Showing papers in "Journal of Dynamic Systems Measurement and Control-transactions of The Asme in 2018"

Axial Vibration Suppression in a PDE Model of Ascending Mining Cable Elevator

Abstract: Lifting up a cage with miners via a mining cable causes axial vibrations of the cable. These vibration dynamics can be described by a coupled wave partial differential equation-ordinary differential equation (PDE-ODE) system with a Neumann interconnection on a time-varying spatial domain. Such a system is actuated not at the moving cage boundary, but at a separate fixed boundary where a hydraulic actuator acts on a floating sheave. In this paper, an observer-based output-feedback control law for the suppression of the axial vibration in the varying-length mining cable is designed by the backstepping method. The control law is obtained through the estimated distributed vibration displacements constructed via available boundary measurements. The exponential stability of the closed-loop system with the output-feedback control law is shown by Lyapunov analysis. The performance of the proposed controller is investigated via numerical simulation, which illustrates the effective vibration suppression with the fast convergence of the observer error.

Kalman Filter and Its Modern Extensions for the Continuous-Time Nonlinear Filtering Problem

Abstract: This paper is concerned with the filtering problem in continuous time. Three algorithmic solution approaches for this problem are reviewed: (i) the classical Kalman–Bucy filter, which provides an exact solution for the linear Gaussian problem; (ii) the ensemble Kalman–Bucy filter (EnKBF), which is an approximate filter and represents an extension of the Kalman–Bucy filter to nonlinear problems; and (iii) the feedback particle filter (FPF), which represents an extension of the EnKBF and furthermore provides for a consistent solution in the general nonlinear, non-Gaussian case. The common feature of the three algorithms is the gain times error formula to implement the update step (to account for conditioning due to the observations) in the filter. In contrast to the commonly used sequential Monte Carlo methods, the EnKBF and FPF avoid the resampling of the particles in the importance sampling update step. Moreover, the feedback control structure provides for error correction potentially leading to smaller simulation variance and improved stability properties. The paper also discusses the issue of nonuniqueness of the filter update formula and formulates a novel approximation algorithm based on ideas from optimal transport and coupling of measures. Performance of this and other algorithms is illustrated for a numerical example.

Redundant MEMS-Based Inertial Navigation Using Nonlinear Observers

Abstract: We present two alternative methods for fault detection and isolation (FDI) with redundant MEMS inertial measurement units (IMUs) in inertial navigation systems (INS) based on nonlinear observers. The first alternative is based on the parity space method, while the second is expanded with quaternion-based averaging and FDI. Both alternatives are implemented and validated using data gathered in a full-scale experiment on an offshore vessel. Data from three identical MEMS IMUs and the vessel’s own industrial sensors is used to verify the methods’ FDI capabilities. The results reveal that when it comes to FDI of the IMUs’ angular rate sensors, there are differences between the two methods. The navigation algorithm based on quaternion weighting is essentially unaffected by the failure of an angular rate sensor, while the parity-space-method-based alternative experiences a perturbation. Nomenclature {b} BODY coordinate frame {t} Earth fixed tangent frame {e} Earth Centered Earth Fixed (ECEF) coordinate frame {i} Earth Centered Inertial (ECI) coordinate frame Rb Rotation matrix from frame {b} to frame {t} qb Unit quaternion representation of rotation from {b} to {t} ωib Angular velocity of {b} relative {i}, decomposed in {b} f ib Specific force of {b} decomposed in {t} ptb Position of {b} relative {t} decomposed in {t} vtb Velocity of {b} relative {t} decomposed in {t} φ,θ,ψ Euler angles: Roll, pitch, yaw μ Latitude λ Longitude S(·) Skew symmetric matrix such that S(z1)z2 = z1× z2 ‖ · ‖2 Euclidean vector norm In n×n identity matrix ⊗ Hamiltonian quaternion product TMO Translational motion observer NLO Nonlinear observer FDI Fault detection and isolation MEMS Microelectromechanical system IMU Inertial measurement unit INS Inertial navigation system

Uncertainty and Disturbance Estimator-Based Robust Trajectory Tracking Control for a Quadrotor in a Global Positioning System-Denied Environment

