Virtual Input Research Articles

Flatness-based control in successive loops for dual-arm robotic manipulators

Dual-arm robotic manipulators are used in industry and for assisting humans since they enable dexterous handling of objects and more agile and secure execution of pick-and-place, grasping and lifting, or assembling tasks. In this article, a multi-loop flatness-based controller is proposed for the dynamic model of a dual-arm robot. In the considered application, the dual-arm robotic system has to lift and transfer an object under synchronized motion of its two end-effectors while it has also to compensate for the grasping forces between the two end-effectors and the object. The dynamic model of this robotic system is formulated while it is proven that the state-space description of the robot’s dynamics is differentially flat. The control problem for this robotic system is solved with the use of a flatness-based control approach which is implemented in successive loops. To apply the multi-loop flatness-based control method, the state-space model of the dual-arm robotic manipulator is separated into subsystems, which are connected in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. The state variables of the preceding i-th subsystem become virtual control inputs for the subsequent ( i + 1)-th subsystem. In turn, real control inputs are applied to the last subsystem. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The multi-loop flatness-based control approach is an exact nonlinear control scheme which (i) does not use approximate linearization of the dynamic model of the controlled system and (ii) does not need changes of state variables and complicated state-space model transformations.

Finite-time SMC-based admittance controller design of macro-micro robotic system for complex surface polishing operations

In the field of robotic polishing, achieving uniform material removal typically involves addressing the issue of constant contact force control. However, multi-source external disturbances in the polishing scenarios of complex workpiece surfaces can severely affect the robot’s force control accuracy. To enhance the responsiveness and disturbance rejection capabilities of robots in the compliant polishing process, this paper proposes an adaptive admittance controller with practical finite-time stability. A virtual control input is introduced into the basic admittance control framework in light of the state space theory, aiming to provide flexibility for common adaptive law designs. On this basis, a robust sliding mode control (SMC) algorithm is proposed to suppress external disturbances. The force tracking error is theoretically proven to achieve finite-time convergence when applying the proposed control strategy. Experimental results across various polishing scenarios demonstrate that, compared with the existing admittance control strategies, the proposed method can reduce fluctuations of the polishing force and improve the surface quality, thus verifying its effectiveness.

Computational techniques to monitoring fractional order type-1 diabetes mellitus model for feedback design of artificial pancreas

Background and objectives:In this paper, we developed a significant class of control issues regulated by nonlinear fractal order systems with input and output signals, our goal is to design a direct transcription method with impulsive instant order. Recent advances in the artificial pancreas system provide an emerging treatment option for type 1 diabetes. The performance of the blood glucose regulation directly relies on the accuracy of the glucose-insulin modeling. This work leads to the monitoring and assessment of comprehensive type-1 diabetes mellitus for controller design of artificial panaceas for the precision of the glucose-insulin glucagon in finite time with Caputo fractional approach for three primary subsystems. Methods:For the proposed model, we admire the qualitative analysis with equilibrium points lying in the feasible region. Model satisfied the biological feasibility with the Lipschitz criteria and linear growth is examined, considering positive solutions, boundedness and uniqueness at equilibrium points with Leray–Schauder results under time scale ideas. Within each subsystem, the virtual control input laws are derived by the application of input to state theorems and Ulam Hyers Rassias. Results:Chaotic Relation of Glucose insulin glucagon compartmental in the feasible region and stable in finite time interval monitoring is derived through simulations that are stable and bounded in the feasible regions. Additionally, as blood glucose is the only measurable state variable, the unscented power-law kernel estimator appropriately takes into account the significant problem of estimating inaccessible state variables that are bound to significant values for the glucose-insulin system. The comparative results on the simulated patients suggest that the suggested controller strategy performs remarkably better than the compared methods. Conclusion:In the model under investigation, parametric uncertainties are identified since the glucose, insulin, and glucagon system’s parameters are accurately measured numerically at different fractional order values. In terms of algorithm resilience and Caputo tracking in the presence of glucagon and insulin intake disturbance to maintain the glucose level. A comprehensive analysis of numerous difficult test issues is conducted in order to offer a thorough justification of the planned strategy to control the type 1 diabetes mellitus with designed the artificial pancreas.

