the results.

Optimal visual control of tendon-sheath-driven continuum robots with robust Jacobian estimation in confined environments

Accurate control of continuum robots in confined environments presents a significant challenge due to the need for a precise kinematic model, which is susceptible to external interference. This paper introduces a model-less optimal visual control (MLOVC) method that enables a tendon-sheath-driven continuum robot (TSDCR) to effectively track visual targets in a confined environment while ensuring stability. The method allows for intraluminal navigation of TSDCRs along narrow lumens. To account for the presence of external outliers, a robust Jacobian estimation method is proposed, wherein improved iterative reweighted least squares with sliding windows are used to online calculate the robot’s Jacobian matrix from sensing data. The estimated Jacobian establishes the motion relationship between the visual feature and the actuation. Furthermore, an optimal visual control method based on quadratic programming (QP) is designed for visual target tracking, while considering the robot’s physical constraint and control constraints. The MLOVC method for visual tracking provides a reliable alternative that does not rely on the precise kinematics of TSDCRs and takes into consideration the impact of outliers. The control stability of the proposed approach is demonstrated through Lyapunov analysis. Simulations and experiments are conducted to evaluate the effectiveness of the MLOVC method, and the results demonstrate that it enhances tracking performance in terms of accuracy and stability.

Distributed multi-sensor multi-target tracking with fault detection and exclusion using belief propagation

Multi-sensor multi-target tracking (MMT) is widely used in civilian and military fields. However, as the number of sensor nodes increases, so does the probability of the sensor node faults corrupting the system. In order to guarantee the tracking performance in the presence of faulty sensors, a distributed MMT algorithm in clutter with sensor fault detection and exclusion under the belief propagation framework (FDE-BP) is proposed in this paper. Firstly, a novel FDE method using the fused residual is proposed to detect the faulty sensors in clutter. To ensure the independence among the fused residuals of different targets, a measurement partition method based on the assignment matrix is proposed. The partition of measurements makes the factor graph have a tree structure rather than a loop one, which reduces the computational complexity. Secondly, the MMT problem is presented by a factor graph model to fuse the information among distributed sensor nodes, and a Gaussian version of FDE-BP is derived. The simulation results show that the proposed FDE-BP algorithm can guarantee the tracking performance in the presence of different types of sensor faults.

Adaptive trajectory tracking control of robotic manipulators based on integral sliding mode

AbstractThis paper proposes an adaptive control scheme with finite‐time convergent property based on integral sliding mode to achieve trajectory tracking control of rigid robotic manipulators. The novel adaptive gain can be adjusted automatically with the system disturbance, as long as the disturbance and its derivative are bounded without requiring more information. Therefore, the control chattering effect can be mitigated obviously. The global robustness of system is guaranteed by Lyapunov stability analysis. Finally, the simulation of a two‐link manipulator is given to show that the desired trajectory tracking performance is obtained with the proposed adaptive sliding mode approach.

Combined simple adaptive and integral terminal sliding mode control for industrial feed drive systems

In the manufacturing industry, feed drive systems are utilized for various applications in industrial machines. These systems are crucial components for ensuring precision in motion control and are, therefore, expected to offer high tracking performance. This study focuses on application of integral terminal sliding mode control (ITSMC) to a feed drive system because of its robustness against disturbances and convergence of the tracking error in finite-time. However, its design requires plant dynamics model. To alleviate this concern, a solution of modifying the original ITSMC law to adaptive ITSMC law based on simple adaptive control (SAC) technique is used. To implement SAC-like adaptive law to a real plant, a property of “almost strictly positive real (ASPR)” is required to be satisfied. ASPR property is satisfied using velocity-based augmented output signal that ensures the stability. In experimental results, the adaptive controller follows the reference trajectory more precisely than conventional methods.

Resonance Suppression Method Based on Hybrid Damping Linear Active Disturbance Rejection Control for Multi-Parallel Converters

The parallel operation of multiple LCL-type converters will result in a deviation of the resonant frequency and resonance phenomena. The occurrence of harmonic resonance can cause problems such as an increase in harmonic voltage and current. This can lead to the malfunction of relay protection and automatic devices, causing damage to system equipment. In severe cases, it can cause accidents and threaten the safe operation of the power system. A hybrid damping active disturbance rejection control (HD-ADRC) method is proposed in this paper to suppress the harmonic resonance of parallel LCL-type converters. First, a third-order linear disturbance rejection controller (LADRC) including the linear extended-state observer and the error-feedback control rate is designed based on LCL-type converter model analysis. The proposed method considers the resonance couplings caused by both internal and external disturbances as the total disturbance, thus improving the anti-disturbance capabilities as well as the operational stability of converters in parallel. Then, a hybrid damping control is proposed to reconstruct the damping characteristics of converters to suppress the parallel resonance spike and reduce the resonance frequency offset. And the parameter selection of the control system is optimized through a stability analysis of the tracking performance and anti-disturbance performance of the HD-ADRC controller. Finally, all the theoretical considerations are verified by simulation and experimental results based on the Matlab/Simulink 2018B and dSpace platform. The simulation and experimental results show that the PI controller gives a THD of 5.33%, which is reduced to 4.66% by employing the HD-LADRC, indicating an improved decoupling between the converters working in parallel with the proposed control scheme.

