Mathematical Modeling,Control, Computer Simulation and Laboratory Experiments of a Spatial Servopneumatic Parallel Robot

In this paper (Part I), the special construction of a controlled parallel structure, called spatial servopneumatic multi-axles test facility (MAP), will be theoretically and numerically investigated and controlled. The investigations include the following steps: (i) design of mathematical models of different complexity of both, the test facility mechanics in terms of DAEs and ODEs (a multi-body system which contains 13 rigid bodies under spatial motion, connected by joints), and the servopneumatic actuators; (ii) construction of different linear and nonlinear (model based) control algorithms; (iii) development of a computer simulation program of the MAP; and (iv) evaluation of various computer simulation runs, obtained by applying different control algorithms and spatial command-input signals. The results show that the efficiency of the control algorithms is closely related to their complexity and cost. In order to check the closeness to reality of the computer simulation results and their benefit for industrial applications, laboratory experiments must be formed with the same MAP, the same control algorithms, and applying the same command-input signals which have been used in the computer simulations. These experimental investigations will be presented in Part II of the paper.     

Using data assimilation in numerical simulations of experimental geophysical flows

Data assimilation is used to couple numerical simulations and laboratory experiments of unsteady fluid flows in a stratified, rotating fluid. The experiments are performed on the large Coriolis turntable (Grenoble) and the simulations are performed with a multi-layer shallow water model. Sequential assimilation of high-resolution CIV (Correlation Image Velocimetry) measurements drives the numerical model close to the experimental flow and provides an estimation of all the flow variables at each time and each point. It is then possible (i) to analyse the flow dynamics in details, (ii) to determine the model errors starting from a realistic initial condition and (iii) to test the assimilation scheme when a reduced set of data is assimilated. To illustrate this, some results on the baroclinic instability of a two-layer vortex are presented. To cite this article: M. Galmiche et al., C. R. Mecanique 331 (2003).     

A unified solution to swing-up control for n-link planar robot with single passive joint based on virtual composite links and passivity

This paper concerns the swing-up control of an n-link revolute planar robot with any one of the joints being passive. The goal is to design and analyze a swing-up controller that can bring the robot into any arbitrarily small neighborhood of the upright equilibrium point, at which all the links are in the upright position. We present a unified solution based on the notion of virtual composite link (VCL), which is a virtual link made up of one or more active links. By using the angles of two series of VCLs separated by the passive joint and using the total mechanical energy of the robot, we design a swing-up controller and analyze the global motion of the robot under the controller. The main new results of this paper are: (1) we obtain a lower bound for each control gain related to the angle of each VCL such that the closed-loop system has only one undesired equilibrium point in addition to the upright equilibrium point, and we present an original proof of the conditions on the control gains for a class of n-link underactuation-degree-one planar robots with an active first joint; (2) we provide a bigger control gain region for achieving the control objective than those of previous results on three- and n-link robots with a passive first joint; (3) we validate the theoretical results via numerical simulations on a 4-link robot with the passive joint in each of the four positions. This paper gains an insight into the passivity-based control of underactuated multiple-DOF systems.     

ZMP based neural network inspired humanoid robot control

This paper concerns ZMP-based control that is inspired by artificial neural networks for humanoid robot walking on varying sloped surfaces. Humanoid robots are currently one of the most exciting research topics in the field of robotics, and maintaining stability while they are standing, walking or moving is a key concern. To ensure a steady and smooth walking gait of such robots, a feedforward type of neural network architecture, trained by the back-propagation algorithm, is employed. The inputs and outputs of the neural network architecture are the ZMPx and ZMPy errors of the robot, and the x, y positions of the robot, respectively. The neural network developed allows the controller to generate the desired balance of the robot positions, resulting in a steady gait for the robot as it moves around on a flat floor, and when it is descending or ascending slopes. In this paper, experiments of humanoid robot walking are carried out, in which the actual position data from a prototype robot are measured in real-time situations, and fed into a neural network inspired controller designed for stable bipedal walking. In addition, natural walking motions on the different surfaces with varying slopes are obtained and the performance of the resulting controller is shown to be satisfactory.     

Three-dimensional static and dynamic stiffness analyses of the cable driven parallel robot with non-negligible cable mass and elasticity

This study investigates the three-dimensional static and dynamic stiffness analyses of the cable driven parallel robot by considering cable mass, elasticity, and mass of end-effector. According to these models, optimization of cable tensions and cable lengths using fminimax solver is performed to determine the static stiffness. Static and dynamic stiffness of the cables are obtained with simulations. Results show that analyses in three-dimension are very important to measure the actual performance of the cable driven parallel robot. This demonstrates potential for general applicability and motion of the cable driven parallel robot.     

