Research Papers

J. Dyn. Sys., Meas., Control. 2016;138(9):091001-091001-14. doi:10.1115/1.4033074.

This paper proposes a new integrated design method to simultaneously optimize the coupled structural parameters and controllers of mechanical systems by combining decentralized control techniques and Riccati-based control theories. The proposed integrated design method aims at minimizing the closed-loop H2 norm from the disturbance to the system cost. In this paper, the integrated design problems have been formulated in the cases of full state-feedback controllers and full order output-feedback controllers. We extend the current linear time invariant (LTI) control system to a more general framework suitable for the needs of integrated design, where the structural design is treated as a passive control optimization tackled by decentralized control techniques with static output feedback, while the active controller is optimized by solving modified Riccati equations. By using this dual-loop control system framework, the original integrated design problem is transferred to a constrained structural design problem with some additional Riccati-equation based constraints simultaneously integrating the controller synthesis. This reduces the independent design variables from the structural design parameters and the parameters of the controller to the structural design parameters only. As a result, the optimization efficiency is significantly improved. Then the constrained structural design problem is reformed as an unconstrained optimization problem by introducing Lagrange multipliers and a Lagrange function. The corresponding optimal conditions for the integrated design are also derived, which can be efficiently solved by gradient-based optimization algorithms. Later, two design examples, an active–passive vehicle suspension system and an active–passive tuned mass damper (TMD) system, are presented. The improvement of the overall system performance is also presented in comparison with conventional design methods.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(9):091002-091002-9. doi:10.1115/1.4033075.

We present analytical and numerical techniques to accurately calculate the shifts in the natural frequencies of electrically actuated micro and nano (carbon nanotubes (CNTs)) cantilever beams implemented as resonant sensors for mass detection of biological entities, particularly Escherichia coli (E. coli) and prostate specific antigen (PSA) cells. The beams are modeled as Euler–Bernoulli beams, including the nonlinear electrostatic forces and the added biological cells, which are modeled as discrete point masses. The frequency shifts due to the added masses of the cells are calculated for the fundamental and higher-order modes of vibrations. Analytical expressions of the natural frequency shifts under a direct current (DC) voltage and an added mass have been developed using perturbation techniques and the Galerkin approximation. Numerical techniques are also used to calculate the frequency shifts and compared with the analytical technique. We found that a hybrid approach that relies on the analytical perturbation expression and the Galerkin procedure for calculating accurately the static behavior presents the most computationally efficient approach. We found that using higher-order modes of vibration of micro-electro-mechanical-system (MEMS) beams or miniaturizing the sizes of the beams to nanoscale leads to significant improved frequency shifts, and thus increased sensitivities.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(9):091003-091003-7. doi:10.1115/1.4033273.

The traditional linear quadratic (LQ) controller can give optimal performance to a known linear system with weightings in the time domain, while the frequency shaped LQ (FSLQ) controller is able to provide optimal performance to the same class of systems with weightings in the frequency domain. When the system contains uncertainties, both of these two approaches fail. In this paper, an adaptive controller is proposed to an uncertain mechanical system such that LQ performance can be achieved with weightings in the frequency domain. The function approximation technique is applied to represent the uncertainties into a finite combination of a set of known basis functions. This allows the system to be with various nonlinearities and uncertainties without significant impact on the design procedure. The Lyapunovlike analysis is used to ensure convergence of the system output and boundedness of the internal signals. A dual stage is built to evaluate the performance of the proposed scheme experimentally.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(9):091004-091004-9. doi:10.1115/1.4033318.

This paper presents a robust-optimal fuzzy controller for position and attitude stabilization and vibration suppression of a flexible spacecraft during antenna retargeting maneuver. The fuzzy controller is based on Takagi–Sugeno (T–S) fuzzy model and uses the parallel distributed compensator (PDC) technique to quadratically stabilize the closed-loop system. The proposed controller is robust to parameter and unstructured uncertainties of the model. We improve the performance and the efficiency of the controller by minimizing the upper bound of the actuator's amplitude and maximizing the uncertainties terms included in the T–S fuzzy model. In addition to actuator amplitude constraint, a fuzzy model-based observer is considered for estimating unmeasurable states. Using Lyapunov stability theory and linear matrix inequalities (LMIs), we formulate the problem of designing an optimal-robust fuzzy controller/observer with actuator amplitude constraint as a convex optimization problem. Numerical simulation is provided to demonstrate and compare the stability, performance, and robustness of the proposed fuzzy controller with a baseline nonlinear controller.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(9):091005-091005-10. doi:10.1115/1.4033311.

