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.

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.

Fusion of inverse optimal and model predictive control strategies

2019 ◽  
Vol 42 (6) ◽  
pp. 1122-1134
Author(s):  
Lütfi Ulusoy ◽  
Müjde Güzelkaya ◽  
İbrahim Eksin
Keyword(s):  
Optimal Control ◽  
Lyapunov Function ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Initial Conditions ◽  
Big Bang ◽  
Receding Horizon ◽  
Control Lyapunov Function ◽  
Candidate Control ◽  
Function Matrix

In this study, model predictive control (MPC) and inverse optimal control (IOC) approaches are merged with each other and a new control strategy is evolved. The key feature in this strategy is to solve the IOC problem repeatedly for each receding horizon of the model predictive control approach. From another perspective, MPC structure is inserted to IOC problem and thus, IOC problem is solved repeatedly using different initial conditions at the beginning of each receding horizon. In the solution phase of IOC, the parameters of the candidate control Lyapunov function matrix are estimated using the global evolutionary Big Bang-Big Crunch (BB-BC) optimization algorithm in an on-line manner. Thus, the proposed control structure solves the optimal control problem in classical MPC approach to the search of an appropriate candidate control Lyapunov function matrix for each control horizon. The comparison of the proposed method with the other related control methods are performed on the ball and beam system via simulations and real-time applications.

scholarly journals Model Predictive Control for Autonomous Driving Vehicles

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2593
Author(s):  
Trieu Minh Vu ◽  
Reza Moezzi ◽  
Jindrich Cyrus ◽  
Jaroslav Hlava
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Driving ◽  
Nonlinear Model Predictive Control ◽  
System Stability ◽  
Vehicle Speed ◽  
Model Predictive Controller ◽  
Predictive Controller

The field of autonomous driving vehicles is growing and expanding rapidly. However, the control systems for autonomous driving vehicles still pose challenges, since vehicle speed and steering angle are always subject to strict constraints in vehicle dynamics. The optimal control action for vehicle speed and steering angular velocity can be obtained from the online objective function, subject to the dynamic constraints of the vehicle’s physical limitations, the environmental conditions, and the surrounding obstacles. This paper presents the design of a nonlinear model predictive controller subject to hard and softened constraints. Nonlinear model predictive control subject to softened constraints provides a higher probability of the controller finding the optimal control actions and maintaining system stability. Different parameters of the nonlinear model predictive controller are simulated and analyzed. Results show that nonlinear model predictive control with softened constraints can considerably improve the ability of autonomous driving vehicles to track exactly on different trajectories.

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