Abstract: This paper addresses the problem of autonomous trajectory tracking control for a quadrotor in a global positioning system (GPS)-denied environment using only onboard sensing. To achieve that goal, it requires accurate estimation of quadrotor states followed by proper control actions. For the position estimation in a GPS-denied environment, an open source high speed optical flow sensor PX4FLOW is adopted. As for the quadrotor control, there are several challenges due to its highly nonlinear system dynamics, such as underactuation, coupling, model uncertainties, and external disturbances. To deal with those challenges, the cascaded inner–outer uncertainty and disturbance estimator (UDE)-based robust control scheme has been developed and applied to the attitude and position control of a quadrotor. Extensive real flight experiments, including attitude stabilization, hover, disturbance rejection, trajectory tracking, and comparison with the proportional–integral–derivative (PID) controller are carried out to demonstrate the effectiveness of the developed UDE-based controllers. [DOI: 10.1115/1.4037736]

Multivariable Extremum Seeking for Joint-Space Trajectory Optimization of a High-Degrees-of-Freedom Robot

Abstract: In this paper, a novel analytical coupled trajectory optimization of a seven degrees-of-freedom (7DOF) Baxter manipulator utilizing extremum seeking (ES) approach is presented. The robotic manipulators are used in network-based industrial units, and even homes, by expending a significant lumped amount of energy, and therefore, optimal trajectories need to be generated to address efficiency issues. These robots are typically operated for thousands of cycles resulting in a considerable cost of operation. First, coupled dynamic equations are derived using the Lagrangian method and experimentally validated to examine the accuracy of the model. Then, global design sensitivity analysis is performed to investigate the effects of changes of optimization variables on the cost function leading to select the most effective ones. We examine a discrete-time multivariable gradient-based ES scheme enforcing operational time and torque saturation constraints in order to minimize the lumped amount of energy consumed in a path given; therefore, time-energy optimization would not be the immediate focus of this research effort. The results are compared with those of a global heuristic genetic algorithm (GA) to discuss the locality/globality of optimal solutions. Finally, the optimal trajectory is experimentally implemented to be thoroughly compared with the inefficient one. The results reveal that the proposed scheme yields the minimum energy consumption in addition to overcoming the robot's jerky motion observed in an inefficient path.

Adaptive Boundary Control for a Flexible Manipulator With State Constraints Using a Barrier Lyapunov Function

Abstract: In this paper, the problem of state constraints control is investigated for a class of output constrained flexible manipulator system with varying payload. The dynamic behavior of the flexible manipulator is represented by partial differential equations. To prevent states of the flexible manipulator system from violating the constraints, a Barrier Lyapunov Function which grows to infinity whenever its arguments approach to some limits is employed. Then based on the Barrier Lyapunov Function, boundary control laws are given. To solve the problem of varying payload, an adaptive boundary controller is developed. Furthermore, based on the theory of Barrier Lyapunov Function and the adaptive algorithm, state constraints and output control under vibration condition can be achieved. The stability of the closed-loop system is carried out by the Lyapunov stability theory. Numerical simulations are given to illustrate the performance of the closed-loop system.

Experimental Validation of Graph-Based Hierarchical Control for Thermal Management

Abstract: This paper proposes and experimentally validates a hierarchical control framework for fluid flow systems performing thermal management in mobile energy platforms. A graph-based modeling approach derived from the conservation of mass and energy inherently captures coupling within and between physical domains. Hydrodynamic and thermodynamic graph-based models are experimentally validated on a thermal-fluid testbed. A scalable hierarchical control framework using the graph-based models with model predictive control (MPC) is proposed to manage the multidomain and multi-timescale dynamics of thermal management systems. The proposed hierarchical control framework is compared to decentralized and centralized benchmark controllers and found to maintain temperature bounds better while using less electrical energy for actuation.

On Tightly Bounding the Dubins Traveling Salesman's Optimum

Abstract: The Dubins Traveling Salesman Problem (DTSP) has generated significant interest over the last decade due to its occurrence in several civil and military surveillance applications. Currently, there is no algorithm that can find an optimal solution to the problem. In addition, relaxing the motion constraints and solving the resulting Euclidean TSP (ETSP) provides the only lower bound available for the problem. However, in many problem instances, the lower bound computed by solving the ETSP is far below the cost of the feasible solutions obtained by some well-known algorithms for the DTSP. This article addresses this fundamental issue and presents the first systematic procedure for developing tight lower bounds for the DTSP.