The problem of maneuvering a vehicle through a race course in minimum time requires computation of both longitudinal (brake and throttle) and lateral (steering wheel) control inputs. Unfortunately, solving the resulting nonlinear optimal control problem is typically computationally expensive and infeasible for real-time trajectory planning. This paper presents an iterative algorithm that divides the path generation task into two sequential subproblems that are significantly easier to solve. Given an initial path through the race track, the algorithm runs a forward–backward integration scheme to determine the minimum-time longitudinal speed profile, subject to tire friction constraints. With this fixed speed profile, the algorithm updates the vehicle's path by solving a convex optimization problem that minimizes the resulting path curvature while staying within track boundaries and obeying affine, time-varying vehicle dynamics constraints. This two-step process is repeated iteratively until the predicted lap time no longer improves. While providing no guarantees of convergence or a globally optimal solution, the approach performs very well when validated on the Thunderhill Raceway course in Willows, CA. The predicted lap time converges after four to five iterations, with each iteration over the full 4.5 km race course requiring only 30 s of computation time on a laptop computer. The resulting trajectory is experimentally driven at the race circuit with an autonomous Audi TTS test vehicle, and the resulting lap time and racing line are comparable to both a nonlinear gradient descent solution and a trajectory recorded from a professional racecar driver. The experimental results indicate that the proposed method is a viable option for online trajectory planning in the near future.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(9):091006-091006-10. doi:10.1115/1.4032878.

This work combines the kinematics estimate of human standing with a hybrid identification algorithm to identify a set of ankle dynamics mechanical parameters. We used the hold and release (H&R) experimental paradigm to model a set of recoverable falls on a population of unimpaired adults. Body kinematics was acquired with a microsoft kinect (mk) version 2 after benchmarking its position accuracy to a camera-based vision system (CVS). The system identification algorithm, combining an extended Kalman filter (EKF) and a genetic algorithm (GA), allowed to identify the effect of tendon and muscle stiffness at the ankle joint, separately. This work highlights that, when associated to soft-computing techniques, affordable tracking devices developed for the gaming industry can be used for the reliable assessment of neuromechanical parameters in clinical settings.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(9):091007-091007-7. doi:10.1115/1.4033410.

This paper develops an adaptive dynamic surface algorithm for designing the control law for uncertain hysteretic structural systems with seismic disturbances that can be converted to a semi strict feedback form. Hysteretic behavior is usually described by Bouc–Wen model for hysteretic structural systems like base isolation systems. Adaptive sliding mode and adaptive backstepping algorithms are also studied and simulated for comparison purposes. The presented simulation results indicate the effectiveness of the proposed control law in reducing displacement, velocity and acceleration responses of the structural system with acceptable control force. Moreover, using dynamic surface control (DSC), the study analyzes the stability of the controlled system based on the Lyapunov theory.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(9):091008-091008-12. doi:10.1115/1.4033409.

This paper presents a unified control framework for both set-point and time-varying force control of robot manipulator by introducing an improved position-based impedance control (IPBIC). In order to essentially achieve accurate force control, especially time-varying force tracking, a new target impedance function compensated by a force controller is presented. The essence of the improved method in realizing time-varying force tracking, as well as the coupled stability of the manipulator–environment system is investigated. To further improve the force control performance, the Newton-type iterative learning control (ILC) is introduced upon the closed-loop system. A case study on a two-link robot model demonstrates the effectiveness of this method.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(9):091009-091009-13. doi:10.1115/1.4033556.

A long-term gas-path fault diagnosis and its rapid prototype system are presented for on-line monitoring of a gas turbine engine. Toward this end, a nonlinear hybrid model-based performance estimation and abnormal detection method are proposed in this paper. An adaptive extended Kalman particle filter (AEKPF) estimator is developed and used to real time estimate engine health parameters, which depict gas turbine performance degradation condition. The health parameter estimators are then pushed into a buffer memory and for periodical renewing baseline model (BM) performance, and the BM is utilized to detect engine anomaly over its life course. The threshold in abnormal detection schemes is adapted to the modeling errors during the engine lifetime. The rapid prototyping system is designed and built up based on the National Instrument (NI) CompactRIO (CRIO) for evaluating gas turbine engine performance estimation and anomaly detection. A number of experiments are carried out to demonstrate the advantages of the proposed abnormal detection scheme and effectiveness of the designed rapid prototype system to the problem of gas turbine life cycle anomaly detection.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(9):091010-091010-11. doi:10.1115/1.4033412.

