Motion Tracking Control Research Articles

Time Domain Design of a Marine Target Tracking System Accounting for Environmental Disturbances

Environmental disturbances represent significant challenges to the performance and accuracy of autonomous systems, especially in marine environments, where their impact varies based on disturbance severity and the employed guidance law. This paper comprehensively investigates a marine target tracking system using time-domain simulations incorporating realistic environmental disturbances. Three guidance laws and four key performance indicators are analysed to evaluate system performance under disturbed and ideal conditions. A robust and systematic evaluation pipeline is developed and applied to a case study featuring a scaled tugboat model. This approach provides a reliable method to assess tracking accuracy and robustness in adverse conditions. The results are selected from a wide range of possibilities to show the effect of the disturbances on the selected target tracking motion control scenario with two manoeuvres and two environmental conditions. The results are measured through the selected key performance indicators, and several phases are identified for each manoeuvre to extend the analysis not only to the global KPI values but also to the partial values of defined phases. They reveal the quantitative effects of environmental disturbances, exposing different system behaviours and trends. These findings demonstrate the effectiveness of the proposed pipeline in quantifying tracking system performance, delivering useful understandings of the system under environmental disturbances. The broader implications of this study are substantial, offering enhanced predictive accuracy for the performance of the analysed systems, particularly in the context of target tracking. Furthermore, introducing numerical key performance indicators facilitates a more rigorous comparison of different system characteristics, enabling informed decisions in designing and optimising autonomous operations in challenging environments.

LSTM-Inversion-Based Feedforward–Feedback Nanopositioning Control

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

Design of a robust intelligent controller based neural network for trajectory tracking of high-speed wheeled robots

The control design of wheeled mobile robots is often accomplished based on the robot’s kinematics which imposes critical challenges in the motion tracking control of such systems. In the related literature, most works designed dynamic controllers based on the terms of voltage or torque; however, the velocity is often utilized in industrial and commercial applications. To address this issue, this paper focuses on the design of a velocity-based control for the trajectory-tracking problem of mobile robots in practical applications including measurement acquisition. The control design of the mobile robot is realized in two separate parts using kinematic control and dynamic control. The design of kinematic control is carried out based on the robot’s kinematics where the motion is described without considering the forces and torques. A radial basis function (RBF) neural network (NN) based on the model-free controller is designed for the dynamic controller of mobile robots without relying on the system dynamics. In the proposed dynamic control, a time-delay estimation is adopted to estimate the disturbances and noises in the mobile robot and remove them from the feedback loop control. Several typical scenarios of mobile robots with high-speed movements are conducted to assess the feasibility of the proposed NN controller-based time-delay estimation under noises and disturbances. To show the supremacy of the suggested controller, the dynamic responses of the mobile robot test system are compared with the prevalent controllers. The extensive simulation and real-time examinations reveal the capability of the proposed NN controller-based time-delay estimation to control high-speed mobile robots under high-level noises and disturbances.

A heavy-duty tracked vehicle model with a reduced feasible domain for motion tracking control considering dynamic characters of hybrid powertrain

Motion tracking control is an essential part of heavy-duty unmanned tracked vehicles to realize autonomous mobility, which adjusts the vehicle motion in real-time by controlling the driving torques. Achieving accurate motion tracking strongly depends on the use of a precise vehicle dynamic model. To support the motion tracking ability, a vehicle dynamic model with a reduced feasible domain is proposed for heavy-duty unmanned tracked vehicles. Firstly, a vehicle dynamic model is established based on the vehicle kinematics and dynamics. Building upon the established vehicle dynamic model, a graph neural network (GNN) is employed to reduce the feasible domain of the vehicle dynamic model, specifically focusing on the limitation of the powertrain. The powertrain components and their interconnections are abstracted into a graph, which is analyzed to study the interactions between the components using the GNN. Finally, the proposed vehicle dynamic model with a reduced feasible domain is verified through actual heavy-duty tracked vehicle experiments in various cases. Compared with the back propagation neural network, the evaluation error of the driving torque decreased by 25.86%. Results show that the proposed vehicle dynamic model with a reduced feasible domain is closer to the performance of the actual heavy-duty tracked vehicle and their powertrain. And the vehicle dynamic model accurately reduces the solution time of the motion tracking control, resulting in improved accuracy and efficiency of the motion tracking control.

Distributed prescribed-time coordinated control of spacecraft formation flying under input saturation

This paper studies the prescribed-time relative motion control problem of spacecraft formation flying under input saturation. Using prescribed-time theory, a prescribed-time sliding mode is designed such that the states on the sliding mode converge to the equilibrium in the prescribed time. Based on the prescribed-time sliding mode, a prescribed-time relative motion tracking controller is developed, which guarantees fast formation maneuvers with feasible fuel consumption and strong robustness under input saturation. Furthermore, a simulation example is carried out to verify the effectiveness of the proposed controller.