Robust Adaptive Sliding Mode Control Using Stochastic Gradient Descent for Robot Arm Manipulator Trajectory Tracking

This paper presents an innovative control strategy for robot arm manipulators, utilizing an adaptive sliding mode control with stochastic gradient descent (ASMCSGD). The ASMCSGD controller significant improvements in robustness, chattering elimination, and fast, precise trajectory tracking. Its performance is systematically compared with super twisting algorithm (STA) and conventional sliding mode control (SMC) controllers, all optimized using the grey wolf optimizer (GWO). Simulation results show that the ASMCSGD controller achieves root mean squared errors (RMSE) of 0.12758 for θ1 and 0.13387 for θ2. In comparison, the STA controller yields RMSE values of 0.1953 for θ1 and 0.1953 for θ2, while the SMC controller results in RMSE values of 0.24505 for θ1 and 0.29112 for θ2. Additionally, the ASMCSGD simplifies implementation, eliminates unwanted oscillations, and achieves superior tracking performance. These findings underscore the ASMCSGD’s effectiveness in enhancing trajectory tracking and reducing chattering, making it a promising approach for robust control in practical applications of robot arm manipulators.

Research on intelligent control system of dynamic magnetic tubular linear compressor

Abstract Addressing the cooling power control issue of linear compressors, a PID controller based on a cubic spline improved grey wolf optimization (tri-GWO) algorithm was designed. The mechanical and electromagnetic characteristics of the linear compressor were used to construct the cooling power transfer function of the linear compressor. The effects of different control algorithms on the cooling power of the linear compressor were studied in Simulink, and the response speed, settling time, and robustness of different algorithms were compared based on the simulation results. Further analysis was conducted on the impact of different algorithms in PID control. The results show that in terms of stability and robustness, the tri-GWO-PID controller has an overshoot close to 0 and quickly stabilizes at the target cooling power; in terms of tracking, the tri-GWO-PID controller exhibits the best tracking performance with the smallest cooling power deviation. The research results provide a reference for the study of cooling power control of linear compressors.

Nussbaum-Based Adaptive Neural Networks Tracking Control for Nonlinear PDE-ODE Systems Subject to Deception Attacks.

In this article, the novel adaptive neural networks (NNs) tracking control scheme is presented for nonlinear partial differential equation (PDE)-ordinary differential equation (ODE) coupled systems subject to deception attacks. Because of the special infinite-dimensional characteristics of PDE subsystem and the strong coupling of PDE-ODE systems, it is more difficult to achieve the tracking control for coupled systems than single ODE system under the circumstance of deception attacks, which result in the states and outputs of both PDE and ODE subsystems unavailable by injecting false information into sensors and actuators. For efficient design of the controllers to realize the tracking performance, a new coordinate transformation is developed under the backstepping method, and the PDE subsystem is transformed into a new form. In addition, the effect of the unknown control gains and the uncertain nonlinearities caused by attacks are alleviated by introducing the Nussbaum technology and NNs. The proposed tracking control scheme can guarantee that all signals in the coupled systems are bounded and the good tracking performance can be achieved, despite both sensors and actuators of the studied systems suffering from attacks. Finally, a simulation example is given to verify the effectiveness of the proposed control method.

Trajectory Tracking Control of Unmanned Vehicles via Front-Wheel Driving

Automated Guided Vehicles (AGVs) are the fastest commercially available application of unmanned driving technology, and the research significance of unmanned vehicle technology remains substantial. This paper investigates the driving mode of AGVs and proposes a method to extend the kinematic model of center-driven unmanned vehicles to front-wheel drive. This change in driving force enables unmanned vehicles to achieve faster tracking and higher consistency, solving the problems of long tracking time and insufficient accuracy in complex environments and reducing production costs. By analyzing the posture relationship of the unmanned vehicle system during movement, we established a posture error system to analyze the trajectory tracking problem. Utilizing Lyapunov stability theory and the concept of backstepping, we designed a control scheme that uses linear velocity and heading angular velocity as variables for the posture error system. This control scheme aims to stabilize the system and achieve synchronized trajectory tracking control of the unmanned vehicle. The impact of control parameters in the controller on tracking performance is also discussed. The final experimental simulation results show that the system error stabilizes, and the unmanned vehicle accurately follows the predetermined trajectory, verifying the feasibility of our proposed method and control scheme.