Consensus for Heterogeneous Multiagent Systems: Output Rate-Coded Secure Control.

This article investigates an output rate-coded secure control based on backstepping for the consensus of heterogeneous multiagent systems (MASs) with output-triggering condition. Due to the nondifferentiable virtual control inputs caused by discontinuous triggering signals, it presents a technical obstacle in implementing the recursive backstepping, rendering previous results inapplicable. To address this problem, an auxiliary high-order filter is elaborately constructed to guarantee the required-order derivatives of virtual control inputs. As this filter is driven by local triggering consensus error, it allows our MASs to have different system orders and relative degrees. Besides, to reduce communication bit and strengthen communication security, we propose a novel distributed rate-coded algorithm from an encoding-decoding viewpoint. When it is triggered, agent's output is encrypted into an L -length codeword, then transmitted to neighbors without exposing any sensitive system states or real control inputs. It is shown that all the closed-loop system signals are ultimately bounded, and the mean square consensus tracking error can be reduced by appropriately selecting control parameters. Simulations illustrate our theoretical finding.

An asymmetrically variable wingtip anhedral angles morphing aircraft based on incremental sliding mode control: Improving lateral maneuver capability

This paper presents the design of an asymmetrically variable wingtip anhedral angles morphing aircraft, inspired by biomimetic mechanisms, to enhance lateral maneuver capability. Firstly, we establish a lateral dynamic model considering additional forces and moments resulting during the morphing process, and convert it into a Multiple Input Multiple Output (MIMO) virtual control system by importing virtual inputs. Secondly, a classical dynamics inversion controller is designed for the outer-loop system. A new Global Fast Terminal Incremental Sliding Mode Controller (NDO-GFTISMC) is proposed for the inner-loop system, in which an adaptive law is implemented to weaken control surface chattering, and a Nonlinear Disturbance Observer (NDO) is integrated to compensate for unknown disturbances. The whole control system is proven semi-globally uniformly ultimately bounded based on the multi-Lyapunov function method. Furthermore, we consider tracking errors and self-characteristics of actuators, a quadratic programming-based dynamic control allocation law is designed, which allocates virtual control inputs to the asymmetrically deformed wingtip and rudder. Actuator dynamic models are incorporated to ensure physical realizability of designed allocation law. Finally, comparative experimental results validate the effectiveness of the designed control system and control allocation law. The NDO-GFTISMC features faster convergence, stronger robustness, and 81.25% and 75.0% reduction in maximum state tracking error under uncertainty compared to the Incremental Nonlinear Dynamic Inversion Controller based on NDO (NDO-INDI) and Incremental Sliding Mode Controller based on NDO (NDO-ISMC), respectively. The design of the morphing aircraft significantly enhances lateral maneuver capability, maintaining a substantial control margin during lateral maneuvering, reducing the burden of the rudder surface, and effectively solving the actuator saturation problem of traditional aircraft during lateral maneuvering.

Feedback equivalence of the chained mechanical system to the almost linear form and its use for the sustainable multi-step walking design

The main theoretical novelty of this paper is the state and feedback equivalence of the underactuated 4-degrees of freedom planar walking-like mechanical chain system with 3 actuators to its 8-dimensional almost linear form with 3 virtual inputs. Moreover, the only residual nonlinearity vanishes on the 4-dimensional linear subspace being forward invariant when 2 of 3 virtual inputs are set to be zero. Dynamics inside that subsystem is actually the chain of 4 integrators fed by the remaining single virtual input and it can be interpreted as a rich variety of synchronous movements of torsos and legs. In such a way, the seemingly abstract and purely theoretical result can be used to design the walking-like movement during the single-support phase. The impact effect during the impulsive-like double-support phase is then attenuated by further special trajectories tuning and finite-time stabilization technique which provides the sustainable multi-step walking design. Moreover, the target walking-like trajectory is attracted by nearby trajectories. This further justify the importance and usefulness of the mentioned state and feedback equivalence. Its viability is further demonstrated by the simulations of various scenarios of the walking-like movement and the respective torsos behaviors.