Wheel Torque Distribution Control Strategy for Electric Vehicles Dynamic Performance With an Electric Torque Vectoring Drive Axle

The ability of active chassis control for dynamic performance improvement of electric vehicles with centralized drive system is limited duo to the driving torque between two-side wheels always equally distributed. To solve this problem, this paper proposes a novel electric torque vectoring (TV) drive-axle and its torque distribution control strategy, which can realize the function of arbitrary distribution of the left/right wheel driving torque in the form of centralized drive, thereby improving the dynamic performance of the vehicle. First, the structure and torque distribution principle of TV drive-axle are analyzed theoretically. Subsequently, an accurate mathematical model of TV drive-axle is established based on bond graph theory. Next, a wheel torque distribution control strategy of TV drive-axle is proposed based on hierarchical control approach to improve the vehicle’s handling stability. Wherein, the target yaw rate motion tracking controller as the upper-level controller is designed based on the second-order super-twist sliding mode control (SS-SMC) algorithm, and the wheel drive anti-slip controller in lower-level is used to restrict possible wheels slipping. Finally, the off-line simulation and hardware-in-the-loop (HIL) experiment results suggest that the proposed novel TV drive-axle and control strategy can significantly improve the handling stability and steering flexibility of the vehicle.

Retracted: Motion Control and Tracking Control of UAV Based on Adaptive Sensor

Retracted: Motion Control and Tracking Control of UAV Based on Adaptive Sensor

Motion Tracking Control of Six-Wheel Skid-Steering Electric Vehicles via Two-Level-Sequence-Based Integrated Algorithm

Motion Tracking Control of Six-Wheel Skid-Steering Electric Vehicles via Two-Level-Sequence-Based Integrated Algorithm

Motion-Tracking Control of Mobile Manipulation Robotic Systems Using Artificial Neural Networks for Manufacturing Applications

Robotic systems have experienced exponential growth in their utilization for manufacturing applications over recent decades. Control systems responsible for executing desired robot motion planning face increasingly stringent performance requirements. These demands encompass high precision, efficiency, stability, robustness, ease of use, and simplicity of the user interface. Furthermore, diverse modern manufacturing applications primarily employ robotic systems within disturbed operating scenarios. This paper presents a novel neural motion-tracking control scheme for mobile manipulation robotic systems. Dynamic position output error feedback and B–Spline artificial neural networks are integrated in the design process of the introduced adaptive robust control strategy to perform efficient and robust tracking of motion-planning trajectories in robotic systems. Integration of artificial neural networks demonstrates performance improvements in the control scheme while effectively addressing common issues encountered in manufacturing environments. Parametric uncertainty, unmodeled dynamics, and unknown disturbance torque terms represent some adverse influences to be compensated for by the robust control scheme. Several case studies prove the robustness of the adaptive neural control scheme in highly coupled nonlinear six-degree-of-freedom mobile manipulation robotic systems. Case studies provide valuable insights and validate the efficacy of the proposed adaptive multivariable control scheme in manufacturing applications.

Hierarchical Motion Planning and Tracking for Autonomous Vehicles Using Global Heuristic Based Potential Field and Reinforcement Learning Based Predictive Control

The autonomous vehicle is widely applied in various ground operations, in which motion planning and tracking control are becoming the key technologies to achieve autonomous driving. In order to further improve the performance of motion planning and tracking control, an efficient hierarchical framework containing motion planning and tracking control for the autonomous vehicles is constructed in this paper. Firstly, the problems of planning and control are modeled and formulated for the autonomous vehicle. Then, the logical structure of the hierarchical framework is described in detail, which contains several algorithmic improvements and logical associations. The global heuristic planning based artificial potential field method is developed to generate the real-time optimal motion sequence, and the prioritized Q-learning based forward predictive control method is proposed to further optimize the effectiveness of tracking control. The hierarchical framework is evaluated and validated by the numerical simulation, virtual driving environment simulation and real-world scenario. The results show that both the motion planning layer and the tracking control layer of the hierarchical framework perform better than other previous methods. Finally, the adaptability of the proposed framework is verified by applying another driving scenario. Furthermore, the hierarchical framework also has the ability for the real-time application.

