Internal Force Errors Research Articles

Bridging semiempirical and abinitio QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation.

Free energy simulations that employ combined quantum mechanical and molecular mechanical (QM/MM) potentials at abinitio QM (AI) levels are computationally highly demanding. Here, we present a machine-learning-facilitated approach for obtaining AI/MM-quality free energy profiles at the cost of efficient semiempirical QM/MM (SE/MM) methods. Specifically, we use Gaussian process regression (GPR) to learn the potential energy corrections needed for an SE/MM level to match an AI/MM target along the minimum free energy path (MFEP). Force modification using gradients of the GPR potential allows us to improve configurational sampling and update the MFEP. To adaptively train our model, we further employ the sparse variational GP (SVGP) and streaming sparse GPR (SSGPR) methods, which efficiently incorporate previous sample information without significantly increasing the training data size. We applied the QM-(SS)GPR/MM method to the solution-phase SN2 Menshutkin reaction, NH3+CH3Cl→CH3NH3++Cl-, using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. For 4000 configurations sampled along the MFEP, the iteratively optimized AM1-SSGPR-4/MM model reduces the energy error in AM1/MM from 18.2 to 4.4 kcal/mol. Although not explicitly fitting forces, our method also reduces the key internal force errors from 25.5 to 11.1 kcal/mol/Å and from 30.2 to 10.3 kcal/mol/Å for the N-C and C-Cl bonds, respectively. Compared to the uncorrected simulations, the AM1-SSGPR-4/MM method lowers the predicted free energy barrier from 28.7 to 11.7 kcal/mol and decreases the reaction free energy from -12.4 to -41.9 kcal/mol, bringing these results into closer agreement with their AI/MM and experimental benchmarks.

On rigid origami III: local rigidity analysis.

In this article, rigid origami is examined from the perspective of rigidity theory. First- and second-order rigidity are defined from local differential analysis of the consistency constraint; while the static rigidity and prestress stability are defined after finding the form of internal force and load. We will show the hierarchical relation among these local rigidities with examples representing different levels. The development of theory here follows the same path as the conventional rigidity theory for bar-joint frameworks, but starts with different high-order rotational constraints. We also bring new interpretation to the internal force and geometric error of constraints associated with energy. Examining the different aspects of the rigidity of origami might give a novel perspective for the development of new folding patterns, or for the design of origami structures where some rigidity is required.

A modified simplified analysis method to evaluate seismic responses of subway stations considering the inertial interaction effect of adjacent buildings

A modified simplified analysis method is proposed in this paper to evaluate seismic responses of subway stations in dense urban environments. Based on the dynamic analysis of the underground structure-soil-adjacent building interaction (SSSI) model, the seismic response of subway station was distinguished as the kinematic interaction (KI) effect (induced by the free-field movement) and the inertial interaction (II) effect (induced by the vibration of adjacent building). In order to comprehensively and simply consider the KI and II effects, a modified model was established after simplifying the analytical solution of underground structural seismic response. Owing to acquiring the dynamic response via static modelling and calculation, the method proposed herein requires a shorter analysis time and incurs a lower cost. In addition, a typical subway station-soil-ground building system in an east city in China was selected as the case-history to verify the modified method and further evaluate its computational accuracy. The traditional response displacement method and dynamic time history analysis method were also conducted in simulation as a comparison method and standard method, respectively. The case study demonstrates that referencing the results obtained by the dynamic time history analysis method (D-M), the maximum story drift and maximum internal force (including bending moment, axial force and shear force) errors of the traditional response displacement method (T-M) reached −28% and −48%, respectively. Obviously, this method is dangerous in designing due to its smaller computing results. In contrast, the modified method (M-M) which can accurately reflect the impacts of adjacent buildings, is a more accurate and safer analysis method (two seismic response indexes of the modified method generally exceeded the actual values, and more gratifying is that the errors were less than 12%). Therefore, the modified simplified analysis method proposed is a higher accuracy, safer design, less time consumed, and faster analysis process method, which can be used to substitute the traditional method in the aseismic performance and dynamic response analysis of subway stations in dense urban environments.

Dual feedforward neural networks based synchronized sliding mode controller for cooperative manipulator system under variable load and uncertainties

The problem of simultaneous position and internal force control is discussed with cooperative manipulators system under variable load and dynamic uncertainties in this study. A position synchronized sliding mode controller is proposed in the presence of variable load, as well as modeling uncertainties, joint friction, and external disturbances. To deal with the complex situation brought by variable load, virtual synchronization coupled errors are introduced for internal force tracking control and joint synchronization in the meantime. Dual feedforward neural networks are adopted, where a radial basis function-neural network based dynamic compensator and a radial basis function-neural network based internal force estimator are established, respectively, so that precise dynamic knowledge and force measurement are out of demand through their cooperation. Together with simulation studies and analysis, the position and internal force errors are shown to converge asymptotically to zero. Using Lyapunov stability approach, the proposed controller is proven to be robust in face of variable external load and the aforementioned uncertainties.

