Powered knee prostheses, compared to traditional energetically-passive knee prostheses, greatly enhance the mobility of transfemoral amputees. However, powered prostheses have a large number of control parameters that...
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ISBN:
(纸本)9781538616451
Powered knee prostheses, compared to traditional energetically-passive knee prostheses, greatly enhance the mobility of transfemoral amputees. However, powered prostheses have a large number of control parameters that must be adjusted for individual amputee users, which presents a gnat challenge for clinical use. To address this challenge, we proposed and compared 2 automatic tuning strategies (i.e. parallel and sequential) using our newly developed optimal adaptivedynamicprogramming (ADP) tuner that objectively tuned the control parameters of an experimental powered knee prosthesis to mimic the knee profile of an able-bodied person (i.e. reference profile). With the parallel tuning strategy, we tuned all control parameters during the stance and the swing phases simultaneously. With the sequential tuning strategy, we alternately tuned stance or swing phase control parameters while fixing the remaining parameters. One able-bodied subject with a prosthesis adapter and one transfemoral amputee subject walked with the experimental powered knee prosthesis under both tuning strategies. Results show that with both tuning strategies, the ADP tuner successfully tuned the impedance parameters to match the prosthetic knee profile to the reference profile. Additionally, the parallel strategy outperformed the sequential strategy with better convergence to the reference profile. Interestingly, with the sequential tuning strategy, tuning during the swing phase greatly impacted the subsequent stance phase profile, but the impact was not as great when the order of tuning was switched. The ability to simultaneously adjust all control parameters with ADP using a parallel strategy may be a preferred solution for the current high-dimension control challenge, which may lead to more advanced, adaptive powered knee prostheses.
This note studies the adaptive optimal output regulation problem for continuous-time linear systems, which aims to achieve asymptotic tracking and disturbance rejection by minimizing some predefined costs. Reinforceme...
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This note studies the adaptive optimal output regulation problem for continuous-time linear systems, which aims to achieve asymptotic tracking and disturbance rejection by minimizing some predefined costs. reinforcementlearning and adaptivedynamicprogramming techniques are employed to compute an approximated optimal controller using input/partial-state data despite unknown system dynamics and unmeasurable disturbance. Rigorous stability analysis shows that the proposed controller exponentially stabilizes the closed-loop system and the output of the plant asymptotically tracks the given reference signal. Simulation results on a LCL coupled inverter-based distributed generation system demonstrate the effectiveness of the proposed approach.
The proceedings contain 711 papers. The topics discussed include: CMOS-nano-bio interface array for cardiac and neuro technology;CMOS bioelectronics: emerging application in molecular diagnostics, microbiology, and ne...
ISBN:
(纸本)9781467368520
The proceedings contain 711 papers. The topics discussed include: CMOS-nano-bio interface array for cardiac and neuro technology;CMOS bioelectronics: emerging application in molecular diagnostics, microbiology, and neuroscience;oscillation-based slime mould electronic circuit model for maze-solving computations;circuit designs of high-performance and low-power rram-based multiplexers based on 4T(transistor)1R(RAM) programming structure;a low-noise fully-differential open-loop interface for high-G capacitive micro-accelerometers with 112.2 DB dynamic range;a 0.42V high bandwidth synthesizable parallel access smart memory fabric for computer vision;architecture for complex network measures of brain connectivity;LLC encoded bow features and softmax regression for microscopic image classification;a 10-b statistical ADC employing pipelining and sub-ranging in 32nm CMOS;depth-projected determination for adaptive search range in motion estimation for HEVC;high resolution and linearity enhanced SAR ADC for wearable sensing systems;pipelined parallel contrastive divergence for continuous generative model learning;on the mechanisms governing spurious tone injection in fractional plls;reducing electrical power dissipation in computational imaging systems through special-purpose optics;lightprobe: a 64-channel programmable ultrasound transducer head with an integrated front-end and a 26.4 GB/s optical link;power-aware space-time-Trellis-coded mimo detector with SNR estimation and state-purging;and a dual-clock vlsi design of H.265 sample adaptive offset estimation for 8k ultra-hd tv encoding.
In this paper, a value iteration adaptivedynamicprogramming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iterati...
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In this paper, a value iteration adaptivedynamicprogramming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. A novel convergence analysis is developed to guarantee that the iterative value function converges to the optimal performance index function. Initialized by different initial functions, it is proven that the iterative value function will be monotonically nonincreasing, monotonically nondecreasing, or nonmonotonic and will converge to the optimum. In this paper, for the first time, the admissibility properties of the iterative control laws are developed for value iteration algorithms. It is emphasized that new termination criteria are established to guarantee the effectiveness of the iterative control laws. Neural networks are used to approximate the iterative value function and compute the iterative control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.
In this paper, we present a problem of regulating the motion of a rolling ball in a one-dimensional space in the presence of non-linear effects of friction and contact. The regulation problem is solved using a model-b...
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In this paper, we present a problem of regulating the motion of a rolling ball in a one-dimensional space in the presence of non-linear effects of friction and contact. The regulation problem is solved using a model-based reinforcementlearning technique. A Gaussian process model is learned to make predictions on the motion of the ball and then, the predictive model is used to solve for the control policy using dynamicprogramming by estimating the value functions. Several results are shown to demonstrate the simple, yet interesting motion dynamics for the ball. Our hope is that the proposed system will serve as a simple benchmark system for reinforcement and robot learning.
Goal representation heuristic dynamicprogramming (GrHDP) control design has been developed in recent years. The control performance of this design has been demonstrated in several case studies, and also showed applic...
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Goal representation heuristic dynamicprogramming (GrHDP) control design has been developed in recent years. The control performance of this design has been demonstrated in several case studies, and also showed applicable to industrial-scale complex control problems. In this paper, we develop the theoretical analysis for the GrHDP design under certain conditions. It has been shown that the internal reinforcement signal is a bounded signal and the performance index can converge to its optimal value monotonically. The existence of the admissible control is also proved. Although the GrHDP control method has been investigated in many areas before, to the best of our knowledge, this is the first study of presenting the theoretical foundation of the internal reinforcement signal and how such an internal reinforcement signal can provide effective information to improve the control performance. Numerous simulation studies are used to validate the theoretical analysis and also demonstrate the effectiveness of the GrHDP design.
In this paper the process of spectrum assignment in narrowband power line communication is studied. Due to the frequency-selective and time-variant nature of the PLC channel, spectrum assignment must be performed dyna...
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ISBN:
(纸本)9781509023899
In this paper the process of spectrum assignment in narrowband power line communication is studied. Due to the frequency-selective and time-variant nature of the PLC channel, spectrum assignment must be performed dynamically throughout the transmission. This requires constant channel state information at the PLC transmitter which in turn increases undesired overhead in the system and degrades throughput. An adaptive pursuit strategy has been proposed in order to overcome this problem by enabling the transmitter through a reinforcementlearning method to select the best part of the spectrum dynamically and without prior knowledge of the channel. Simulation results show a fast adaptation to the channel conditions which results in an increase in spectral efficiency and improves the performance of the transmission.
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