Uncertainty and Disturbance Estimator-Based Backstepping Control for Nonlinear Systems With Mismatched Uncertainties and Disturbances

Abstract: This paper proposes an uncertainty and disturbance estimator (UDE)-based controller for nonlinear systems with mismatched uncertainties and disturbances, integrating the UDE-based control and the conventional backstepping scheme. The adoption of the backstepping scheme helps to relax the structural constraint of the UDE-based control. Moreover, the reference model design in the UDE-based control offers a solution to address the “complexity explosion” problem of the backstepping approach. Furthermore, the strict-feedback form condition in the conventional backstepping approach is also relaxed by using the UDE-based control to estimate and compensate “disturbance-like” terms including nonstrict-feedback terms and intermediate system errors. The uniformly ultimate boundedness of the closed-loop system is analyzed. Both numerical and experimental studies are provided.

Robust Adaptive Impedance Control With Application to a Transfemoral Prosthesis and Test Robot

Abstract: This paper presents, compares, and tests two robust model reference adaptive impedance controllers for a three degrees-of-freedom (3DOF) powered prosthesis/test robot. We first present a model for a combined system that includes a test robot and a transfemoral prosthetic leg. We design these two controllers, so the error trajectories of the system converge to a boundary layer and the controllers show robustness to ground reaction forces (GRFs) as nonparametric uncertainties and also handle model parameter uncertainties. We prove the stability of the closed-loop systems for both controllers for the prosthesis/test robot in the case of nonscalar boundary layer trajectories using Lyapunov stability theory and Barbalat's lemma. We design the controllers to imitate the biomechanical properties of able-bodied walking and to provide smooth gait. We finally present simulation results to confirm the efficacy of the controllers for both nominal and off-nominal system model parameters. We achieve good tracking of joint displacements and velocities, and reasonable control and GRF magnitudes for both controllers. We also compare performance of the controllers in terms of tracking, control effort, and parameter estimation for both nominal and off-nominal model parameters.

Improving the Energy Efficiency of a Hydraulic Press Via Variable-Speed Variable-Displacement Pump Unit

Abstract: Hydraulic presses are widely applied in various forming processes to manufacture products with complex shapes, however, they are energy-intensive. In order to lower the energy consumption, a variable-speed variable-displacement pump unit (SVVDP) was developed for hydraulic presses, where the flow rate required by the press in a forming process can be realized by changing the motor rotating speed and the pump displacement simultaneously. A theoretical model was built to reveal the energy dissipation behavior of the drive unit, which shows that the energy efficiency of the drive unit can be optimized by varying the rotating speed of the motor under a variety of load conditions. An experimental platform with a SVVDP was established to find the optimum rotating speed and the corresponding displacement in different load conditions, and experimental results verified the improved energy efficiency of the SVVDP compared with that of the commonly used single variable drive unit. By employing the strategy that the determined optimum rotating speeds in different load conditions were preset as recommended values for the drive unit working in different operations, the proposed drive unit was applied to a press completing a forming process and the results indicate significant energy saving potentials.

Adaptive Control of a Robot-Assisted Tele-Surgery in Interaction With Hybrid Tissues

Abstract: In a haptic teleoperation system, which interacts with unknown and hybrid environments, it is important to achieve stability and transparency. In medical usages, the utilization of knowledge on the tissues behavior in a controller design can improve the performance of the surgery in a robot-assisted telesurgery. Simultaneous interaction with hard and soft tissues makes it difficult to achieve stability and transparency. To deal with this difficulty, two controller schemes are designed. At first, a nonlinear mathematical model (inspired by the Hunt-Crossley (HC) model), which has the properties of soft and hard tissues, is combined with the slave side dynamic. In the second approach, the reaction force applied by hybrid tissues during the transition between tissues of different properties is modeled as an unknown force acting on the slave side. In a four-channel (4-CH) architecture, nonlinear adaptive controllers are designed without any knowledge about the parameters of the master, the slave robot, and the environment. For both control schemes, Lyapunov candidate functions provide a way to ensure the stability and transparency in the presence of uncertainties. The testbed comprises two Novint Falcon robots functioning as master and slave robots. Moreover, the experiments are performed on various objects, including a soft cube, a hard cube, and a phantom tissue. This paper rigorously evaluates the performances of the proposed methods, comparing them with each other and other previous schemes. Experimental and numerical results demonstrate the effectiveness of the proposed control schemes.