Two-phase Control Strategy Research Articles

Subject specific optimal control of powered knee exoskeleton to assist human lifting tasks under controlled environment

AbstractWearable robots, sometimes known as exoskeletons, are incredible devices for improving human strength, reducing fatigue, and restoring impaired mobility. The control of powered exoskeletons, on the other hand, is still a challenge. This necessitates the development of a technique to simulate exoskeleton–wearer interaction. This study uses a two-dimensional human skeletal model with a powered knee exoskeleton to predict the optimal lifting motion and assistive torque. For lifting motion prediction, an inverse dynamics optimization formulation is utilized. In addition, the electromechanical dynamics of the exoskeleton DC motor are modeled in the lifting optimization formulation. The design variables are human joint angle profiles and exoskeleton motor current profiles. The human joint torque square is minimized subject to physical and lifting task constraints. Then, the lifting optimization problem is solved by the gradient-based sparse nonlinear optimizer (SNOPT). Furthermore, the optimal exoskeleton torque is implemented through a two-phase control strategy to provide optimal assistance in lifting. Experimental validations of the optimal control with 6 Nm and 16 Nm maximum assistive torque are presented. Both 6 Nm and 16 Nm maximum optimal assistance of the exoskeletons reduce the mean values of vastus lateralis, biceps femoris, and latissimus dorsi muscle activations compared to the lifting without the exoskeleton. However, the mean value of the vastus medialis activation is increased by a small amount for the exoskeleton case, although its peak value is reduced. Finally, the experimental results demonstrate that the proposed lifting optimization formulation and control strategy are promising for powered knee exoskeleton for lifting tasks.

Adaptive fixed-time stabilization for a class of nonlinear uncertain systems.

Finite-time stability (FTS) has attained great interest in nonlinear control systems in recent two decades. Fixed-time stability (FxTS) is an improved version of FTS in consideration of its settling time independent of the initial values. In this article, the adaptive fixed-time stabilization issue is studied for a kind of nonlinear systems with nonlinear parametric uncertainties and uncertain control coefficients. Using the adaptive estimate and the adding one power integrator (AOPI) design tool, we propose a two-phase control strategy, which makes that the system states tend to the origin in fixed-time, and other signals are bounded on [0,+∞). We prove the main results by means of the recently developed fixed-time Lyapunov stability theory. Finally, we apply the proposed adaptive fixed-time stabilizing control strategy into the pendulum system, and the simulation results verify the efficacy of the presented method.

Mitigating China’s Ozone Pollution with More Balanced Health Benefits

China is confronting the challenge of opposite health benefits (OHBs) during ambient ozone (O3) mitigation because the same reduction scheme might yield opposite impacts on O3 levels and associated public health across different regions. Here, we used a combination of chemical transport modeling, health benefit assessments, and machine learning to capture such OHBs and optimize O3 mitigation pathways based on 121 control scenarios. We revealed that, for the China mainland, Beijing-Tianjin-Hebei and its surroundings ("2 + 26" cities), Yangtze River Delta, and Pearl River Delta, there could be at most 2897, 920, 1247, and 896 additional O3-related deaths in urban areas, respectively, accompanying 21,512, 3442, 5614, and 642 avoided O3-related deaths in rural areas, respectively, at the same control stage. Additionally, potential disbenefits during O3 mitigation were "pro-wealthy", that is, residents in developed regions are more likely to afford additional health risks. In order to avoid OHBs during O3 abatement, we proposed a two-phase control strategy, whereby the reduction ratio of NOX (nitrogen oxide) to VOCs (volatile organic compounds) was adjusted according to health benefit distribution patterns. Our study provided novel insights into China's O3 attainment and references for other countries facing the dual challenges of environmental pollution and associated inequality issues.

