Unknown External Forces Research Articles

Stereo Visual Servoing Control of a Soft Endoscope for Upper Gastrointestinal Endoscopic Submucosal Dissection.

Quickly and accurately completing endoscopic submucosal dissection (ESD) operations within narrow lumens is currently challenging because of the environment's high flexibility, invisible collision, and natural tissue motion. This paper proposes a novel stereo visual servoing control for a dual-segment robotic endoscope (DSRE) for ESD surgery. Departing from conventional monocular-based methods, our DSRE leverages stereoscopic imaging to rapidly extract precise depth data, enabling quicker controller convergence and enhanced surgical accuracy. The system's dual-segment configuration enables agile maneuverability around lesions, while its compliant structure ensures adaptability within the surgical environment. The implemented stereo visual servo controller uses image features for real-time feedback and dynamically updates gain coefficients, facilitating rapid convergence to the target. In visual servoing experiments, the controller demonstrated strong performance across various tasks. Even when subjected to unknown external forces, the controller maintained robust performance in target tracking. The feasibility and effectiveness of the DSRE were further verified through ex vivo experiments. We posit that this novel system holds significant potential for clinical application in ESD surgeries.

Fractional-order elastic net regularization for identifying various types of unknown external forces

Force identification is an important and fundamental problem in various engineering fields, and several regularization techniques have been introduced to improve the ill-posedness of this problem. While these regularization techniques can achieve satisfactory identified accuracy, they are best suited for dealing with specific forms of external forces. The external force, in reality, is always unknown and needs to be identified, making it difficult to select an appropriate regularization technique in advance. To overcome this disadvantage, a novel regularization technique, i.e. fractional-order elastic net regularization is proposed for force identification in this study. Firstly, the relationship between acceleration response and coefficient vector of external force is established. Then, a weighted matrix is introduced to define the difference between measured and calculated acceleration responses. Meanwhile, the external force and coefficient vector are constrained by their respective norm penalties with the help of regularization parameters. As a result, an optimization problem is defined for force identification, and the corresponding geometric significance is analyzed to propose a selection way for the regularization parameters. Finally, both numerical simulations and experimental verifications are conducted to evaluate the effectiveness of the proposed method. Comparison with existing methods reveals that the proposed method has great identification accuracy and applicability for various types of external forces.

A Regularization-Free Scheme for Recovering Large External Forces of Higher-Order Nonlinear Evolution Equations

In this study, the inverse engineering problems of the Ostrovsky equation (OE), Kawahara equation (KE), modified Kawahara equation (mKE), and sixth-order Korteweg-de Vries (KdV) equation will be investigated numerically. An effective numerical approach to tackle these inverse Coriolis dispersion problems and the above-mentioned inverse problems are still not available. To use different boundary shape functions, we must deal with the boundary data, initial conditions, and terminal time conditions of the OE, KE, mKE, and sixth-order KdV equations. The unknown Coriolis dispersion of OE and unknown large external forces of those three equations can be retrieved through back-substitution of the solution into the OE, KE, mKE, and sixth-order KdV equations while we obtain the solution with the symmetry property by employing the boundary shape function scheme (BSFS). Five numerical experiments with noisy data are carefully validated and discussed.

Sliding-mode control of electro-hydraulic positioning tracking system combining K-observer and nonlinear disturbance observer

High-precision position control of hydraulic systems is of great significance in industrial applications. However, unknown external force disturbance, measurement noise, and other uncertain nonlinearities widely exist in electro-hydraulic servo systems, which severely degrade the system performance. To handle this problem, a sliding-mode controller based on K-observer with nonlinear disturbance observer is proposed for electro-hydraulic position systems in this article. The nonlinear disturbance observer is to estimate and eliminate the time-varying external force disturbance. The sliding-mode controller based on K-observer is to reach low gain observation of states and improve the system performance by reducing the chattering of the sliding-mode algorithm. The proposed controller is proved to be stable by the Lyapunov stability criterion. Comparative experiments support that the controller has high precision and strong robustness.

