Trajectory Tracking Task Research Articles

Improvement of Maximum Variance Weight Partitioning Particle Filter in Urban Computing and Intelligence

At present, urban computing and intelligence has become an important topic in the research field of artificial intelligence. On the other hand, computer vision as a crucial bridge between urban world and artificial intelligence is playing a key role in urban computing and intelligence. Conventional particle filter is derived from Karman filter, which theoretically based on Monte Carlo method. Sequential importance resampling (SIR) is implemented in conventional particle filter to avoid the degeneracy problem. In order to overcome the shortcomings of the resampling algorithm in the traditional particle filter, we proposed an optimized particle filter using the maximum variance weight segmentation resampling algorithm in this paper, which improved the performance of particle filter. Compared with the traditional particle filter algorithm, the experimental results show that the proposed scheme outperforms in terms of computational consumption and the accuracy of particle tracking. The final experimental results proved that the quality of the maximum variance weight segmentation method increased the accuracy and stability in motion trajectory tracking tasks.

Trajectory Tracking Control for a Boost Converter Based on the Differential Flatness Property

This paper presents the control design for the regulation and trajectory tracking tasks of the non-minimum phase output voltage of a DC-DC Boost-type power converter. The proposed approach exploits the differential flatness property of the system leading to a control strategy whose benefits include a simple derivation based on the parameterization of the control input and a zero steady-state error in the system response due to the establishment of predefined stable error dynamics. The main contribution of this paper is that the proposed controller is robust against power supply variations that may arise from the use of renewable energy sources. Furthermore, it is also robust against load variations that are normally related to energy consumption demands. These aspects are disregarded in most of the existing control techniques. Successful validation of the effectiveness of the flatness-based control system is carried out through numerical simulations and experimental tests performed in a built prototype of the system.

Iteratively Successive Projection: A Novel Continuous Approach for the Task-Based Control of Redundant Robots

The task-based control approach with an activation task mechanism endows a robot the possibility to act complex behaviors in an unstructured environment. However, the task-based control approach based on the classical inverse may suffer from discontinuities as tasks switch between inactive and active states. In this paper, an iteratively successive projection method is proposed to construct a null space projection operator by iteratively successive projecting the space between the null space of each task. And the null space projection operator constructed by the iteratively successive projection method should converge to the null space projection operator obtained by the classical inverse, as the iteration time increases to infinity. By introducing activation factors, a continuous projection operator is obtained with a finite-time iteration. Based on the continuous projection operator, a closed-loop control algorithm is presented, which ensures both the continuity of the joint motion and the boundedness of the tracking error. The simulations on a six-link planar manipulator for the trajectory tracking task in the presence of obstacles show the efficacy of the proposed method in continuity, stability, and calculation-time consumption.

Trajectory planning of multi-degree-of-freedom robot with coupling effect

For a multi-degree-of-freedom (MDOF) robot with flexible components, vibration errors can easily occur during operation. Thus, the position of the end effector inevitably deviates from its desired value and causes failure of the precise trajectory tracking task. To solve this problem, an MDOF KUKA robot is introduced in the present study. We propose a new methodology to investigate the dynamic coupling effect and trajectory planning for the robot. The dynamic coupling effect index is defined for the first time, thereby providing a theoretical basis for the trajectory planning. Moreover, a new trajectory plan is adopted to reduce the vibration errors caused by the coupling effect in the Cartesian coordinate and joint coordinate systems. The advantages of the proposed methodology in improving accuracy and stability are validated by experiments. In addition, the chaos phenomenon is observed, which is the focus of our future study.

Contributions of feedforward and feedback control in a manual trajectory-tracking task

Abstract In joint human-cyber-physical systems, the human operator may rely on a combination of reactive (feedback) and predictive (feedforward) control. This paper proposes an experimental and analytical approach to simultaneously identify the human feedback and feedforward controllers in the context of human-cyber-physical systems (HCPS). In our experiments, participants play a 1DOF reference-tracking video game, tasked to guide a cursor to follow a pseudo-random trajectory. For such tasks, the model inversion hypothesis suggests that the human operator would implement as a feedforward controller the inverse of the cyber-physical-system dynamics. Our results indicate that at lower frequencies (≤ 0.15 Hz), individuals capably invert the system dynamics to implement a feedforward controller, but at higher frequencies, the magnitudes of the estimated feedforward transformation are approximately half those of the exact model inverse. This suggests that a frequency limit at which individuals are unable to follow the system dynamics, and thus, the model inversion prediction is only applicable at lower frequencies.