Distributed safe formation tracking control of multiquadcopter systems using barrier Lyapunov function.

Coordinating the movements of a robotic fleet using consensus-based techniques is an important problem in achieving the desired goal of a specific task. Although most available techniques developed for consensus-based control ignore the collision of robots in the transient phase, they are either computationally expensive or cannot be applied in environments with dynamic obstacles. Therefore, we propose a new distributed collision-free formation tracking control scheme for multiquadcopter systems by exploiting the properties of the barrier Lyapunov function (BLF). Accordingly, the problem is formulated in a backstepping setting, and a distributed control law that guarantees collision-free formation tracking of the quads is derived. In other words, the problems of both tracking and interagent collision avoidance with a predefined accuracy are formulated using the proposed BLF for position subsystems, and the controllers are designed through augmentation of a quadratic Lyapunov function. Owing to the underactuated nature of the quadcopter system, virtual control inputs are considered for the translational (x and y axes) subsystems that are then used to generate the desired values for the roll and pitch angles for the attitude control subsystem. This provides a hierarchical controller structure for each quadcopter. The attitude controller is designed for each quadcopter locally by taking into account a predetermined error limit by another BLF. Finally, simulation results from the MATLAB-Simulink environment are provided to show the accuracy of the proposed method. A numerical comparison with an optimization-based technique is also provided to prove the superiority of the proposed method in terms of the computational cost, steady-state error, and response time.

Comprehensive Chassis Control Strategy Based on Coordination of Traction/Braking Distribution and Active Roll Control

A coordinated control of front/rear traction, four-wheel independent braking, and an active roll control system is proposed in this paper to improve overall vehicle cornering performance. The control algorithm is structured in a hierarchical form: the supervisory controller is used to calculate the desired yaw rate and roll angle based on the driver input and vehicle states; the upper-level controller is utilized to decide the target longitudinal tire forces, yaw moment, and anti-roll moment based on the sliding mode control method; and the control allocator is used to optimally map the virtual control input to the control commands of specific actuators. Co-simulations of MATLAB/Simulink and CarSim were conducted to verify the performance enhancement of the proposed controller. The results suggest that the proposed controller can effectively enhance the driving performance in limit cornering while maintaining lateral stability, and it reduces tire usage, as indicated by the index of tire dissipation energy when compared to control methods such as AWD, AWD-ESC, and ICC.

Improving small sample prediction performance via novel nonlinear interpolation virtual sample generation with self-supervised learning

In the early stages of production processes, due to expensive experimental costs and demanding experimental conditions, the amounts of collected samples are relatively limited. Information and knowledge extracted from minimal experimental data may be distorted and unreliable. Tackling this task, some researchers have recommended virtual sample generation (VSG), aiming at improving prediction accuracy of forecasting models for small sample sets by creating informative artificial samples. To more effectively capture nonlinear feature representations in small data, in this paper, we propose a promising VSG method based on self-supervised learning architecture to generate optimal, feasible virtual samples. The suggested technology considers distilling nonlinear feature representations using a manifold algorithm. Based on these representations, we integrated Newton’s divided difference and Chebyshev interpolation to create nonlinear interpolation points. We feed those interpolation points into a U-net model and an encoder-decoder net model to produce virtual sample inputs and output, respectively. Additionally, in this paper, we developed a Siamese network model to select virtual samples resembling original data. In the experiments, two industrial datasets were utilized to demonstrate efficacy of the proposed method. These experimental results show that the proposed method can more successfully boost prediction accuracy compared to the other three cutting-edge VSG methods.