Neural networks based self-learning PID control of electronic throttle

An electronic throttle is a low-power DC servo drive which positions the throttle plate. Its application in modern automotive engines leads to improvements in vehicle drivability, fuel economy, and emissions. In this paper, a neural networks based self-learning proportional-integral-derivative (PID) controller is presented for electronic throttle. In the proposed self-learning PID controller, the controller parameters, K _P , K _I , and K _D are treated as neural networks weights and they are adjusted using a neural networks algorithm. The self-learning algorithm is operated iteratively and is developed using the Lyapunov method. Hence, the convergence of the learning algorithm is guaranteed. The neural networks based self-learning PID controller for electronic throttle is verified by computer simulations.     

State-Space Generalized Predictive Control for Redundant Parallel Robots

Abstract

The article deals with the design and properties of generalized predictive control (GPC) for path control of redundant parallel robots. Redundant parallel classification means redundant number of actuators, i.e., more actuators than degrees of freedom of the robot. Control of such structures suffers from several new control problems like potential inconsistency of steady state positions or nonuniqueness of control actions. The article explains classical direct derivation of GPC and its modification based on square root two-step design of control actions for solving the control problems. As an example for verification of algorithms, a prototype of a planar redundant parallel robot is used. Both design approaches are compared and several possibilities of extensions are presented for taking into consideration additional requirements, like smooth course of actuators or fulfillment of the anti-backlash condition.     

Dynamical analysis of an improved FitzHugh-Nagumo neuron model with multiplier-free implementation

The cubic-polynomial nonlinearity with N-shaped curve plays a crucial role in generating abundant electrical activities for the original FitzHugh-Nagumo (FHN) neuron model. The pioneer FHN neuron model is efficient in theoretical analysis and numerical simulation for these abundant electrical activities, but analog multipliers are indispensable in hardware implementation since the involvement of cubic-polynomial nonlinearity. Analog multiplier goes against the circuit integration of FHN neuron model due to its huge implementation costs. To avoid the involvement of analog multiplier in hardware implementation, a nonlinear function possessing N-shaped curve and multiplier-free implementation is presented in this paper. To confirm the availability of this nonlinear function in generating electrical activities, numerical simulations and hardware experiments are successfully executed on an improved two-dimensional (2D) FHN neuron model with externally applied stimulus. The results demonstrate that the improved FHN neuron model can generate rich electrical activities of periodic spiking behavior, chaotic behavior, and quasi-periodic behavior. Analog circuit implementation without any multiplier and its hardware experiment show the availability of the proposed nonlinear function, which is appropriate for analog circuit implementation of FHN neuron-based neuromorphic intelligence.

     

Fractional-order PD control at Hopf bifurcations in a fractional-order congestion control system

In this paper, we address the problem of the bifurcation control of a delayed fractional-order dual model of congestion control algorithms. A fractional-order proportional–derivative (PD) feedback controller is designed to control the bifurcation generated by the delayed fractional-order congestion control model. By choosing the communication delay as the bifurcation parameter, the issues of the stability and bifurcations for the controlled fractional-order model are studied. Applying the stability theorem of fractional-order systems, we obtain some conditions for the stability of the equilibrium and the Hopf bifurcation. Additionally, the critical value of time delay is figured out, where a Hopf bifurcation occurs and a family of oscillations bifurcate from the equilibrium. It is also shown that the onset of the bifurcation can be postponed or advanced by selecting proper control parameters in the fractional-order PD controller. Finally, numerical simulations are given to validate the main results and the effectiveness of the control strategy.

     

Mathematical Modeling and Parameter Identification of a Planar Servo-Pneumatic Test Facility. Part I: Mathematical Modeling and Computer Simulation

High quality multi-axis test facilities used for testing heavy loads and large structures of industrial equipment are usually simulated, designed and controlled based on reduced model equations neglecting the inertia properties of the actuators. The design and control of servo-pneumatic test facilities used for testing small and light structures must take into account extended test facility models including the various inertia properties of the actuators. In this paper (Part I) an extended test facility model is presented including the various inertia properties and joints of the actuators. These extended model equations are represented in a form well suited to be directly implemented in control algorithms based on exact linearization techniques for real time control. This is done by stepwise projecting the inertia properties of the various actuator housings and actuator pistons down to the common mass of the test table and payload. The resulting extended model equations have the same form as the reduced model equations. They only include more complex system matrices and vector functions. These compact model equations turn out to be suitable for an efficient nonlinear controller design of these test facilities. Computer simulations and associated laboratory experiments show the necessity to use extended model equations in case of testing small and light structures. In Part II of this paper [1] the inertia parameters of the planar test facility will be identified in laboratory experiments.     