A nonlinear programming algorithm for finite element limit analysis using feasible arc searching technique

AbstractThis article presents a nonlinear programming algorithm for finite element limit analysis (FELA) based on feasible arc searching technique (FAST). The proposed algorithm has the potential to significantly reduce the iteration numbers required for convergence, making it a valuable tool for solving complex optimization problems from FELA. The algorithm also introduces several new features to the existing methods, including: (i) a novel method for determining a reasonable updating step length; (ii) the avoidance of solving an additional “phase one problem” for finding an initial feasible point; and (iii) the proposition of an empirical criterion for detecting infeasibility problems. The effectiveness of the proposed approach has been demonstrated through several classic examples derived from geotechnical engineering. The initial two examples show the superior convergence speed of the novel approach compared to existing methods. Additionally, the third example highlights the efficacy of the feasibility detection criterion for problems involving both prescribed and unknown external forces.

Optomechanical force sensor operating over wide detection range.

A detector with both broad operation range and high sensitivity is desirable in the measurement of weak periodic forces. Based on a nonlinear dynamical mechanism of locking the mechanical oscillation amplitude in optomechanical systems, we propose a force sensor that realizes the detection through the cavity field sidebands modified by an unknown external periodic force. Under the mechanical amplitude locking condition, the unknown external force happens to modify the locked oscillation amplitude linearly to its magnitude, thus achieving a linear scaling between the sideband changes read by the sensor and the magnitude of the force to be measured. This linear scaling range is found to be comparable to the applied pump drive amplitude, so the sensor can measure a wide range of force magnitude. Because the locked mechanical oscillation is rather robust against thermal perturbation, the sensor works well at room temperature. In addition to weak periodic forces, the same setup can as well detect static forces, though the detection ranges are much narrower.

A Novel Adaptive Finite-Time Position Tracking Control Strategy for Teleoperation System with Varying Communication Delays

Based on the traditional control approach, the position-tracking performance of the teleoperation system with communication delay is generally asymptotically stable. In practical applications, the closed-loop system is expected to achieve stable and finite-time convergence performance. A novel finite-time bilateral control scheme for a telerobotics system with communication delay is presented in this paper. On the basis of the traditional proportional damping injection control, this paper proposes and designs a new finite-time control method by introducing the non-integer power to the position error, velocity, and the combined error with position error and velocity. In comparison to existing proportional damping injection and finite-time control structures, the proposed method not only achieves the finite-time convergence performance of position tracking, but it also has the advantages of a simple structure and fewer gain coefficients. The controller also incorporates the radial basis function (RBF) neural network and adaptive approach to compensate unknown dynamics and external forces, thus also avoiding the measurement of force signals. The Lyapunov–Krasovskii function is then defined, and it is demonstrated that the position tracking of closed-loop teleoperation system has bounded stability and finite-time control performance. The simulation experiment is also performed, and the results further illustrated the bounded stability of the system. Moreover, compared to the position tracking errors of other non-finite-time control methods, it is demonstrated that the proposed finite-time control scheme has a faster convergence rate and higher convergence precision.

A Brownian–Markov stochastic model for cart-like wheeled mobile robots

Wheeled mobile robots are commonly used in a wide range of applications from automated warehouses to patrolling. A rigorous and accurate model describing their dynamics is then important for control and tracking. It is worth mentioning that the majority of these models in literature deal with deterministic settings. Therefore, they are not able to take into account stochastic uncertainty or non-predictable phenomena such as lateral grip loss. In this paper, we present a novel stochastic dynamic model which considers random perturbations, while also considering the effect of unknown dissipative external forces acting on wheeled mobile robots. In particular, our approach is based on a two-state hybrid system of Stochastic Differential Equations modeling the robot dynamics subject to Brownian motion noises, with transitions from one state to the other triggered by to a homogeneous Markov chain.