Digital hydraulics uses simple and cheap on/off valves in order to replace expensive proportional valves. Furthermore, with fast switching hydraulic converters the energy efficiency can be raised compared to proportional valve control. The hydraulic buck converter (HBC) represents an energy efficient and cost-effective switched inertance system, because its inductance is realized by a simple pipe. In this paper, a prototype for a hydraulic linear cylinder drive controlled by an HBC is presented. Characteristic for this drive axis is that the HBC is directly mounted on the cylinder, which allows a reduction of the oil transport loss between the axis and the hydraulic power supply unit. Furthermore, piston accumulators are used for decoupling and pressure attenuation. Due to their robustness regarding the prepressure to operating pressure, the load pressure can be controlled arbitrary in the piston-sided chamber. The energy performance and the tracking behavior of the axis with a flatness-based control (FBC) are investigated by steady-state measurements and dynamic trajectories, respectively. The results are discussed and an outlook about further improvements of the concept is provided.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(9):091011-091011-10. doi:10.1115/1.4033312.

This paper proposes a consensus state estimator for sensor networks of distributed parameter structures. A thin beam with clamped–clamped boundary conditions enhanced by piezoelectric sensors is considered, and individual observers are assigned for each of these sensors. The so-called estimation agents are then connected to one another in a network with certain directed topology, and consensus is enforced between the agents estimated output in observers dynamics. Observer gains are optimized using algebraic Riccati equations (AREs), and robustness to measurement disturbances is applied via H design. The consensus state estimator is then numerically investigated for a sensor network of five agents. According to the results of the optimal and robust designs, the proposed consensus observer successfully estimates the modal system states in finite time, whereas the estimation output is resilient to measurement disturbances. Implementation of the consensus sensor network increases the robustness of the estimation, due to its inherent redundancy.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(9):091012-091012-11. doi:10.1115/1.4033621.

The paper proposes a dynamic model of an automotive dry dual clutch system, which comprises submodels of a lever-based electromechanical actuator and a dual clutch assembly. The model is developed by using the bond graph approach, and it can be used for clutch design, analysis, and control tasks. Special attention is devoted to modeling of friction, compliance, and lever geometry effects, as they are the ones that predominantly determine the accuracy of clutch static curve description and computational efficiency of the model. Several custom-designed test rigs are utilized for the purpose of collecting the experimental data needed for model parameterization and validation. Experimental validation demonstrates a good modeling accuracy for a wide range of operating parameters.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(9):091013-091013-11. doi:10.1115/1.4033631.

This paper provides a generic analysis of the relationship between time- and frequency-domain disturbance observer (DOB) design methodology. It is discovered that the traditional frequency-domain DOBs using a low-pass filter with unity gain can only handle disturbances satisfying matching condition, while the traditional time-domain DOBs always generate an observer with a high-order. A functional disturbance observer (FDOB) is proposed to improve the existing results together with its design guideline, frequency analysis, and existence condition. Compared with the existing frequency-domain DOBs, the proposed FDOB can handle more classes of disturbances, while compared with the existing time-domain DOBs, the proposed FDOB can generate an observer with a lower-order. Numerical examples are presented to illustrate the main findings of this paper including a rotary mechanical system of nonminimum phase.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2016;138(9):091014-091014-8. doi:10.1115/1.4033906.

With the development of wind turbine technology, more wind turbines operate in the partial load region, where one of the main objectives is to maximize captured wind energy. This paper presents the development of an optimal control framework to maximize wind energy capture for wind turbines with limited rotor speed ranges. Numerical optimal control (NOC) techniques were applied to search for the achievable maximum power coefficient, thus maximum wind energy capture. Augmentations of these optimal techniques significantly reduced the computational cost. Simulation results show that, in comparison with the traditional torque feedback and conventional optimal control algorithms, the proposed augmented optimal control algorithm increases the harvested energy while minimizing the computational expense for speed-constrained wind turbines during partial load operation.

Commentary by Dr. Valentin Fuster

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