Design of an adaptive fuzzy variable structure compensator for the nonholonomic mobile robot in trajectory tracking task

Design of an adaptive fuzzy variable structure compensator for the nonholonomic mobile robot in trajectory tracking task

Enhanced neural network control of lower limb rehabilitation exoskeleton by add-on repetitive learning

Abstract This paper addresses neural network (NN) control of a lower limb exoskeleton for rehabilitation. Two control schemes are presented for exoskeleton to conduct trajectory tracking tasks, in the presence of unmodeled dynamics of exoskeleton, interaction between human and exoskeleton, as well as additional disturbances. The first controller is purely NN-based, and a combined error factor (CEF), which consists of the weighted sum of tracking error and its derivative, is adopted to enhance the human safety by improved transient response. The second controller is developed based on a combined scheme of repetitive learning control (RLC) and NN, where the add-on RLC is used to learn periodic uncertainties that attribute to the repetitive motion of the exoskeleton leg. Stabilities of the controllers are proved rigorously in a Lyapunov way. It is worthwhile to highlight that although the pure NN controller can deal with periodic and non-periodic uncertainties simultaneously, the main feature of exoskeleton motion during rehabilitation therapy, namely, the repetitiveness, is fully ignored, thus could degrade the tracking performance. Compared simulation reveals that, the proposed control method has achieved a significant control effect with remarkable transient performance.

Visual Tracking and Depth Estimation of Mobile Robots Without Desired Velocity Information.

In this paper, a visual servoing approach is developed for the trajectory tracking control and depth estimation problem of a mobile robot without a priori knowledge about desired velocities. By exploiting the multiple images captured by the on-board camera, the current and desired poses (i.e., scaled translation and orientation) of the mobile robot are reconstructed to define system errors. Then, an adaptive time-varying controller is proposed to achieve the trajectory tracking task in the presence of nonholonomic constraint and unknown depth parameters. Most of previous works require the measurement of the desired velocity information to facilitate the controller design, leading to tedious offline computation. In this paper, to eliminate this requirement, the desired velocities are estimated in real-time based on a reduced order observer. Moreover, an augmented update law is designed to compensate for the unknown depth parameters and identify the inverse depth constant. The Lyapunov-based method is employed to prove that the proposed controller achieves asymptotic tracking, and the inverse depth estimate converges to its actual value provided that a persistent excitation condition is satisfied. Subsequently, a robust data-driven algorithm is introduced to ensure the convergence of the inverse depth estimate under a relaxed finite excitation condition. Simulation and experimental results are provided to demonstrate the effectiveness of the proposed approach.

Control of Musculoskeletal Systems Using Learned Dynamics Models

Controlling musculoskeletal systems, especially robots actuated by pneumatic artificial muscles, is a challenging task due to nonlinearities, hysteresis effects, massive actuator delay, and unobservable dependencies such as temperature. Despite such difficulties, muscular systems offer many beneficial properties to achieve human-comparable performance in uncertain and fast-changing tasks. For example, muscles are backdrivable and provide variable stiffness while offering high forces to reach high accelerations. In addition, the embodied intelligence deriving from the compliance might reduce the control demands for specific tasks. In this letter, we address the problem of how to accurately control musculoskeletal robots. To address this issue, we propose to learn probabilistic forward dynamics models using Gaussian processes and, subsequently, to employ these models for control. However, Gaussian processes dynamics models cannot be set up for our musculoskeletal robot as for traditional motor-driven robots because of unclear state composition, etc. We hence empirically study and discuss in detail how to tune these approaches to complex musculoskeletal robots and their specific challenges. Moreover, we show that our model can be used to accurately control an antagonistic pair of pneumatic artificial muscles for a trajectory tracking task while considering only one-step-ahead predictions of the forward model and incorporating model uncertainty.