Modeling a Viscoelastic Support Considering Its Mass-Inertial Characteristics During Non-Stationary Vibrations of the Beam

Non-stationary loading of a mechanical system consisting of a hinged beam and additional support installed in the beam span was studied using a model of the beam deformation based on the Timoshenko hypothesis with considering rotatory inertia and shear. The system of partial differential equations describing the beam deformation was solved by expanding the unknown functions in the Fourier series with subsequent application of the integral Laplace transform. The additional support was assumed to be realistic rather than rigid. Thus it has linearly elastic, viscous, and inertial components. This means that the effect of a part of the support vibrating with the beam was considered such that their displacements coincide. The beam and additional support reaction were replaced by an unknown concentrated external force applied to the beam. This unknown reaction was assumed to be time-dependent. The time law was determined by solving the first kind of Volterra integral equation. The methodology of deriving the integral equation for the unknown reaction was explained. Analytic formulae and results of computations for specific numerical parameters were given. The impact of the mass value on the additional viscoelastic support reaction and the beam deflection at arbitrary points were determined. The research results of this paper can be helpful for engineers in designing multi-span bridges.

Identification of Time-Varying External Force Using Group Sparse Regularization and Redundant Dictionary.

How to accurately identify unknown time-varying external force from measured structural responses is an important engineering problem, which is critical for assessing the safety condition of the structure. In the context of a few available accelerometers, this paper proposes a novel time-varying external force identification method using group sparse regularization based on the prior knowledge in the redundant dictionary. Firstly, the relationship between time-varying external force and acceleration responses is established, and a redundant dictionary is designed to create a sparse expression of external force. Then, the relevance of atoms in the redundant dictionary is revealed, and this prior knowledge is used to determine the group structures of atoms. As a result, a force identification governing equation is formulated, and the group sparse regularization is reasonably introduced to ensure the accuracy of the identified results. The contribution of this paper is that the group structures of atoms are reasonably determined based on prior knowledge, and the complexity in the process for identifying external force from measured acceleration responses is reduced. Finally, the effectiveness of the proposed method is demonstrated by numerical simulations and an experimental structure. The illustrated results show that, compared with the force identification method based on the standard l1-norm regularization, the proposed method can further improve the identified accuracy of unknown external force and greatly enhance the computational efficiency for the force identification problem.

A learning-based model predictive control scheme and its application in biped locomotion

This paper proposes a learning-based model predictive control scheme. This scheme divides the predictive model into a known nominal model and an unknown model residual. Model residual is learned using Gaussian process regression. The learned stochastic model is solved quickly using differential dynamic programming, taking into account control input constraints. The simulation results show that compared with state of art optimal control methods, this scheme has good robustness to model residual, accelerates the solution of high-dimensional problems, and can strictly constrain the control inputs according to the actual situation. Based on this learning-based model predictive control scheme, this paper also proposes an online learning gait generator for the uncertainty problem in the locomotion control of biped robots. The zero moment point is strictly constrained during training to ensure safety. The simulation results show that the gait generator is robust to unknown load and unknown external force.

Online Gain Adaptation of Whole-Body Control for Legged Robots with Unknown Disturbances.

This paper proposes an online gain adaptation approach to enhance the robustness of whole-body control (WBC) framework for legged robots under unknown external force disturbances. Without properly accounting for external forces, the closed-loop control system incorporating WBC may become unstable, and therefore the desired task goals may not be achievable. To study the effects of external disturbances, we analyze the behavior of our current WBC framework via the use of both full-body and centroidal dynamics. In turn, we propose a way to adapt feedback gains for stabilizing the controlled system automatically. Based on model approximations and stability theory, we propose three conditions to ensure that the adjusted gains are suitable for stabilizing a robot under WBC. The proposed approach has four contributions. We make it possible to estimate the unknown disturbances without force/torque sensors. We then compute adaptive gains based on theoretic stability analysis incorporating the unknown forces at the joint actuation level. We demonstrate that the proposed method reduces task tracking errors under the effect of external forces on the robot. In addition, the proposed method is easy-to-use without further modifications of the controllers and task specifications. The resulting gain adaptation process is able to run in real-time. Finally, we verify the effectiveness of our method both in simulations and experiments using the bipedal robot Draco2 and the humanoid robot Valkyrie.