Integral barrier Lyapunov function-based fixed-time integrated guidance and control with asymmetric field-of-view angle constraints

The missile integrated guidance and control (IGC) problem with seeker’s asymmetric field-of-view (FOV) angle constraints is addressed. In the introduced model, the fin deflections controller is used to drive the body line-of-sight angle rate, which avoids the solving and tracking of aerodynamic angles in traditional IGC method. A novel fixed-time convergence virtual input based on the integral barrier Lyapunov function is designed to ensure the asymmetric FOV angle constraints are never violated. The virtual input is tracked by a new proposed pre-defined fixed time controller with adjustable initial convergence speed. The lumped uncertainty including aerodynamic coefficient and target maneuvering is coped by the fixed-time disturbance observer. It is proved that the closed-loop system states are converged to the bounded region in a fixed-time and the asymmetric FOV angle constraints are satisfied. The 6-degree of freedom flight simulations and comparisons verified the advantages of the proposed algorithm.

Design and analysis of tracking differentiator based on SO(3)

Motivated by the issue of insufficient dynamic performance and tracking accuracy in SO(3)-based attitude tracking differentiators during large-angle maneuvers and complex trajectory tracking, a novel design approach for a three-degree-of-freedom attitude tracking differentiator within the SO(3) framework is proposed by incorporating second-order system theory and Lie group theory and improving the classical tracking differentiator. The kinematics model and error dynamics model of a rigid body on SO(3) are derived, and a reasonable virtual control input on SO(3) is constructed subsequently in order to achieve better dynamic response and tracking performance. Simulation and experimental results validate that the designed tracking differentiator could realize rapid and smooth convergence during large-angle maneuvers, and the initial large tracking error rapidly drops to near zero in a short period of time; additionally, it can also track expected time-varying curves well in complex trajectory tracking, with initial errors rapidly decreasing and maintaining at normal levels, demonstrating excellent tracking and control capabilities. There are strong application prospects for this new approach in addition to its theoretical significance.

Adaptive Optimal Tracking Control of an Underactuated Surface Vessel Using Actor-Critic Reinforcement Learning.

In this article, we present an adaptive reinforcement learning optimal tracking control (RLOTC) algorithm for an underactuated surface vessel subject to modeling uncertainties and time-varying external disturbances. By integrating backstepping technique with the optimized control design, we show that the desired optimal tracking performance of vessel control is guaranteed due to the fact that the virtual and actual control inputs are designed as optimized solutions of every subsystem. To enhance the robustness of vessel control systems, we employ neural network (NN) approximators to approximate uncertain vessel dynamics and present adaptive control technique to estimate the upper boundedness of external disturbances. Under the reinforcement learning framework, we construct actor-critic networks to solve the Hamilton-Jacobi-Bellman equations corresponding to subsystems of surface vessel to achieve the optimized control. The optimized control algorithm can synchronously train the adaptive parameters not only for actor-critic networks but also for NN approximators and adaptive control. By Lyapunov stability theorem, we show that the RLOTC algorithm can ensure the semiglobal uniform ultimate boundedness of the closed-loop systems. Compared with the existing reinforcement learning control results, the presented RLOTC algorithm can compensate for uncertain vessel dynamics and unknown disturbances, and obtain the optimized control performance by considering optimization in every backstepping design. Simulation studies on an underactuated surface vessel are given to illustrate the effectiveness of the RLOTC algorithm.

Neural Network Control of Underactuated Surface Vehicles With Prescribed Trajectory Tracking Performance.

This article is concerned with the fast and accurate trajectory tracking control problem for a sort of underactuated surface vehicle under model uncertainties and environmental disturbances. A novel neural networks (NNs)-based prescribed performance control strategy is proposed to solve the problem. In the control design, a new type of performance function is constructed which provides a way to predefine the settling time and accuracy, straightforward. Then, a pair of barrier functions are employed to combat not only the position error but also the virtual control input. This evades the possible singularity or discontinuity of the control solution. Next, an initialization technique is exploited, removing the requirement for the initial condition of the control system. Finally, two NNs are employed to deal with the unknown ship nonlinearities. The performance analysis not only demonstrates the effectiveness of the proposed approach but also reveals its robustness against disturbances and unknown reference trajectory derivatives. There is, thus, no need to acquire such knowledge or employ specialized tools to handle disturbances. The theoretical findings are illustrated by a simulation study.