Two-phase strategy of neural control for planar reaching movements: II—relation to spatiotemporal characteristics of movement trajectory

In the companion paper utilizing a quantitative model of optimal motor coordination (Part I, Rand and Shimansky, in Exp Brain Res 225:55-73, 2013), we examined coordination between X and Y movement directions (XYC) during reaching movements performed under three prescribed speeds, two movement amplitudes, and two target sizes. The obtained results indicated that the central nervous system (CNS) utilizes a two-phase strategy, where the initial and the final phases correspond to lower and higher precision of information processing, respectively, for controlling goal-directed reach-type movements to optimize the total cost of task performance including the cost of neural computations. The present study investigates how two different well-known concepts used for describing movement performance relate to the concepts of optimal XYC and two-phase control strategy. First, it is examined to what extent XYC is equivalent to movement trajectory straightness. The data analysis results show that the variability, the movement trajectory's deviation from the straight line, increases with an increase in prescribed movement speed. In contrast, the dependence of XYC strength on movement speed is opposite (in total agreement with an assumption of task performance optimality), suggesting that XYC is a feature of much higher level of generality than trajectory straightness. Second, it is tested how well the ballistic and the corrective components described in the traditional concept of two-component model of movement performance match with the initial and the final phase of the two-phase control strategy, respectively. In fast reaching movements, the percentage of trials with secondary corrective submovement was smaller under larger-target shorter-distance conditions. In slower reaching movements, meaningful parsing was impossible due to massive fluctuations in the kinematic profile throughout the movement. Thus, the parsing points determined by the conventional submovement analysis did not consistently reflect separation between the ballistic and error-corrective components. In contrast to the traditional concept of two-component movement performance, the concept of two-phase control strategy is applicable to a wide variety of experimental conditions.

Two-phase strategy of controlling motor coordination determined by task performance optimality

A quantitative model of optimal coordination between hand transport and grip aperture has been derived in our previous studies of reach-to-grasp movements without utilizing explicit knowledge of the optimality criterion or motor plant dynamics. The model's utility for experimental data analysis has been demonstrated. Here we show how to generalize this model for a broad class of reaching-type, goal-directed movements. The model allows for measuring the variability of motor coordination and studying its dependence on movement phase. The experimentally found characteristics of that dependence imply that execution noise is low and does not affect motor coordination significantly. From those characteristics it is inferred that the cost of neural computations required for information acquisition and processing is included in the criterion of task performance optimality as a function of precision demand for state estimation and decision making. The precision demand is an additional optimized control variable that regulates the amount of neurocomputational resources activated dynamically. It is shown that an optimal control strategy in this case comprises two different phases. During the initial phase, the cost of neural computations is significantly reduced at the expense of reducing the demand for their precision, which results in speed-accuracy tradeoff violation and significant inter-trial variability of motor coordination. During the final phase, neural computations and thus motor coordination are considerably more precise to reduce the cost of errors in making a contact with the target object. The generality of the optimal coordination model and the two-phase control strategy is illustrated on several diverse examples.

Two-phase strategy of neural control for planar reaching movements: I. XY coordination variability and its relation to end-point variability

A quantitative model of optimal transport-aperture coordination (TAC) during reach-to-grasp movements has been developed in our previous studies. The utilization of that model for data analysis allowed, for the first time, to examine the phase dependence of the precision demand specified by the CNS for neurocomputational information processing during an ongoing movement. It was shown that the CNS utilizes a two-phase strategy for movement control. That strategy consists of reducing the precision demand for neural computations during the initial phase, which decreases the cost of information processing at the expense of lower extent of control optimality. To successfully grasp the target object, the CNS increases precision demand during the final phase, resulting in higher extent of control optimality. In the present study, we generalized the model of optimal TAC to a model of optimal coordination between X and Y components of point-to-point planar movements (XYC). We investigated whether the CNS uses the two-phase control strategy for controlling those movements, and how the strategy parameters depend on the prescribed movement speed, movement amplitude and the size of the target area. The results indeed revealed a substantial similarity between the CNS's regulation of TAC and XYC. First, the variability of XYC within individual trials was minimal, meaning that execution noise during the movement was insignificant. Second, the inter-trial variability of XYC was considerable during the majority of the movement time, meaning that the precision demand for information processing was lowered, which is characteristic for the initial phase. That variability significantly decreased, indicating higher extent of control optimality, during the shorter final movement phase. The final phase was the longest (shortest) under the most (least) challenging combination of speed and accuracy requirements, fully consistent with the concept of the two-phase control strategy. This paper further discussed the relationship between motor variability and XYC variability.