Adaptive backstepping control of multi-mobile manipulators handling a rigid object in coordination

This paper presents an adaptive backstepping control scheme applied to a group of mobile manipulator robots transporting a rigid object in coordination. All the dynamic parameters of the robotic system, including the handled object and the mobile manipulators, are assumed to be unknown but constant. The problem of uncertain parameters is resolved by using the virtual decomposition approach (VDC). This approach was originally applied to multiple manipulator robot systems. In this paper, the VDC approach is combined with backstepping control to ensure a good position tracking. The controller developed in this work ensures that the position error in the workspace converges to zero, and that the internal force error is bounded. The global stability of the entire system is proven based on the appropriate choice of Lyapunov function using virtual stability of each subsystem, based on the principle of the virtual work. An experimental validation is carried out for two mobile manipulators moving a rigid object in order to show the effectiveness of the proposed approach.

Adaptive backstepping control of multi-mobile manipulators handling a rigid object in coordination

This paper presents an adaptive backstepping control scheme applied to a group of mobile manipulator robots transporting a rigid object in coordination. All the dynamic parameters of the robotic system, including the handled object and the mobile manipulators, are assumed to be unknown but constant. The problem of uncertain parameters is resolved by using the virtual decomposition approach (VDC). This approach was originally applied to multiple manipulator robot systems. In this paper, the VDC approach is combined with backstepping control to ensure a good position tracking. The controller developed in this work ensures that the position error in the workspace converges to zero, and that the internal force error is bounded. The global stability of the entire system is proven based on the appropriate choice of Lyapunov function using virtual stability of each subsystem, based on the principle of the virtual work. An experimental validation is carried out for two mobile manipulators moving a rigid object in order to show the effectiveness of the proposed approach.

Coordinated Motion/Force Control of Multiarm Robot With Unknown Sensor Nonlinearity and Manipulated Object’s Uncertainty

To achieve satisfying motion/force objectives, it is required for the multiarm robot manipulation to tackle unknown nonlinearities and uncertainties. Existing control schemes have a requirement that the deadzone nonlinearity in the sensor channel could be ignored. However, the sensor deadzone is widely spread in real world applications; and its existence significantly limits the robotic performances. Moreover, the kinematics and dynamics of the manipulated object cannot be accurately known as the prior knowledge. Hence, such robotic systems may not be perfectly controlled by conventional approaches. In this paper, a coordinated motion/force control method is presented to handle unknown sensor deadzone and object’s uncertainty. The proposed method ensures the motion and internal force errors be bounded in a small neighborhood around the origin. Finally, comparative studies are presented to show the effectiveness and robustness of the proposed scheme.

Neural network-based position synchronised internal force control scheme for cooperative manipulator system

ABSTRACTMore complex problems of simultaneous position and internal force control occur with cooperative manipulator systems than that of a single one. In the presence of unwanted parametric and modelling uncertainties as well as external disturbances, a decentralised position synchronised force control scheme is proposed. With a feedforward neural network estimating engine, a precise model of the system dynamics is not required. Unlike conventional cooperative or synchronised controllers, virtual position and virtual synchronisation errors are introduced for internal force tracking control and task space position synchronisation. Meanwhile joint space synchronisation and force measurement are unnecessary. Together with simulation studies and analysis, the position and the internal force errors are shown to asymptotically converge to zero. Moreover, the controller exhibits different characteristics with selected synchronisation factors. Under certain settings, it can deal with temporary cooperation by an intelligent retreat mechanism, where less internal force would occur and rigid collision can be avoided. Using a Lyapunov stability approach, the controller is proven to be robust in face of the aforementioned uncertainties.

Simulation of Neural Network Based Adaptive Compensator Control Scheme for Multiple Mobile Manipulators with Uncertainties

Abstract This paper proposes an adaptive neural network based control scheme for coordinated multiple mobile manipulators in the presence of model uncertainties and external disturbances. A key feature of this scheme is that the prior information of the possible upper bound of the uncertainty is not required. A feedforward neural network (FFNN) is employed to learn this upper bound. The proposed controller not only achieves uniformly ultimate boundedness (UUB) for trajectory tracking error of the object but also keeps the internal force error under a bound. Simulation studies are carried out for coordinated two wheels driven mobile manipulators.