Adaptive predefined-time tracking control of uncertain permanent-magnet synchronous motor drive chaotic system

A predefined-time tracking control algorithm is formulated for the Permanent Magnet Synchronous Motor(PMSM) driver system subjected to chaotic oscillations and undeterministic parameters, which can determine the upper bound of settling-time by controller gains. Radial Basis Function Neural Networks(RBFNNs) are employed to approximate the unknown nonlinear functions as universal approximators. The innovative predefined-time tracking differentiators are constructed to estimate the derivatives of the virtual inputs, resolving “the explosion of complexity” in traditional backstepping method. Then, an adaptive predefined-time controller is proposed in the backstepping framework. Lyapunov’s theoretical analysis proves that all signals in the closed-loop systems are uniformly ultimately bounded and the output tracking errors can converge to a small neighborhood of the origin within the predefined-time. Finally, simulation studies are performed to demonstrate that the proposed control scheme can quash the chaotic behaviors of the PMSM drive system and ensure fast-tracking performance even under unknown parameters.

Flatness‐based control in successive loops for mechatronic motion transmission systems

AbstractMechatronic systems with nonlinear dynamics are met in motion transmission applications for vehicles and robots. In this article, the control problem for the nonlinear dynamics of mechatronic motion transmission systems is solved with the use of a flatness‐based control approach which is implemented in successive loops. The state‐space model of these systems is separated into a series of subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system, and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. In this chain of subsystems, the state variables of the subsequent ( )‐th subsystem become virtual control inputs for the preceding ‐th subsystem and so on. In turn, exogenous control inputs are applied to the last subsystem and are computed by tracing backwards the virtual control inputs of the preceding subsystems. The whole control method is implemented in successive loops, and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in the following two case studies: (a) control of a permanent magnet linear synchronous motor (PMLSM)‐actuated vehicle's clutch and (ii) control of a multi‐Degrees of Freedom (multi‐DOF) flexible joint robot.

Fuzzy-tolerant-strategy for constrained nonlinear system via adaptive observer

This article considers the tracking control of nonlinear system with state constraints and faults. A fuzzy tolerant control approach on adaptive sliding-mode observer is proposed to accomplish good tracking performance. Firstly, for the purpose of getting the unknown state, a state estimator based on the fuzzy logic systems (FLSs) is designed. Secondly, a fuzzy observer by variable structure method is posed to approximate the faults, and the finite time convergence of observation error is proven. Furthermore, a two-layer nested adaptive law is constructed to improve the observer. Thirdly, a fuzzy backstepping tolerant controller based on super-twisting differentiator is proposed. In which, the barrier Lyapunov function is designed to counter the state constraints, and the super-twisting estimator is employed to estimate the derivative of virtual control input. Finally, two simulation results are analyzed to demonstrate the potential of the developed schemes.

Robust Control for Underactuated Fixed-Wing Unmanned Aerial Vehicles

Dynamic surface control (DSC) is a recognized nonlinear control approach for high-order systems. However, as the complexity of the system increases and the first-order filter (FOF) is introduced, there exists a singularity problem, i.e., the control input will reach infinity. This limits the application of the DSC algorithm to a class of real-world systems with complex dynamics. To address the problem of singularity, we present a novel DSC approach called nonsingular dynamic surface control (NDSC), which completely avoids the singularity problem and significantly improves the overall control performance. NDSC includes a nonsingular hypersurface, which is constructed by the error between system states and virtual control inputs. Then the nonsingular hypersurface will be applied to derive the corresponding control law with the aid of the DSC approach to ensure the output of the system can track arbitrary desired trajectories. NDSC has the following novel features: (1) finite time asymptotic stabilization can be guaranteed; (2) the performance of NDSC is insensitive to the FOF’s parameter variation once the maximum tracking error of FOF is bounded, which significantly reduces reliance on the control sampling frequency. We thoroughly evaluate the proposed NDSC algorithm in an unmanned aerial vehicle (UAV) system with an underactuated nature. Finally, the simulation results illustrate and highlight the effectiveness and superiority of the proposed control algorithm.

