Model Predictive Control-Based Dynamic Control Allocation in a Hybrid Neuroprosthesis

2014 ◽  
Author(s):  
Nicholas A. Kirsch ◽  
Naji A. Alibeji ◽  
Nitin Sharma
Keyword(s):  
Model Predictive Control ◽  
Muscle Fatigue ◽  
Predictive Control ◽  
Electric Motor ◽  
Dynamic Control ◽  
Rapid Onset ◽  
Knee Extension ◽  
Joint Torque ◽  
Lower Limb Muscles ◽  
Hybrid Neuroprosthesis

To date, a functional electrical stimulation (FES)-based walking technology is incapable of enabling a paraplegic user to walk more than a few hundred meters. This is primarily due to the rapid onset of muscle fatigue, which causes limited torque generation capability of the lower-limb muscles. A hybrid walking neuroprosthesis that combines FES with an electric motor can overcome this challenge, since an electric motor can be used to compensate for any reduction in force generation due to the muscle fatigue. However, the hybrid actuation structure creates an actuator redundancy control problem; i.e., a closed-loop controller must optimally distribute torque between FES and an electric motor. Further, the control inputs to FES and an electric motor must adapt as a skeletal muscle fatigues. We consider these issues as open research control problems. In this paper, we propose that a model predictive control (MPC)-based control design can be used to optimally distribute joint torque, and can adapt as the muscle fatigue sets in. Particularly, a customized quadratic programming solver (generated using CVXGEN) was used to simulate MPC-based control of the hybrid neuroprosthesis that elicits knee extension via FES and an electric actuator.

Nonlinear Model Predictive Control of Functional Electrical Stimulation

2015 ◽  
Author(s):  
Nicholas A. Kirsch ◽  
Naji A. Alibeji ◽  
Nitin Sharma
Keyword(s):  
Optimal Control ◽  
Electrical Stimulation ◽  
Model Predictive Control ◽  
Muscle Fatigue ◽  
Predictive Control ◽  
Functional Electrical Stimulation ◽  
Rapid Onset ◽  
Knee Extension ◽  
Control Lyapunov Function ◽  
Predictive Controller

One of the major limitations of functional electrical stimulation (FES) is the rapid onset of muscle fatigue. Minimizing stimulation is the key to decreasing the adverse effects of muscle fatigue caused by FES. Optimal control can be used to compute the minimum amount of stimulation necessary to produce a desired motion. In this paper, a gradient projection-based model predictive controller is used for an approximate optimal control of a knee extension neuroprosthesis. A control Lyapunov function is used as a terminal cost to ensure stability of the model predictive control.

scholarly journals Model Predictive Control of a Feedback-Linearized Hybrid Neuroprosthetic System With a Barrier Penalty

2019 ◽  
Vol 14 (10) ◽  
Author(s):  
Xuefeng Bao ◽  
Nicholas Kirsch ◽  
Albert Dodson ◽  
Nitin Sharma
Keyword(s):  
Model Predictive Control ◽  
Muscle Fatigue ◽  
Predictive Control ◽  
Electric Motor ◽  
Feedback Linearization ◽  
Angular Position ◽  
Work Load ◽  
Rapid Onset ◽  
State Variables ◽  
State Dependent

Abstract Functional electrical stimulation (FES) is prescribed as a treatment to restore motor function in individuals with neurological impairments. However, the rapid onset of FES-induced muscle fatigue significantly limits its duration of use and limb movement quality. In this paper, an electric motor-assist is proposed to alleviate the fatigue effects by sharing work load with FES. A model predictive control (MPC) method is used to allocate control inputs to FES and the electric motor. To reduce the computational load, the dynamics is feedback linearized so that the nominal model inside the MPC method becomes linear. The state variables: the angular position and the muscle fatigue are still preserved in the transformed state space to keep the optimization meaningful. Because after feedback linearization the original linear input constraints may become nonlinear and state-dependent, a barrier cost function is used to overcome this issue. The simulation results show a satisfactory control performance and a reduction in the computation due to the linearization.

scholarly journals Model predictive control based on artificial intelligence and EPA-SWMM model to reduce CSOs impacts in sewer systems

2021 ◽  
Author(s):  
Khalid El Ghazouli ◽  
Jamal El Khatabi ◽  
Aziz Soulhi ◽  
Isam Shahrour
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategies ◽  
Dynamic Control ◽  
Combined Sewer Overflows ◽  
Sewer System ◽  
Sewer Systems ◽  
Proposed Model ◽  
The Impact ◽  
The City

Abstract Urbanization and an increase in precipitation intensities due to climate change, in addition to limited urban drainage systems (UDS) capacity, are the main causes of combined sewer overflows (CSOs) that cause serious water pollution problems in many cities around the world. Model predictive control (MPC) systems offer a new approach to mitigate the impact of CSOs by generating optimal temporally and spatially varied dynamic control strategies of sewer system actuators. This paper presents a novel MPC based on neural networks for predicting flows, a stormwater management model (SWMM) for flow conveyance, and a genetic algorithm for optimizing the operation of sewer systems and defining the best control strategies. The proposed model was tested on the sewer system of the city of Casablanca in Morocco. The results have shown the efficiency of the developed MPC to reduce CSOs while considering short optimization time thanks to parallel computing.