Neural-adaptive control of single-master-multiple-slaves teleoperation for coordinated multiple mobile manipulators with time-varying communication delays and input uncertainties.

In this paper, adaptive neural network control is investigated for single-master-multiple-slaves teleoperation in consideration of time delays and input dead-zone uncertainties for multiple mobile manipulators carrying a common object in a cooperative manner. Firstly, concise dynamics of teleoperation systems consisting of a single master robot, multiple coordinated slave robots, and the object are developed in the task space. To handle asymmetric time-varying delays in communication channels and unknown asymmetric input dead zones, the nonlinear dynamics of the teleoperation system are transformed into two subsystems through feedback linearization: local master or slave dynamics including the unknown input dead zones and delayed dynamics for the purpose of synchronization. Then, a model reference neural network control strategy based on linear matrix inequalities (LMI) and adaptive techniques is proposed. The developed control approach ensures that the defined tracking errors converge to zero whereas the coordination internal force errors remain bounded and can be made arbitrarily small. Throughout this paper, stability analysis is performed via explicit Lyapunov techniques under specific LMI conditions. The proposed adaptive neural network control scheme is robust against motion disturbances, parametric uncertainties, time-varying delays, and input dead zones, which is validated by simulation studies.

A Robust Hybrid Intelligent Position/Force Control Scheme for Cooperative Manipulators

We examine in this paper the complex problem of simultaneous position and internal force control in multiple cooperative manipulator systems. This is done in the presence of unwanted parametric and modeling uncertainties as well as external disturbances. A decentralized adaptive hybrid intelligent control scheme is proposed here. The controller makes use of a multi-input multi-output fuzzy logic engine and a systematic online adaptation mechanism. Unlike conventional adaptive controllers, the proposed controller does not require a precise dynamical model of the system's dynamics. As a matter of fact, the controller can achieve its control objectives starting from partial or no a priori knowledge of the system's dynamics. The ability to incorporate the already acquired knowledge about the system's dynamics is among what distinguishes the proposed controller from its predecessor adaptive fuzzy controllers. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying intensity levels of the aforementioned uncertainties. The position and the internal force errors are also shown to asymptotically converge to zero under such conditions

Robust computationally efficient control of cooperative closed-chain manipulators with uncertain dynamics

This article presents a decentralized control scheme for the complex problem of simultaneous position and internal force control in cooperative multiple manipulator systems. The proposed controller is composed of a sliding mode control term and a force robustifying term to simultaneously control the payload's position/orientation as well as the internal forces induced in the system. This is accomplished independently of the manipulators dynamics. Unlike most controllers that do not require prior knowledge of the manipulators dynamics, the suggested controller does not use fuzzy logic inferencing and is computationally inexpensive. Using a Lyapunov stability approach, the controller is proven to be robust in the face of varying system's dynamics. The payload's position/orientation and the internal force errors are also shown to asymptotically converge to zero under such conditions.

Semi-decentralized adaptive fuzzy control for cooperative multirobot systems with H/sup ∞/ motion/internal force tracking performance

We present a semi-decentralized adaptive fuzzy control scheme for cooperative multirobot systems to achieve H(infinity) performance in motion and internal force tracking. First, we reformulate the overall system dynamics into a fully actuated system with constraints. To cope with both parametric and nonparametric uncertainties, the controller for each robot consists of two parts: 1) model-based adaptive controller; and 2) adaptive fuzzy logic controller (FLC). The model-based adaptive controller handles the nominal dynamics which results in both zero motion and internal force errors for a pure parametric uncertain system. The FLC part handles the unstructured dynamics and external disturbances. An H(infinity) tracking problem defined by a novel performance criterion is given and solved in the sequel. Hence, a robust controller satisfying the disturbance attenuation is derived being simple and singularity-free. Asymptotic convergence is obtained when the fuzzy approximation error is bounded with finite energy. Maintaining the same results, the proposed controller is further simplified for easier implementation. Finally, the numerical simulation results for two cooperative planar robots transporting an object illustrate the expected performance.