Model-Free Recurrent Reinforcement Learning for AUV Horizontal Control

In this paper, aiming at the problems of 2-DOF horizontal motion control with high precision for autonomous underwater vehicle(AUV) trajectory tracking tasks, deep reinforcement learning controllers are applied to these conditions. These control problems are considered as a POMDP (Partially Observable Markov Decision Process). Model-free reinforcement learning(RL) algorithms for continuous control mission based on Deterministic Policy Gradient(DPG) allows robots learn from received delayed rewards when interacting with environments. Recurrent neural networks LSTM (Long Short-Term Memory) are involved into the reinforcement learning algorithm. Through this deep reinforcement learning algorithm, AUVs learn from sequences of dynamic information. The horizontal trajectory tracking tasks are described by LOS method and the motion control are idealized as a SISO model. Tanh-estimators are presented as data normalization. Moreover, AUV horizontal trajectory tracking and motion control simulation results demonstrate this algorithm gets better accuracy compared with the PID method and other non-recurrent methods. Efforts show the efficiency and effectiveness of the improved deep reinforcement learning algorithm.

Towards Robot-Based Cognitive and Motor Assessment Across the HIV-Stroke Spectrum.

There is an increasing population of people living with both HIV and stroke around the world with no effective neurorehabilitation strategies to deal with the combination of physical, cognitive, and social impairment that result from both diseases. This gap is caused by a lack of tools that are able to assess the various impairments across the HIV-stroke spectrum. Rehabilitation robotics provide a potential approach to address this problem. In this study, we implement a motor and cognitive task on the Haptic TheraDrive, a single degree-of-freedom upper limb rehabilitation robot. We collect data on healthy and HIV-stroke subjects from both upper limbs. Our preliminary data show that mean performance error on a trajectory tracking task and total score and reaction time on the n-back task are metrics that show differences between HIV-stroke patients and a healthy population.

Rendering Strategies for Underactuated Hand Exoskeletons

Underactuated exoskeletons offer improved simplicity, affordability, and movement adjustability based on interaction forces, but in turn they suffer from lack of controllability. In this letter, we propose different rendering strategies to improve the controllability of underactuated devices, with no prior knowledge about the task to be performed. Since underactuated devices cannot achieve all desired poses or render all desired forces, the proposed strategies replace these desired values with proxy values that the devices can satisfy. Strategies in the force and operational spaces aim to minimize the error between the desired and the actual output force or pose, respectively, during operation. We test these strategies first using an underactuated hand exoskeleton during a virtual grasping task. Then, we compare them to each other and to other existing methods by analyzing their control performance during a trajectory tracking task.

Underactuated Stratospheric Airship Trajectory Control Using an Adaptive Integral Backstepping Approach

This paper deals with trajectory tracking control for an underactuated stratospheric airship in the presence of model uncertainties and external disturbances. An adaptive integral backstepping trajectory control is designed from the underactuated airship nonlinear dynamics model. The proposed controller has a two-level structure. A low-level controller based on nonlinear adaptive integral backstepping method stabilizes the attitude and velocity of the airship, whereas a high-level controller performs guidance and trajectory tracking task. Two underactuated cases of zero lateral control force and zero roll moment input are studied. Meanwhile a control allocation component based on active set with weight least square algorithm is put into the low-level controller, to find the optimal control surfaces and thrust inputs under constraints of actuator saturation. By using Lyapunov theory the closed-loop trajectory control system is proven to be globally asymptotically stable. Finally, simulation results show that the proposed adaptive integral backstepping controller can follow the reference even in the presence of parametric uncertainties and external disturbances and time delay inputs.