Robust Adaptive Backstepping Motion Control of Underwater Cable-Driven Parallel Mechanism Using Improved Linear Model Predictive Control

This paper proposes a novel motion-tracking control methodology for an underwater cable-driven parallel mechanism (CDPM) that achieves calculation of dynamic tension constraint values, tension planning, parameter linearization, and motion tracking. The control objective is divided into three sub-objectives: motion tracking, horizontal displacement suppression, and cable-tension restriction. A linear model predictive control (LMPC) method is designed to plan cable tensions for motion-tracking and displacement suppression. The robust adaptive backstepping controller converts cable tension into winch speed based on the joint-space method and command filtering. Moreover, the X−swapping method is used to linearize and identify the time−varying nonlinear parameters. An essential prerequisite for restricting cable tension is to obtain cable-tension constraint values. A novel dynamic minimum tension control (DMTC) method, based on the equivalent control concept, is proposed for this aim. The DMTC can adaptively obtain the lower cable-tension threshold through the platform posture and motion status, anchor distribution position, and cable integrity status. Compared to traditional fixed tension constraint values, DMTC can more effectively cope with sudden changes in cable tension than fixed tension constraints. Finally, several simulations are carried out to verify the effectiveness and robustness of the proposed approach.

A hybrid Gaussian mutation PSO with search space reduction and its application to intelligent selection of piston seal grooves for homemade pneumatic cylinders

To make the motion tracking control of the homemade pneumatic cylinder as accurate as possible, it is necessary to properly match the seal groove and seal ring on the piston to generate the appropriate friction. However, since the relationship between friction and motion control accuracy is not yet clear, and the friction affected by many factors cannot be accurately modeled, it is impossible to design the optimal seal groove with the highest possible motion control accuracy for pneumatic cylinder through theoretical calculation or simulation optimization. For this reason, an experimental optimization method is considered to select the optimal one from the six empirically designed seal grooves through a particle swarm optimization (PSO) algorithm. To cope with the shortcomings of slow convergence rate and the tendency to fall into local optimum of the basic PSO algorithm (BPSO), three improved PSO algorithms (HGMPSO-0, HGMPSO and HGMPSO-SSR) are successively proposed in this study. The first two improved algorithms are compared with other PSO variants on 23 benchmark functions tested, and the results show that HGMPSO has better overall performance. To significantly improve the search space search efficiency and make the algorithm converge faster, the search space contraction mechanism is introduced into the HGMPSO algorithm to form the HGMPSO-SSR algorithm. The experimental results show that the HGMPSO-SSR algorithm significantly outperforms other PSO variants used for comparison and successfully achieves intelligent selection of piston seal grooves for the designed homemade pneumatic cylinder.

Adaptive Backstepping Axial Position Tracking Control of Autonomous Undersea Vehicles with Deferred Output Constraint

In this paper, an adaptive backstepping control scheme is proposed to solve the the surge motion tracking control problem of an autonomous undersea vehicle (AUV) with system constraint. First, an initial rectification reference signal is constructed for the subsequent implementation of deferred output constraint and making the control input smaller and smoother in the early stage of system operation. Second, a barrier Lyapunov function is adopted for developing an output-constrained state feedback adaptive controller. Then, on the basis of coordinate transformation and estimating the derivative of surge displacement by a linear and nonlinear combined differentiator, we develop an output feedback adaptive backstepping control scheme for AUVs whose velocity signals are unmeasurable. We also carried out a comparative numerical simulation with traditional adaptive control to verify the feasibility of the proposed control strategy.

Retracted] Motion Control and Tracking Control of UAV Based on Adaptive Sensor

In order to meet the requirements of UAV motion control and tracking control, an adaptive sensor‐based technology is proposed. The main content of the technology is based on the mathematical model of the adaptive sensor, through the quaternion attitude update model, using nonlinear attitude SVDCK filtering and dynamic adaptive adjustment factors and other technologies, and finally through simulation experiments and analysis to build the research means of UAV motion control and tracking control system. The experimental results show that the roll Angle, pitch Angle, and yaw Angle of SVDCKF are 1.703, 1.972, and 1.928, respectively. By adjusting the dynamic adaptive factor, the attitude‐filtering algorithm reduces the error of the attitude solution and improves the robustness of the attitude solution under high dynamic conditions. Conclusion. The technology research based on adaptive sensor can meet the requirements of UAV motion control and tracking control.

Passivity-Based Motion and Force Tracking Control for Constrained Elastic Joint Robots

In the past, several motion and force controls were successfully implemented on rigid-joint robots with constraints. With the invention of mechanically compliant robots, the focus on designing controllers for elastic joint robots with constraints is increasing, especially involving the complexity of the joint elasticity in control. Aiming to bridge the gap between the control schemes of rigid- and elastic-joint robots, this letter presents a controller consisting of a PD+ task-space tracking and integral force control, while the intrinsic inertial and elastic properties of the system are fully preserved. We provide a passivity analysis and prove uniform asymptotic stability of the equilibrium. Simulations on a planar two-armed benchmark system with constraints validate the proposed control law.