Adaptive tracking control for cooperative multiple robot systems

This study presents a novel adaptive controller for cooperative multiple robot systems to obtain both zero motion and force tracking errors. First, by introducing the concepts of force decomposition and virtual control input, we reformulate the control problem of cooperative manipulators as a problem of controlling a single constrained manipulator. Next, a stable subspace is defined by both motion and internal force errors. This subspace includes both zero motion and force errors as its largest invariant set. The controller is then designed to drive the error dynamics into the stable subspace. A non‐adaptive and an adaptive approach both systematically show the asymptotic motion/internal force tracking. Note here that the zero force tracking error is achieved without needing the persistent excitation (PE) condition in the adaptive control scheme. Furthermore, based on a decentralized method, the proposed controllers are implemented in multiprocessor systems to balance the computation load. Finally, two cooperative planar robots are used to demonstrate the developed methodology where the simulation results illustrate the expected performance.

Robust control of multiple robots handling a constrained load

Multiple-robot systems are usually confronted with uncertainties, such as uncertainties in the manipulators and load parameters, and unmodelled dynamics. In this paper, the problem of controlling multiple manipulators handling a constrained load is addressed. A reduced-order dynamic model of the system is first derived, and several properties of this model are established. Using the reduced-order model, a robust control law is proposed. This controller guarantees the uniform ultimate boundedness of the position error, the internal-force error, and the constraint-force error. The proposed control law requires only the bounds on the uncertainties of the parameters. Simulation results of two planar robots moving a load along a horizontal plane are given to illustrate the theoretical developments.

Force/Position Learning Control of Multiple Robot Manipulators

Multiple robot manipulators are used in a coordinated way for carrying out tasks which cannot be performed by a single robot. When two or more robots handle a large object in free space, it is necessary to control the internal force applied on the object as well as the positions of the robot end effectors. In this paper, a learning control algorithm is proposed for the iterative-type position and force control of multiple manipulators carrying an object in free space. The learning control algorithm is of the D-type, and uses acceleration errors and internal force errors for learning. A proof of the convergence of the position and internal force trajectories to the desired trajectories is given. Simulation and experimental results are presented for two-robot manipulator systems to illustrate the effectiveness of the proposed learning controller.

Dynamic control of coordinated redundant robots with torque optimization

The problem of controlling a system of coordinated redundant robots with torque optimization based on joint redundancy is addressed. Local and global optimal control laws, both minimizing joint torque loading, are developed. A general method of load distribution among the coordinated robots is also proposed. The control problem is to regulate the motion of the object held by the coordinated robots and the internal force generated as a result of constraints on the object. The errors in the object motion and internal force converge asymptotically to zero under the proposed optimal control laws, when exact knowledge of the dynamic models is assumed. Furthermore, the robustness of the proposed method to model uncertainty is also analyzed. The motion and internal force errors are uniformly ultimately bounded under the proposed optimal controllers, when uncertainty in the dynamic models is assumed to exist.

An Adaptive Approach to Motion and Force Control of Multiple Coordinated Robots

In this paper an approach to motion and force control of multiple coordinated robots, based on an adaptive scheme, is developed. The approach can be used to control the motion of an object held by the robots, the contact force between the object and the environment, and the internal force which do not contribute to the object motion and contact force. Three subsystem error equations are generated, i.e., position error subsystem, contact force error subsystem, and internal force error subsystem. The adaptive law is derived to estimate the unknown parameters of the multiple coordinated robots, the object, and the environment in terms of the three error subsystem equations. The convergence of the position, contact, internal force errors, and parameter errors is analyzed based on the Lyapunov stability theory. The paper shows that the adaptive control scheme improves the position, and the internal and contact force tracking accuracy for a class of robotic systems with uncertain knowledge of the dynamic model.

Control of cooperative multiple flexible joint robots

The problem of controlling multiple flexible joint robots during cooperative manipulation of a rigidly grasped common load (denoted as the CMFJR problem) is addressed. The dynamic model of the CMFJR shows the interaction between the manipulators in the closed chain is analogous to a single manipulator interacting with frictionless (algebraic constraint) surface, while in motion along the surface. It is assumed that each flexible joint robot is equipped with joint velocity and position sensors and also actuator mounted position and velocity sensors. It is shown that it is not necessary to employ force sensors mounted on the robot wrist in order to track a desired load trajectory and an internal force trajectory. The transient response of the position error and the internal force error can be arbitrarily assigned even though the force sensors are not employed. >

Dynamic control of multiple coordinated redundant robots

An approach to the control of multiple coordinated redundant robots working in an environment with obstacles is developed in which the extended task space technique is presented for establishing the dynamic equations of the coordinated redundant robots using the redundant degrees of freedom. This approach can be used to control the motion of an object while avoiding obstacles. Three subsystem error equations are generated: a position error subsystem, an internal force error subsystem, and a constraint error subsystem. The adaptive law is derived to estimate the unknown parameters of the coordinated redundant robots and the object in terms of the three error subsystem equations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>