37,692 research outputs found
High-Performance Multi-Axis Control with Applications in Compliant Parallel Manipulators
A two-axis high-performance positioning system is analyzed and controlled. Firstly, the control system was extended from one to two-axis movements. The control algorithm for each axis is based on a PID feedback controller and a force feed-forward controller. Two cross-coupling interactions arise between the axes. They are analyzed and compensated. Finally, when periodic reference signals are used, performances are improved using repetitive control (RPC) and iterative learning control (ILC)
Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization
Contemporary autopilot systems for unmanned aerial vehicles (UAVs) are far
more limited in their flight envelope as compared to experienced human pilots,
thereby restricting the conditions UAVs can operate in and the types of
missions they can accomplish autonomously. This paper proposes a deep
reinforcement learning (DRL) controller to handle the nonlinear attitude
control problem, enabling extended flight envelopes for fixed-wing UAVs. A
proof-of-concept controller using the proximal policy optimization (PPO)
algorithm is developed, and is shown to be capable of stabilizing a fixed-wing
UAV from a large set of initial conditions to reference roll, pitch and
airspeed values. The training process is outlined and key factors for its
progression rate are considered, with the most important factor found to be
limiting the number of variables in the observation vector, and including
values for several previous time steps for these variables. The trained
reinforcement learning (RL) controller is compared to a
proportional-integral-derivative (PID) controller, and is found to converge in
more cases than the PID controller, with comparable performance. Furthermore,
the RL controller is shown to generalize well to unseen disturbances in the
form of wind and turbulence, even in severe disturbance conditions.Comment: 11 pages, 3 figures, 2019 International Conference on Unmanned
Aircraft Systems (ICUAS
Hybrid iterative learning control of a flexible manipulator
This paper presents an investigation into the development of a hybrid control scheme with iterative learning for input tracking and end-point vibration suppression of a flexible manipulator system. The dynamic model of the system is derived using the finite element method. Initially, a collocated proportional-derivative (PD) controller using hub angle and hub velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate a non-collocated proportional-integral-derivative (PID) controller with iterative learning for control of vibration of the system. Simulation results of the response of the manipulator with the controllers are presented in the time and frequency domains. The performance of the hybrid iterative learning control scheme is assessed in terms of input tracking and level of vibration reduction in comparison to a conventionally designed PD-PID control scheme. The effectiveness of the control scheme in handling various payloads is also studied
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
PID control architectures are widely used in industrial applications. Despite
their low number of open parameters, tuning multiple, coupled PID controllers
can become tedious in practice. In this paper, we extend PILCO, a model-based
policy search framework, to automatically tune multivariate PID controllers
purely based on data observed on an otherwise unknown system. The system's
state is extended appropriately to frame the PID policy as a static state
feedback policy. This renders PID tuning possible as the solution of a finite
horizon optimal control problem without further a priori knowledge. The
framework is applied to the task of balancing an inverted pendulum on a seven
degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast
and data-efficient policy learning, even on complex real world problems.Comment: Accepted final version to appear in 2017 IEEE International
Conference on Robotics and Automation (ICRA
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
Journal 201
Neuro fuzzy control of the FES assisted freely swinging leg of paraplegic subjects
The authors designed a neuro fuzzy control strategy for control of cyclical leg movements of paraplegic subjects. The cyclical leg movements were specified by three `swing phase objectives', characteristic of natural human gait. The neuro fuzzy controller is a combination of a fuzzy logic controller and a neural network, which makes the controller self tuning and adaptive. Two experiments have been performed, in which the performance of the neuro fuzzy control strategy has been compared with conventional PID control strateg
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