Controller Design for a Rubbing Rotor

A new approach to control a rubbing rotor by applying an active auxiliary bearing is developed. The auxiliary bearing is attached to the foundation via two unidirectional actuators. The control force is applied indirectly using the active auxiliary bearing only in case of rubbing. A framework for the development of a feedback controller for an active auxiliary bearing is presented. The theory of a robust two-phase control strategy which guarantees a smooth transition from free rotor motion to the state of full annular rub is presented. A simulation environment for the elastic rotor and the auxiliary bearing including the non-smooth nonlinear dynamics of the rubbing contact is used to develop the feedback controller. Experimental studies have been carried out at a rotor test rig. Various experiments show the outstanding success of the strategy. In case of rubbing, the contact forces are reduced up to 90%.

Simulation-Based Controller Design for an Active Auxiliary Bearing

A framework for the development of a feedback controller for an active auxiliary bearing is presented. In a new approach, a dry bushing type auxiliary bearing is attached to the foundation via two unidirectional actuators. The control force is applied indirectly using the active auxiliary bearing only in case of rubbing. A simulation for the elastic rotor and the auxiliary bearing including the non-smooth nonlinear dynamics of the rubbing contact is used to develop the feedback controller. A robust two-phase control strategy has been developed which stabilizes the rotor system in case of rubbing and effectively avoids “backward whirling”. Experimental studies have been carried out at a rotor test rig. The experiments show the outstanding success of the strategy. In case of rubbing, the contact forces are reduced up to 85%.

Control of a rubbing rotor using an active auxiliary bearing

A new approach to control a rubbing rotor applying an active auxiliary bearing is presented. A two-phase control strategy has been developed, which guarantees a smooth transition from free rotor motion to the state of full annular rub, in case of an operation state which causes rotor rubbing. The designed control system is able of both, avoiding high impact forces and stabilizing the rotor during rubbing. For the experimental verification of the designed control system a test rig has been realized. The experimental results show a drastic reduction of the contact forces, as well as a reduction of the rotor deflection.

Load balancing for cellular/WLAN integrated networks

The interworking between heterogeneous third-generation cellular networks and wireless local area networks is one promising evolution approach to fourth-generation wireless networks, which can exploit the complementary advantages of the cellular network and WLANs. Resource management for the 4G-oriented cellular/WLAN integrated network is an important open issue that deserves more research efforts. In this article we present a policy framework for resource management in a loosely coupled cellular/WLAN integrated network, where load balancing policies are designed to efficiently utilize the pooled resources of the network. A two-phase control strategy is adopted in the load balancing policies, in which call assignment is used to provide a statistical quality of service guarantee during the admission phase, and dynamic vertical handoff during the traffic service phase is used to minimize the performance variations. Numerical results are presented to demonstrate that the proposed load balancing solution achieves significant performance improvement over two other reference schemes

Active identification for discrete-time nonlinear control. I. Output-feedback systems

The problem of controlling nonlinear systems with unknown parameters has received a great deal of attention in the continuous-time case. In contrast, its discrete-time counterpart remains largely unexplored, primarily due to the difficulties associated with utilizing Lyapunov design techniques in a discrete-time framework. Existing results impose restrictive growth conditions on the nonlinearities to yield global stability. In this paper, we propose a novel approach, which removes this obstacle and yields global stability and tracking for systems that can be transformed into an output-feedback canonical form, in which the nonlinearities depend only on the measured output, but are otherwise arbitrary. The main novelties of our design are: (i) the temporal and algorithmic separation of the parameter estimation task from the control task, and (ii) the development of an active identification procedure, which uses the control input to actively drive the system state to points in the state space that allow the orthogonalized projection estimator to acquire all the necessary information about the unknown parameters. We prove that our algorithm guarantees complete (for control purposes) identification in a finite time interval, whose maximum length we compute. Thus, the traditional structure of concurrent online estimation and control is replaced by a two-phase control strategy: first use active identification, and then utilize the acquired parameter information to implement any control strategy as if the parameters were known.