Reinforcement Learning Adaptive PID Controller for an Under-actuated Robot Arm

Abstract: An adaptive PID controller is used to control of a two degrees of freedom under actuated manipulator. An actor-critic based reinforcement learning is employed for tuning of parameters of the adaptive PID controller. Reinforcement learning is an unsupervised scheme wherein no reference exists to which convergence of algorithm is anticipated. Thus, it is appropriate for real time applications. Controller structure and learning equations as well as update rules are provided. Simulations are performed in SIMULINK and performance of the controller is compared with NARMA-L2 controller. The results verified good performance of the controller in tracking and disturbance rejection tests

Robust control of underactuated wheeled mobile manipulators using GPI disturbance observers

This article describes the design of a linear observer–linear controller-based robust output feedback scheme for output reference trajectory tracking tasks in the case of nonlinear, multivariable, nonholonomic underactuated mobile manipulators. The proposed linear feedback scheme is based on the use of a classical linear feedback controller and suitably extended, high-gain, linear Generalized Proportional Integral (GPI) observers, thus aiding the linear feedback controllers to provide an accurate simultaneous estimation of each flat output associated phase variables and of the exogenous and perturbation inputs. This information is used in the proposed feedback controller in (a) approximate, yet close, cancelations, as lumped unstructured time-varying terms, of the influence of the highly coupled nonlinearities, and (b) the devising of proper linear output feedback control laws based on the approximate estimates of the string of phase variables associated with the flat outputs simultaneously provided by the disturbance observers. Simulations reveal the effectiveness of the proposed approach

Autonomous thruster failure recovery on underactuated spacecraft using model predictive control

Thruster failures historically account for a large percentage of failures that have occurred on orbit. These failures are typically handled through redundancy, however, with the push to using smaller, less expensive satellites in clusters or formations there is a need to perform thruster failure recovery without additional hardware. This means that a thruster failure may cause the spacecraft to become underactuated, requiring more advanced control techniques. A model of a thruster-controlled spacecraft is developed and analyzed with a nonlinear controllability test, highlighting several challenges including coupling, nonlinearities, severe control input saturation, and nonholonomicity. Model Predictive Control (MPC) is proposed as a control technique to solve these challenges. However, the real-time, online implementation of MPC brings about many issues. A method of performing MPC online is described, implemented and tested in simulation as well as in hardware on the Synchronized Position-Hold, Engage, Reorient Experimental Satellites (SPHERES) testbed at the Massachusetts Institute of Technology (MIT) and on the International Space Station (ISS). These results show that MPC provided improved performance over a simple path planning technique