Initial-Rectification Neuroadaptive Finite-Time Surge Motion Tracking Control of Autonomous Underwater Vehicle With Input Saturation

Initial-Rectification Neuroadaptive Finite-Time Surge Motion Tracking Control of Autonomous Underwater Vehicle With Input Saturation

Extended state observer design with fractional order Bode’s ideal cut-off filter in a linear motor motion control system

A desired output tracking motion control scheme of a new type of self-made linear motor is proposed in the presence of parameter uncertainties and external disturbance. An extended state observer is designed with a fractional order Bode’s ideal cut-off filter to suppress the high-frequency measurement noise. Characteristics of the linear motor are analyzed, including its working principle, mathematical model, and mechanical structure. A fractional order Bode’s ideal cut-off filter, a second-order extended state observer, a reference acceleration feedforward control scheme, and a normal proportional–derivative algorithm constitute the linear-motion control system in this article. The effectiveness of the control scheme has been verified by comparative simulations and experimental results. The proposed method is an effective way to obtain desired output tracking control performance in actual motion control engineering.

An Adaptive Prescribed Performance Tracking Motion Control Methodology for Robotic Manipulators with Global Finite-Time Stability.

In this paper, the problem of an APPTMC for manipulators is investigated. During the robot’s operation, the error states should be kept within an outlined range to ensure a steady-state and dynamic attitude. Firstly, we propose the modified PPFs. Afterward, a series of transformed errors is used to convert “constrained” systems into equivalent “unconstrained” ones, to facilitate control design. The modified PPFs ensure position tracking errors are managed in a pre-designed performance domain. Especially, the SSE boundaries will be symmetrical to zero, so when the transformed error is zero, the tracking error will be as well. Secondly, a modified NISMS based on the transformed errors allows for determining the highest acceptable range of the tracking errors in the steady-state, finite-time convergence index, and singularity elimination. Thirdly, a fixed-time USOSMO is proposed to directly estimate the lumped uncertainty. Fourthly, an ASTwCL is applied to deal with observer output errors and chattering. Finally, an observer-based-control solution is synthesized from the above techniques to achieve PCP in the sense of finite-time Lyapunov stability. In addition, the precision, robustness, as well as harmful chattering reduction of the proposed APPTMC are improved significantly. The Lyapunov theory is used to analyze the stability of closed-loop systems. Throughout simulations, the proposed PPTMC has been shown to perform well and be effective.

RLOC: Terrain-Aware Legged Locomotion Using Reinforcement Learning and Optimal Control

We present a unified model-based and data-driven approach for quadrupedal planning and control to achieve dynamic locomotion over uneven terrain. We utilize on-board proprioceptive and exteroceptive feedback to map sensory information and desired base velocity commands into footstep plans using a reinforcement learning (RL) policy. This RL policy is trained in simulation over a wide range of procedurally generated terrains. When ran online, the system tracks the generated footstep plans using a model-based motion controller. We evaluate the robustness of our method over a wide variety of complex terrains. It exhibits behaviors which prioritize stability over aggressive locomotion. Additionally, we introduce two ancillary RL policies for corrective whole-body motion tracking and recovery control. These policies account for changes in physical parameters and external perturbations. We train and evaluate our framework on a complex quadrupedal system, ANYmal version B, and demonstrate transferability to a larger and heavier robot, ANYmal C, without requiring retraining.

Improved A* algorithm and model predictive control- based path planning and tracking framework for hexapod robots

PurposePath planning is a fundamental and significant issue in robotics research, especially for the legged robots, since it is the core technology for robots to complete complex tasks such as autonomous navigation and exploration. The purpose of this paper is to propose a path planning and tracking framework for the autonomous navigation of hexapod robots.Design/methodology/approachFirst, a hexapod robot called Hexapod-Mini is briefly introduced. Then a path planning algorithm based on improved A* is proposed, which introduces the artificial potential field (APF) factor into the evaluation function to generate a safe and collision-free initial path. Then we apply a turning point optimization based on the greedy algorithm, which optimizes the number of turns of the path. And a fast-turning trajectory for hexapod robot is proposed, which is applied to path smoothing. Besides, a model predictive control-based motion tracking controller is used for path tracking.FindingsThe simulation and experiment results show that the framework can generate a safe, fast, collision-free and smooth path, and the author’s Hexapod robot can effectively track the path that demonstrates the performance of the framework.Originality/valueThe work presented a framework for autonomous path planning and tracking of hexapod robots. This new approach overcomes the disadvantages of the traditional path planning approach, such as lack of security, insufficient smoothness and an excessive number of turns. And the proposed method has been successfully applied to an actual hexapod robot.