Smooth path and velocity planning under 3D path constraints for car-like vehicles

Existing path and velocity planning methods for car-like vehicles, the paths of which are subject to constraints on the derivative of the curvature in the horizontal plane, do not accurately express the relationships among position, velocity and acceleration in 3D space. Moreover, velocity planning algorithms are efficient only when the curvature and derivative of the curvature have the same velocity demand. As efficiency and comfort are two key issues in promoting planning algorithms, in this paper, the vehicle is allowed to know the nearly shortest-length path and to set a continuous velocity and acceleration profile to track the trajectory reference while taking into account bounds on acceleration (including lateral acceleration) and jerk that are consistent with comfort. First, to construct a nearly shortest path, the 3D path surface is mapped onto the horizontal, profile and frontal planes, and a 2D path smoothing method is applied to solve the 3D path smoothing problem. This method has been used in highway design, but the theoretical understanding of its performance remains limited. This limitation is addressed from the viewpoint of 3D path smoothing in this paper. In addition, the jerk, acceleration, velocity, steering angle and steering angular acceleration profile are merged into a trajectory tracking task to provide a new velocity planning method to find the time-optimal path. Finally, the capabilities of the path and velocity planning methods within general planning schemes are also demonstrated.

Selective-Compliance-Based Lagrange Model and Multilevel Noncollocated Feedback Control of a Humanoid Robot

This paper presents unified control schemes for compliant humanoid robots that are aimed at ensuring successful execution of both balancing tasks and walking trajectories for this class of bipeds, given the complexity of under-actuation. A set of controllers corresponding to the single-support (SS) and double-support (DS) walking phases has been designed based on the flexible sagittal joint dynamics of the system, accounting for both the motor and link states. The first controller uses partial state feedback (proportional–derivative–derivative (PDD)), whereas the second considers the full state of the robot (proportional–proportional–derivative–derivative (PPDD)), while both are mathematically proven to stabilize the closed-loop systems for regulation and trajectory tracking tasks. It is demonstrated mathematically that the PDD controller possesses better stability properties than the PPDD scheme for regulation tasks, even though the latter has the advantage of allowing for its associated gain-set to be generated by means of standard techniques, such as linear quadratic regulator (LQR) control. A switching condition relating the center-of-pressure (CoP) to the energy functions corresponding to the DS and SS models has also been established. The theoretical results are corroborated by means of balancing and walking experiments using the COmpliant huMANoid (COMAN), while a practical comparison between the designed controller and a classical PD controller for compliant robots has also been performed. Overall, and a key conclusion of this paper, the PPDD scheme has produced a significantly improved trajectory tracking performance, with 9%, 15%, and 20% lower joint space error for the hip, knee, and ankle, respectively.

An Adaptive Dynamic Controller for Quadrotor to Perform Trajectory Tracking Tasks

This work proposes an adaptive dynamic controller to guide an unmanned aerial vehicle (UAV) when accomplishing trajectory tracking tasks. The controller structure consists of a kinematic controller that generates reference commands to a dynamic compensator in charge of changing the reference commands according to the system dynamics. The final control actions thus generated are then sent to the UAV to make it to track an arbitrary trajectory in the 3D space. The parameters of the dynamic compensator are directly updated during navigation, configuring a directly updated self-tuning regulator with input error, aiming at reducing the tracking errors, thus improving the system performance in task accomplishment. After describing the control system thus designed, its stability is proved using the Lyapunov theory. To validate the proposed system simulations and real experiments were run, some of them are reported here, whose results demonstrate the effectiveness of the proposed control system and its good performance, even when the initial values of the parameters associated to the dynamic model of the UAV are completely unknown. One of the conclusions, regarding the results obtained, is that the proposed system can be used as if it were an on-line identification subsystem, since the parameters converge to values that effectively represent the UAV dynamics.

A wearable resistive robot facilitates locomotor adaptations during gait.

Robotic-resisted treadmill walking is a form of task-specific training that has been used to improve gait function in individuals with neurological injury, such as stroke, spinal cord injury, or cerebral palsy. Traditionally, these devices use active elements (e.g., motors or actuators) to provide resistance during walking, making them bulky, expensive, and less suitable for overground or in-home rehabilitation. We recently developed a low-cost, wearable robotic brace that generates resistive torques across the knee joint using a simple magnetic brake. However, the possible effects of training with this device on gait function in a clinical population are currently unknown. The purpose of this study was to test the acute effects of resisted walking with this device on kinematics, muscle activation patterns, and gait velocity in chronic stroke survivors. Six stroke survivors wore the resistive brace and walked on a treadmill for 20 minutes (4×5 minutes) at their self-selected walking speed while simultaneously performing a foot trajectory-tracking task to minimize stiff-knee gait. Electromyography, sagittal plane gait kinematics, and overground gait velocity were collected to evaluate the acute effects of the device on gait function. Robotic-resisted treadmill training resulted in a significant increase in quadriceps and hamstring EMG activity during walking. Significant aftereffects (i.e., improved joint excursions) were also observed on the hip and knee kinematics, which persisted for several steps after training. More importantly, training resulted in significant improvements in overground gait velocity. These results were consistent in all the subjects tested. This study provides preliminary evidence indicating that robotic-resisted treadmill walking using our knee brace can result in meaningful biomechanical aftereffects that translate to overground walking.

