1,703 research outputs found
Data-Driven Robust Control of Unknown MIMO Nonlinear System Subject to Input Saturations and Disturbances
This paper presented a new data-driven robust control scheme for unknown nonlinear systems in the presence of input saturation and external disturbances. According to the input and output data of the nonlinear system, a recurrent neural network (RNN) data-driven model is established to reconstruct the dynamics of the nonlinear system. An adaptive output-feedback controller is developed to approximate the unknown disturbances and a novel input saturation compensation method is used to attenuate the effect of the input saturation. Under the proposed adaptive control scheme, the uniformly ultimately bounded convergence of all the signals of the closed-loop nonlinear system is guaranteed via Lyapunov analysis. The simulation results are given to show the effectiveness of the proposed data-driven robust controller
Lagrange Stabilization of Pendulum-like Systems: A Pseudo H-infinity Control Approach
This paper studies the Lagrange stabilization of a class of nonlinear systems
whose linear part has a singular system matrix and which have multiple periodic
(in state) nonlinearities. Both state and output feedback Lagrange
stabilization problems are considered. The paper develops a pseudo H-infinity
control theory to solve these stabilization problems. In a similar fashion to
the Strict Bounded Real Lemma in classic H-infinity control theory, a Pseudo
Strict Bounded Real Lemma is established for systems with a single unstable
pole. Sufficient conditions for the synthesis of state feedback and output
feedback controllers are given to ensure that the closed-loop system is pseudo
strict bounded real. The pseudo H-infinity control approach is applied to solve
state feedback and output feedback Lagrange stabilization problems for
nonlinear systems with multiple nonlinearities. An example is given to
illustrate the proposed method
Resource-aware motion control:feedforward, learning, and feedback
Controllers with new sampling schemes improve motion systems’ performanc
Analysis of Implicit Uncertain Systems. Part II: Constant Matrix Problems and Application to Robust H2 Analysis
This paper introduces an implicit framework for the analysis of uncertain systems, of which the general properties were described in Part I. In Part II, the theory is specialized to problems which admit a finite dimensional formulation. A constant matrix version of implicit analysis is presented, leading to a generalization of the structured singular value μ as the stability measure; upper bounds are developed and analyzed in detail. An application of this framework results in a practical method for robust H2 analysis: computing robust performance in the presence of norm-bounded perturbations and white-noise disturbances
Feedback Control as a Framework for Understanding Tradeoffs in Biology
Control theory arose from a need to control synthetic systems. From
regulating steam engines to tuning radios to devices capable of autonomous
movement, it provided a formal mathematical basis for understanding the role of
feedback in the stability (or change) of dynamical systems. It provides a
framework for understanding any system with feedback regulation, including
biological ones such as regulatory gene networks, cellular metabolic systems,
sensorimotor dynamics of moving animals, and even ecological or evolutionary
dynamics of organisms and populations. Here we focus on four case studies of
the sensorimotor dynamics of animals, each of which involves the application of
principles from control theory to probe stability and feedback in an organism's
response to perturbations. We use examples from aquatic (electric fish station
keeping and jamming avoidance), terrestrial (cockroach wall following) and
aerial environments (flight control in moths) to highlight how one can use
control theory to understand how feedback mechanisms interact with the physical
dynamics of animals to determine their stability and response to sensory inputs
and perturbations. Each case study is cast as a control problem with sensory
input, neural processing, and motor dynamics, the output of which feeds back to
the sensory inputs. Collectively, the interaction of these systems in a closed
loop determines the behavior of the entire system.Comment: Submitted to Integr Comp Bio
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