The effects of water flow velocity and odor release frequency on the odor-tracking behavior of swimming crabs (Portunus trituberculatus)

Chemical-guided tracking is vital for critical activities such as foraging and mating in swimming crabs (Portunus trituberculatus), as it facilitates the detection and localization of odor sources. While water flow and odor release patterns are known to influence odor-tracking behavior, the specific mechanisms of their impact remain poorly understood. This study quantified the tracking behaviors of juvenile swimming crabs in response to varying water flow velocities (0.7 cm/s, 1.6 cm/s, and 2.5 cm/s) and odor release frequencies (0.5 Hz, 1.0 Hz, and 1.5 Hz). Results indicate that (1) at a flow velocity of 1.6 cm/s, crabs exhibited a higher proportion of tracking and success. Successful trackers at this flow velocity also demonstrated better tracking performance, including higher net-to-gross displacement ratios (NGDR), reduced gross displacements, shorter exit and track durations, and simpler tracking trajectories. (2) While odor release frequency did not significantly affect the proportion of tracking or success, the successful trackers at a higher frequency (1.5 Hz) had lower NGDR and higher gross displacements compared to those exposed to a lower frequency (0.5 Hz). (3) The successful trackers were not stastistically affected by the interaction between flow velocities and release frequencies. (4) In contrast to successful trackers, unsuccessful ones showed highly variable responses to flow velocity and odor release frequency, with generally inferior tracking performance. These findings provide valuable insights into the olfactory ecology of swimming crabs and can inform the optimized of aquaculture management and formulated feeds production.

An adaptive learning based aberrance repressed multi-feature integrated correlation filter for Visual Object Tracking (VOT)

Target tracking via Correlation Filter (CF) is a hot research area of computer vision domain, and offers various credible benefits. Existing CF algorithms face challenges when there are target appearance variations due to background noise, scale and illumination changes, occlusion, and fast motion, which severely degrades the overall tracker performance. To get maximum benefits, an object tracker should perform well with the less computational burden in the presence of real time challenging situations. To address this issue, a novel visual object trackeris proposed based on multi feature fusion and adaptive learning technique with aberrance suppression. At first, multiple features i.e., Histogram of gradient (HOG), Color Naming (CN), saliency, and gray level intensities are combined using feature fusion technique. Further, based on the evaluation of final fused response map using Peak-to-Sidelobe Ratio (PSR), an adaptive learning strategy is integrated to improve the learning phase of tracker. Tracking results show that the proposed strategy beats the other modern CF trackers with Distance Precision (DP) scores of 88.2%, 85.9%, and 74.1% and 64.7% over OTB2013, OTB2015, and TempleColor128 and UAV123 datasets respectively.

Implementation of the MPPT Particle Swarm Optimization Algorithm on a Hybrid PV-TEG Solar Panel with a DC-DC Buck Converter

Abstract Solar panels necessitate power control to locate the optimal working point amidst quickly changing voltages and diverse weather circumstances. This enables the system to adapt and sustain optimal performance in real-time. In order to address this issue, the optimization methodology known as Maximum Power Point Tracking (MPPT) is employed using an algorithmic artificial intelligence (AI) method. Particle Swarm Optimization (PSO) is a swarm intelligence technique that has effectively tackled diverse optimization issues in intricate systems. The DC-DC Buck Converter, which incorporates Maximum Power Point Tracking (MPPT), serves as an interface between the load and the photovoltaic (PV) system to control the output voltage of the system. A comparison is made between the performance of the Maximum Power Point Tracking (MPPT) technique using the Particle Swarm Optimization (PSO) algorithm and the hill climbing (HC) algorithm developed through MATLAB/SIMULINK simulations. The findings indicate that the PSO algorithm exhibits superior performance in terms of tracking time, output power, and stability, with little fluctuations or noise, as compared to the HC method. The tracking time is 0.02 seconds and 2.52 seconds, respectively. The power at constant irradiation is 14.49 Watts and 14.43 Watts. The power at irradiation variation is 6.98 Watt and 6.96 Watt. The PSO algorithm achieved a remarkable accuracy of 99.76% in this investigation, surpassing that of HC. This enhancement makes the system more effective in acquiring maximum power values.

Time-Delay Sliding Mode Control for Trajectory Tracking of Robot Manipulators

Time-Delay Sliding Mode Control for Trajectory Tracking of Robot Manipulators

Dynamic Surface Tracking Control for Nonlinear Constraints

Abstract An adaptive control scheme based on dynamic surface control (DSC), and intelligent control techniques is developed for a 3-DOF helicopter with unknown disturbance and asymmetric saturation. The reconstructed dynamic neural network (NN) is established to compensate for nonlinear terms in real-time. A designed smooth nonlinear sigmoid function is used to model saturation nonlinearity. The combination of dynamic surface control with adaptive intelligent control techniques resolves and improves the tracking and robust performance of the system. The stability and ultimately bound of all signals of the controlled system are demonstrated by theoretical analysis. Numerical results prove the feasibility of the presented control scheme.