VR interactive input system based on INS and binocular vision fusion

In virtual reality interactive input systems, real-time and accurate spatial position and pose measurement is key to achieving a natural user interface. This study explores the application of inertial measurement units and binocular vision fusion technology in virtual reality interactive input systems, with the aim of improving the tracking accuracy of the system through optimized pose models and visual algorithms. A virtual reality measurement technology that integrates inertial measurement units and binocular vision is proposed by using an improved Kalman filtering algorithm to process inertial measurement units data, and combining it with the SURF algorithm optimized binocular vision system. Experimental results showed that fusion technology could reduce sensor noise and bias, improve the accuracy of pose estimation, and promote stable and robust motion tracking in virtual reality systems. In the angle range of -90 ° to 90 °, the average absolute error of the fusion system compared to the simple inertial measurement units pose calculation decreased from 3.371 ° to 1.369 ° In the experiment of measuring distance with a binocular vision system, the average absolute error value decreased to 1.532 mm, and the error range of the marker within the distance range of 500 mm to 650 mm was controlled within -1.3 mm to 1.2 mm. This study provides an effective solution for achieving high-precision virtual reality interactive input systems and is meaningful for the advancement of virtual reality technology.

Flatness-based control in successive loops for VSI-fed PM synchronous motors and induction motors

Electric traction systems consisting of inverter controlled three-phase motors are widely used in electric vehicles. In this article the control problem for the nonlinear dynamics of Voltage Source Inverter-fed synchronous and asynchronous motors is solved with the use of a flatness-based control approach which is implemented in successive loops. The state–space model of these systems is separated into a series of subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearised flat systems. In this chain of i = 1 , 2 , … , N subsystems, the state variables of the subsequent (i + 1th) subsystem become virtual control inputs for the preceding (ith) subsystem, and so on. In turn, exogenous control inputs are applied to the last subsystem and are computed by tracing backwards the virtual control inputs of the preceding N−1 subsystems. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in two case studies: (a) control of a Voltage Source Inverter-fed Permanent Magnet Synchronous Motor (VSI-fed PMSM), (ii) control of a Voltage Source Inverter-fed Induction Motor (VSI-fed IM).

Are Brazilian Higher Education Institutions Efficient in Their Graduate Activities? A Two-Stage Dynamic Data-Envelopment-Analysis Cooperative Approach

Higher education evaluation presents itself as a worldwide trend. It aims to improve performance due to its importance for economic and personal growth. Graduate activities are essential for Brazilian research and innovation systems. However, previous studies have disregarded the importance of this educational level and have evaluated efficiency by jointly considering teaching and research or only undergraduate courses. Therefore, this study contributes to Brazilian reality by proving a national graduate activities efficiency evaluation that considers them as a two-stage system (formative and scientific production stages). The study provides three main methodological contributions by presenting a new centralized two-stage dynamic network data envelopment analysis (DNDEA) model with shared resources. Besides measuring efficiency, an efficiency decomposition based on a leader–follower assumption shows managers how much efficiency can alter when one of the stages needs to be prioritized. Finally, a new framework based on modified virtual inputs and outputs provides a bi-dimensional representation of the efficiency frontier. Results indicate the usefulness of the approach for ranking universities, and the need to improve scientific production, highlighting the negative impacts of COVID-19 on the formative process efficiency and showing no significant regional discrepancies regarding performance.