Development and control of a robotic lower limb exoskeleton for paraplegic patients

Robot-assisted therapy has gained wide attention in rehabilitation engineering, allowing patients with lower limb motor disorders to perform repeatable and consistent upright bipedal walking. The goal of this paper is to develop a novel wearable powered exoskeleton that enables the paraplegic to perform basic daily movements for active rehabilitation training of lower limbs. Mechanical structure and driving devices of the exoskeleton are reasonably designed and selected to ensure natural interaction with the user and provide sufficient driving torques in the course of walking. In order to avoid unwanted interaction forces between the exoskeleton and the patient, the passive exoskeleton testing system is proposed to measure and record various postures as the reference trajectories. In addition, a cascaded proportional–integral–derivative controller is designed to complete walking assistance tasks in passive control modes. Four typical trajectory tracking tasks are carried out for the sake of evaluating the accuracy and effectiveness of the proposed control strategy. Further experiments were conducted by a healthy subject wearing the rehabilitation exoskeleton to perform sit-to-stand and level walking, and the results demonstrated that the developed robot-assisted system had the natural period and favorable response patterns.

Robust and novel two degree of freedom fractional controller based on two-loop topology for inverted pendulum

A rotary single inverted pendulum (RSIP) typically represents a space booster rocket, Segway and similar systems with unstable equilibrium. This paper proposes a novel two degree of freedom (2-DOF) fractional control strategy based on 2-loop topology for RSIP system which can be extended to control the systems with unstable equilibrium. It comprises feedback and feed-forward paths. Primary controller relates the perturbation attenuation while the secondary controller is accountable for set point tracking. To tune the parameters of proposed fractional controller a simple graphical tuning method based on frequency response is used. The study will serve the outstanding experimental results for both, stabilization and trajectory tracking tasks. The study will also serve to present a comparison of the performance of the proposed controller with the 1-DOF FOPID controller and sliding mode controller (SMC) for the RSIP system. Further to confirm the usability of the proposed controller and to avoid the random perturbations sensitivity, robustness, and stability analysis through fractional root-locus and Bode-plot is investigated.

A multi-switching mode intelligent hybrid control of electro-hydraulic proportional systems

The electro-hydraulic proportional system is a highly non-linear system owing to the fact that the system is composed of components from different disciplines such as electric, hydraulic and mechanical disciplines. In general, a switching-based controller is suitable to the control of such a system. In this paper, a switching-based controller is proposed, which is called multi-switching mode intelligent hybrid control, for electro-hydraulic proportional systems. The novelty of the multi-switching mode intelligent hybrid control is that it integrates the PID control law, the neural-fuzzy control law, and the expert-based control law. To demonstrate the effectiveness of the proposed multi-switching mode intelligent hybrid control, both experiment and simulation were conducted. It is shown that the experimental result corresponds well with the simulation result. Further, the proposed control system was compared with the traditional ones such as PID and neural-fuzzy controller for a trajectory tracking task with an electro-hydraulic proportional, which shows that the proposed one is far superior to these traditional ones. Overall, there is evidence that the proposed multi-switching mode intelligent hybrid control is very effective. It is noted that though the idea of the switching-based control system to electro-hydraulic proportional systems may not be new, the specific integration of the member control laws along with specific control laws developed in this work to electro-hydraulic proportional systems is not reported in the literature and multi-switching mode intelligent hybrid control is potentially useful to other electro-hydraulic proportional systems.