Adaptive fuzzy formation control for a swarm of nonholonomic differentially driven vehicles

In this paper, an adaptive fuzzy robust H ^∞ controller is proposed for formation control of a swarm of differential driven vehicles with nonholonomic dynamic models. Artificial potential functions are used to design the formation control input for kinematic model of the robots and matrix manipulations are used to transform the nonholonomic model of each differentially driven vehicle into equivalent holonomic one. The main advantage of the proposed controller is the robustness to input nonlinearity, external disturbances, model uncertainties, and measurement noises, in a formation control of a nonholonomic robotic swarm. Moreover, robust stability proof is given using Lyapunov functions. Finally, simulation results are demonstrated for a swarm formation problem of a group of six unicycles, illustrating the effective attenuation of approximation error and external disturbances, even in the case of robot failure.     

Mathematical Modeling and Parameter Identification of a Planar Servo-Pneumatic Test Facility. Part II: Experimental Identification

The main objective of this paper is the identification of the inertia parameters of a rigid body under planar motion using a planar servo-pneumatic test facility designed for vibration tests. The hardware realization of the test facility used is discussed. The pneumatic components as well as the mechanical components of the test facility are described by linear and by nonlinear mathematical models, derived in Part I [1] of this paper. These model equations are used as identification hypotheses in the identification process. A comparison of time histories obtained by computer simulations of the nonlinear test facility model and by laboratory experiments shows that this nonlinear test facility model provides a realistic identification hypothesis for the estimation experiments. Based on different model hypotheses the inertia parameters of the test table and of the payload have been successfully identified from laboratory experiments. The relative estimation errors of the identified parameters are less than 10%.     

An adaptive critic neural network for motion control of a wheeled mobile robot

In this paper, we propose a new application of the adaptive critic methodology for the feedback control of wheeled mobile robots, based on a critic signal provided by a neural network (NN). The adaptive critic architecture uses a high-level supervisory NN adaptive critic element (ACE), to generate the reinforcement signal to optimise the associative search element (ASE), which is applied to approximate the non-linear functions of the mobile robot. The proposed tracking controller is derived from Lyapunov stability theory and can guarantee tracking performance and stability. A series of computer simulations have been used to emulate the performance of the proposed solution for a wheeled mobile robot.     

Thermally actuated shape-memory polymers: Experiments, theory, and numerical simulations

With the aim of developing a thermo-mechanically coupled large-deformation constitutive theory and a numerical-simulation capability for modeling the response of thermally actuated shape-memory polymers, we have (i) conducted large strain compression experiments on a representative shape-memory polymer to strains of approximately unity at strain rates of 10⁻³ and 10⁻¹ s⁻¹, and at temperatures ranging from room temperature to approximately 30 °C above the glass transition temperature of the polymer; (ii) formulated a thermo-mechanically coupled large-deformation constitutive theory; (iii) calibrated the material parameters appearing in the theory using the stress-strain data from the compression experiments; (iv) numerically implemented the theory by writing a user-material subroutine for a widely used finite element program; and (v) conducted representative experiments to validate the predictive capability of our theory and its numerical implementation in complex three-dimensional geometries. By comparing the numerically predicted response in these validation simulations against measurements from corresponding experiments, we show that our theory is capable of reasonably accurately reproducing the experimental results. As a demonstration of the robustness of the three-dimensional numerical capability, we also show results from a simulation of the shape-recovery response of a stent made from the polymer when it is inserted in an artery modeled as a compliant elastomeric tube.     

On the approximate zeroth and first‐order consistency in the presence of 2‐D irregular boundaries in SPH obtained by the virtual boundary particle methods

In this paper, a new method to impose 2‐D solid wall boundary conditions in smoothed particle hydrodynamics is presented. The wall is discretised by means of a set of virtual particles and is simulated by a local point symmetry approach. The extension of a previously published modified virtual boundary particle (MVBP) method guarantees that arbitrarily complex domains can be readily discretised guaranteeing approximate zeroth and first‐order consistency. To achieve this, three important new modifications are introduced: (i) the complete support is ensured not only for particles within one smoothing length distance, h, from the boundary but also for particles located at a distance greater than h but still within the support of the kernel; (ii) for a non‐uniform fluid particle distribution, the fictitious particles are generated with a uniform stencil (unlike the previous algorithms) that can maintain a uniform shear stress on a particle‐moving parallel to the wall in a steady flow; and (iii) the particle properties (density, mass and velocity) are defined using a local point of symmetry to satisfy the hydrostatic conditions and the Cauchy boundary condition for pressure. The extended MVBP model is demonstrated for cases including hydrostatic conditions for still water in a tank with a wedge and for curved boundaries, where significant improved behaviour is obtained in comparison with the conventional boundary techniques. Finally, the capability of the numerical scheme to simulate a dam break simulation is also shown. Copyright © 2015 John Wiley & Sons, Ltd.     