A Meshless Method for Retrieving Nonlinear Large External Forces on Euler-Bernoulli Beams

We retrieve unknown nonlinear large space-time dependent forces burdened with the vibrating nonlinear Euler-Bernoulli beams under varied boundary data, comprising two-end fixed, cantilevered, clamped-hinged, and simply supported conditions in this study. Even though some researchers used several schemes to overcome these forward problems of Euler-Bernoulli beams; however, an effective numerical algorithm to solve these inverse problems is still not available. We cope with the homogeneous boundary conditions, initial data, and final time datum for each type of nonlinear beam by employing a variety of boundary shape functions. The unknown nonlinear large external force can be recuperated via back-substitution of the solution into the nonlinear Euler-Bernoulli beam equation when we acquire the solution by utilizing the boundary shape function scheme and deal with a small-scale linear system to gratify an additional right-side boundary data. For the robustness and accuracy, we reveal that the current schemes are substantiated by comparing the recuperated numerical results of four instances to the exact forces, even though a large level of noise up to 50% is burdened with the overspecified conditions. The current method can be employed in the online real-time computation of unknown force functions in space-time for varied boundary supports of the vibrating nonlinear beam.

Learning-based Control for Passive Flexible Multirotors (PFMs) with Physical Interaction Abilities

Flexible, deforming aerial vehicles have introduced a paradigm shift in existing research by enabling stable aerial-physical interactions. These vehicles harvest interaction energies to undergo a deformation of the vehicle body and absorb impacts, therefore retaining stability under the action of unknown external forces. The low-level control of such aerial vehicles gets complicated owing to the highly nonlinear and varying dynamics of a passive deforming chassis, coupled with the action of external forces resulting from interactions with the environments. In this work, we propose a morphology-aware Q-learning-based tracking controller that accounts for varying morphology and the various external interaction forces. We train this learning-based controller offline under various case scenarios and then augment an online rollout scheme to improve the tracking performance in case of model-mismatch. Simulation studies are presented that validate the proposed controller in comparison with a conventional disturbance observer approach with improved tracking performance and optimized control effort.

Disturbance Observer-Based Force Estimation and Fault Detection for Robotic Manipulator in Radioactive Environments

Robotic systems with force sensing have great potential for use in radioactive environments. In this study, a modified observer-based method was developed to calculate the unknown external force without adding a redundant sensor and achieve the fault detection in the presence of a force sensor. A dynamic model of a serial robotic manipulator was built and the design procedure for a modified disturbance observer (MDO) was established. The output of observer was then used to suppress the disturbance and generate the fault signature. Moreover, the stability analysis shows that the convergence of the observer error is ultimately bounded. Simulation results under the step and composite sinusoidal disturbance torque demonstrate the performance of the force estimation and disturbance rejection. The results, obtained using the Kinova Jaco2 robot manipulator, show that the estimated errors of the external force in X-Y-Z direction are bounded within ±0.5 N, ±2 N, and ±3 N, respectively. Finally, the effectiveness of fault detection is also verified by the experiment results.

Синтез адаптивного скользящего режима управления полетом квадрокоптера в условиях переменной нагрузки и помех

Adaptive sliding control mode of an unmanned aerial vehicle (UAV) has been synthesized using neural networks to identify unknown factors acting on UAV. The MATLAB SIMULINK software was used to simulate the operation of the UAV control system. The simulation of the operation of the UAV location tracking system and UAV movement under the influence of unknown external forces of both constant magnitude and external forces varying according to a linear law is performed. An assessment of the performance of the UAV control system under the influence of random interference is given.

Energy Shaping Control of Hydraulic Soft Continuum Planar Manipulators

This letter investigates the model-based control of a class of soft continuum manipulators with hydraulic actuation that bend on a plane due to pressurization of one or more internal chambers. A port-Hamiltonian formulation is employed to describe the system dynamics, which includes the pressure dynamics of the hydraulic fluid. A new nonlinear control law is constructed with an energy-shaping approach, and it is combined with an adaptive observer to compensate the effect of unknown external forces. Stability conditions are investigated with a Lyapunov approach, and the effect of the tuning parameters and of key model parameters is discussed. The effectiveness of the controller is demonstrated with numerical simulations.

