Topic
Open-loop controller
About: Open-loop controller is a research topic. Over the lifetime, 16148 publications have been published within this topic receiving 224014 citations. The topic is also known as: non-feedback controller & open-loop control law.
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TL;DR: An information-theoretic framework for analyzing control systems based on the close relationship of controllers to communication channels is proposed, providing new derivations of the advantage afforded by closed-loop control and proposing an information-based optimality criterion for control systems.
Abstract: We propose an information-theoretic framework for analyzing control systems based on the close relationship of controllers to communication channels. A communication channel takes an input state and transforms it into an output state. A controller, similarly, takes the initial state of a system to be controlled and transforms it into a target state. In this sense, a controller can be thought of as an actuation channel that acts on inputs to produce desired outputs. In this transformation process, two different control strategies can be adopted: (i) the controller applies an actuation dynamics that is independent of the state of the system to be controlled (open-loop control); or (ii) the controller enacts an actuation dynamics that is based on some information about the state of the controlled system (closed-loop control). Using this communication channel model of control, we provide necessary and sufficient conditions for a system to be perfectly controllable and perfectly observable in terms of information and entropy. In addition, we derive a quantitative trade-off between the amount of information gathered by a closed-loop controller and its relative performance advantage over an open-loop controller in stabilizing a system. This work supplements earlier results (Phys. Rev. Lett. 84 (2000) 1156) by providing new derivations of the advantage afforded by closed-loop control and by proposing an information-based optimality criterion for control systems. New applications of this approach pertaining to proportional controllers, and the control of chaotic maps are also presented.
218 citations
01 Apr 2009
TL;DR: This paper compares reinforcement learning with model predictive control in a unified framework and reports experimental results of their application to the synthesis of a controller for a nonlinear and deterministic electrical power oscillations damping problem.
Abstract: This paper compares reinforcement learning (RL) with model predictive control (MPC) in a unified framework and reports experimental results of their application to the synthesis of a controller for a nonlinear and deterministic electrical power oscillations damping problem. Both families of methods are based on the formulation of the control problem as a discrete-time optimal control problem. The considered MPC approach exploits an analytical model of the system dynamics and cost function and computes open-loop policies by applying an interior-point solver to a minimization problem in which the system dynamics are represented by equality constraints. The considered RL approach infers in a model-free way closed-loop policies from a set of system trajectories and instantaneous cost values by solving a sequence of batch-mode supervised learning problems. The results obtained provide insight into the pros and cons of the two approaches and show that RL may certainly be competitive with MPC even in contexts where a good deterministic system model is available.
214 citations
TL;DR: The key element in this work is the employment of an infinite-dimensional "backstepping" transformation, and the resulting complete Lyapunov function, for the infinite dimensional systems consisting of the state of the ODE plant and the delay state, which allows to establish inverse optimality of the modified feedback and its disturbance attenuation properties.
214 citations
TL;DR: Numerical simulation results demonstrate the effectiveness of the fuzzy P+ID controller in comparison with the conventional PID controller, especially when the controlled object operates under uncertainty or in the presence of a disturbance.
Abstract: Presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral-derivative (fuzzy P+ID) controller in an incremental form. This controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional PID controller, By using the bounded-input/bounded-output "small gain theorem", the sufficient condition for stability of this controller is derived. Based on the condition, we modify the Ziegler and Nichols' approach to design the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. When a plant can be described by any modeling method, the fuzzy P+ID controller can be determined by an optimization technique. Finally, this controller is used to control a nonlinear system. Numerical simulation results demonstrate the effectiveness of the fuzzy P+ID controller in comparison with the conventional PID controller, especially when the controlled object operates under uncertainty or in the presence of a disturbance.
212 citations