scholarly journals Common-Mode Voltage Reduction Algorithm for Photovoltaic Grid-Connected Inverters with Virtual-Vector Model Predictive Control

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2607
Author(s):  
Hui Hwang Goh ◽  
Xinyi Li ◽  
Chee Shen Lim ◽  
Dongdong Zhang ◽  
Wei Dai ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pulse Width Modulation ◽  
Control Method ◽  
Dynamic Control ◽  
Single Stage ◽  
Control Technique ◽  
Vector Model ◽  
Common Mode ◽  
Common Mode Voltage

Model predictive control (MPC) has been proven to offer excellent model-based, highly dynamic control performance in grid converters. The increasingly higher power capacity of a PV inverter has led to the industrial preference of adopting higher DC voltage design at the PV array (e.g., 750–1500 V). With high array voltage, a single stage inverter offers advantages of low component count, simpler topology, and requiring less control tuning effort. However, it is typically entailed with the issue of high common-mode voltage (CMV). This work proposes a virtual-vector model predictive control method equipped with an improved common-mode reduction (CMR) space vector pulse width modulation (SVPWM). The modulation technique essentially subdivides the hexagonal voltage vector space into 18 sub-sectors, that can be split into two groups with different CMV properties. The proposal indirectly increases the DC-bus utilization and extends the overall modulation region with improved CMV. The comparison with the virtual-vector MPC scheme equipped with the conventional SVPWM suggests that the proposed technique can effectively suppress 33.33% of the CMV, and reduce the CMV toggling frequency per fundamental cycle from 6 to either 0 or 2 (depending on which sub-sector group). It is believed that the proposed control technique can help to improve the performance of photovoltaic single-stage inverters.

scholarly journals Double-layered nonlinear model predictive control based on Hammerstein–Wiener model with disturbance rejection

2018 ◽  
Vol 51 (7-8) ◽  
pp. 260-275 ◽  
Author(s):  
Hongbin Cai ◽  
Ping Li ◽  
Chengli Su ◽  
Jiangtao Cao
Keyword(s):  
Steady State ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Disturbance Rejection ◽  
Control Method ◽  
Dynamic Control ◽  
State Feedback Control ◽  
Nonlinear Model Predictive Control ◽  
Wiener Model

This paper presents the double-layered nonlinear model predictive control method for a continuously stirred tank reactor and a pH neutralization process that are subject to input disturbances and output disturbances at the same time. The nonlinear systems can be described as a Hammerstein -Wiener model. Furthermore, two nonlinear parts of the Hammerstein -Wiener model should be transformed into linear combination of known input and unknown disturbances, respectively. By taking advantage of Kalman filter, disturbances and states can be estimated. The estimated disturbances and states can be considered to calculate steady-state target in steady-state target calculation layer. Moreover, the state feedback control law can be obtained in dynamic control layer. A simple proof for offset-free control is given in the proposed method. The simulation results show that the controlled variable can achieve the offset-free control. It can be seen that the proposed method has better disturbance rejection performance, strong robustness and practical value.

scholarly journals Minimizing muscle fatigue through optimization of electrical stimulation parameters

2016 ◽  
Vol 3 (1) ◽  
pp. 33 ◽  
Author(s):  
Hossein Rouhani ◽  
Karen Elena Rodriguez ◽  
Austin J Bergquist ◽  
Kei Masani ◽  
Milos R Popovic
Keyword(s):  
Electrical Stimulation ◽  
Muscle Fatigue ◽  
Pulse Duration ◽  
Plantar Flexion ◽  
Pulse Amplitude ◽  
Bilateral Symmetry ◽  
Rapid Onset ◽  
Joint Torque ◽  
Plantar Flexors ◽  
Time Integral

Rapid onset of muscle fatigue in response to electrical stimulation is a major challenge when designing a neuroprosthesis. This study aimed to introduce a decision-making algorithm to optimize pulse amplitude and pulse duration, for current regulated electrical stimulators, to attain a target joint torque level while minimizing muscle fatigue. We measured ankle torque produced by different pairs of pulse amplitude and pulse duration applied to the plantar-flexors. In each session, we measured the maximum generated torque and calculated muscle fatigue (fatigue time and torque-time integral). Then, we determined the pulse amplitude and pulse duration pair that generated a target level of torque while minimizing muscle fatigue. High bilateral symmetry and day-to-day repeatability was observed for the torque time-series between the left and right plantar-flexors of each participant (median correlation coefficient = 0.95). Compared to the average fatigue obtained by various pulse amplitude and pulse duration pairs for a given level of torque, delivering pulses with the optimal pair reduced fatigue on average by 22.5% according to fatigue time and 6.6% according to torque-time integral. We created an empirical model describing how pulse amplitude and pulse duration can be modulated to generate specific levels of plantar-flexion torque with minimum muscle fatigue.

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