Computational micro-to-macro transitions of discretized microstructures undergoing small strains

Summary  The paper investigates algorithms for the computation of homogenized stresses and overall tangent moduli of microstructures undergoing small strains. Typically, these microstructures define representative volumes of nonlinear heterogeneous materials such as inelastic composites, polycrystalline aggregates or particle assemblies. We consider a priori given discretized microstructures, without focusing on details of specific discretization techniques in space and time. The key contribution of the paper is the construction of a family of algorithms and matrix representations of the overall properties of discretized microstructures. It is shown that the overall stresses and tangent moduli of a typical microstructure may exclusively be defined in terms of discrete forces and stiffness properties on the boundary. We focus on deformation-driven microstructures, where the overall macroscopic deformation is controlled. In this context, three classical types of boundary conditions are investigated: (i) linear displacements, (ii) constant tractions and (iii) periodic displacements and antiperiodic tractions. Incorporated by the Lagrangian multiplier method, these constraints generate three classes of algorithms for the computation of equilibrium states and the overall properties of microstructures. The proposed algorithms and matrix representations of the overall properties are formally independent of the interior spatial structure and the local constitutive response of the microstructure and are therefore applicable to a broad class of model problems. We demonstrate their performance for some representative model problems including elastic–plastic deformations of composite materials. Received 29 May 2001; accepted for publication 2 January 2002     

Apparatus for Testing Concrete under Multiaxial Compression at Elevated Temperature (mac2T)

This paper describes a new test facility for determining material mechanical properties of structural concrete. The novel facility subjects 100 mm cubic concrete specimens to true multiaxial compression (σ₁ ≠ σ₂ ≠ σ₃) up to 400 MPa at temperatures of up to 300°C. Forces are delivered through three independent loading frames equipped with servo-controlled hydraulic actuators creating uniform displacement boundary conditions via rigid platens. Specimen deformation is calculated from displacements measured to an accuracy of 10⁻⁶ m using a system of six laser interferometers. The combination of stiff loading frames, rigid platens, an accurate and reliable strain measurement system and a fast control system enables investigation of the material response in the post-peak range. The in-house developed control software allows complex multi-stage experiments involving (i) load and temperature cycling, (ii) small stress probes and (iii) arbitrary (pre-defined) loading paths. The program also enables experiments in which the values of the control parameters and the execution of the test sequences depend on the response of the specimen during the test. The capabilities of the facility are illustrated in this paper by experiments determining the effects of different heat-load regimes on the strength and stiffness of the material and tests identifying the tangent stiffness matrix of the material and the associated changes in the acoustic tensor under multiaxial compression.     

Nonlinear robust and optimal control of robot manipulators

In this paper, we propose a new optimal control method for robust control of nonlinear robot manipulators. Many industrial robot systems are required to perform relatively large angular movement with sufficient accuracy. In real circumstances, highly nonlinear manipulator dynamics and uncertainties such as unknown load placed on the manipulator, external disturbance, and joint friction make the precise control of manipulators a very challenging task. The main contribution of this work is to develop a new robust control strategy to accomplish the precise control of robot manipulators under load uncertainty using a nonlinear optimal control formulation and solution. This methodology is based on the underlying relation between the robust stability and performance optimality. A class of robust control problems can be transformed to an equivalent optimal control problem by incorporating the uncertainty bounds into the cost functional. The θ-D optimal control approach is utilized to find an approximate closed-form feedback solution to the resultant nonlinear optimal control problem via a perturbation process. Numerical simulations show that the proposed robust controller is able to control the robot manipulator precisely under large load variations.     

Adaptive robust control of Mecanum-wheeled mobile robot with uncertainties

This paper presents a novel implementation of an adaptive robust second-order sliding mode control (ARSSMC) on a mobile robot with four Mecanum wheels. Each wheel of the mobile robot is actuated by separate motors. It is the first time that higher-order sliding mode control method is implemented for the trajectory tracking control of Mecanum-wheeled mobile robot. Kinematic and dynamic modeling of the robot is done to derive an equation of motion in the presence of friction, external force disturbance, and uncertainties. In order to make the system robust, second-order sliding mode control law is derived. Further, adaptive laws are defined for adaptive estimation of switching gains. To check the tracking performance of the proposed controller, simulations are performed and comparisons of the obtained results are made with adaptive robust sliding mode control (ARSMC) and PID controller. In addition, a new and low-cost experimental approach is proposed to implement the proposed control law on a real robot. Experimental results prove that without compromising on the dynamics of the robot real-time implementation is possible in less computational time. The simulation and experimental results obtained confirms the superiority of ARSSMC over ARSMC and PID controller in terms of integral square error (ISE), integral absolute error (IAE), and integral time-weighted absolute error (ITAE), control energy and total variance (TV).