Mutual Skill Learning and Adaptability to Others via Haptic Interaction.

When learning a new skill through an unknown environment, should we practice alone, or together with another beginner, or learn from the expert? It is normally helpful to have an expert guiding through unknown environmental dynamics. The guidance from the expert is fundamentally based on mutual interactions. From the perspective of the beginner, one needs to face dual unknown dynamics of the environment and motor coordination of the expert. In a cooperative visuo-haptic motor task, we asked novice participants to bring a virtual mass onto the specified target location under an unknown external force field. The task was completed by an individual or with an expert or another novice. In addition to evaluation of the motor performance, we evaluated the adaptability of the novice participants to a new partner while attempting to achieve a common goal together. The experiment was set in five phases; baseline for skill transfer and adaptability, learning and evaluation for adaptability and skill transfer respectively. The performance of the participants was characterized by using the time to target, effort index, and length of the trajectory. Experimental results suggested that (1) peer-to-peer interactions among paired beginners enhanced the motor learning most, (2) individuals practicing on their own (learning as a single) showed better motor learning than practicing under the expert's guidance, and (3) regarding the adaptability, peer-to-peer interactions induced higher adaptability to a new partner than the novice-to-expert interactions while attempting to achieve a common goal together. Thus, we conclude that the peer-to-peer interactions under a collaborative task can realize the best motor learning of the motor skills through the new environmental dynamics, and adaptability to others in order to achieve a goal together. We suggest that the peer-to-peer learning can induce both adaptability to others and learning of motor skills through the unknown environmental dynamics under mutual interactions. On the other hand, during the peer-to-peer interactions, the novice can learn how to coordinate motion with his/her partner (even though one is a new partner), and thus, is able to learn the motor skills through new environmental dynamics.

ESO-Based Vibration Control for All-Clamped Plate Using an Electrodynamic Inertial Actuator

This paper proposes a disturbance rejection method with extended state observer (ESO) and a tracking differentiator (TD) to realize vibration suppression of all-clamped plate structure in the presence of lumped disturbance, i.e. internal dynamic uncertainties, unknown external forces and accelerometer measurement noises. First, the structure is modeled as two degrees of freedom system based on vibration characteristics. Second, an ESO is employed to ensure the vibration suppression performance by estimating the lumped disturbances and compensating these disturbances via real-time feedforward mechanism. Meanwhile, a TD is introduced to eliminate the influence of the measurement noises. Moreover, the stability of the closed-loop system is discussed in detail. Finally, the proposed controller is verified on the hardware-in-loop plat-form based on NI PCIe-6343 data acquisition card. Theoretical analysis and experimental results show that the proposed method possesses good vibration suppression performance.

Identification of model‐free hysteretic forces of magnetorheological dampers embedded in buildings under unknown excitations using incomplete structural responses

Magnetorheological (MR) damper is efficient to mitigate vibration of the structure subject to severe excitations. The identification of nonlinear characteristics of MR damper has attracted increasing attention. However, it is still challenging to identify model-free hysteresis of MR dampers embedded in structures using only incomplete measurements of structural responses under unknown excitations. In this paper, an identification technique is proposed for this tough task, namely, nonparametric identification of MR nonlinear restoring forces in building structures under unknown excitations. The proposed technique involves identifications in two stages. In the first stage, the identification of linear parameters of bare structure and MR dampers under low-level unknown excitations is conducted based on the generalized extended Kalman filter with unknown input (GEKF-UI) by the authors. In the second stage, the hysteretic forces of MR dampers are proposed to be treated as “unknown fictitious forces” to the corresponding linear bare structure identified in the first stage. The generalized Kalman filter with unknown inputs (GKF-UI) by the authors is adopted to identify all unknown inputs including the “unknown fictitious forces” originated from MR dampers. To demonstrate the proposed technique, some numerical examples are used to identify model-free hysteresis of single or multi MR dampers with different nonlinear restoring force models in shear frames under unknown external force excitations or unknown seismic excitations, respectively. Furthermore, experimental testing of the identification of model-free MR damper in a multi-story shear frame under unknown external excitation is conducted.