H∞ control for networked systems with random communication delays

Copyright [2006] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This note is concerned with a new controller design problem for networked systems with random communication delays. Two kinds of random delays are simultaneously considered: i) from the controller to the plant, and ii) from the sensor to the controller, via a limited bandwidth communication channel. The random delays are modeled as a linear function of the stochastic variable satisfying Bernoulli random binary distribution. The observer-based controller is designed to exponentially stabilize the networked system in the sense of mean square, and also achieve the prescribed H∞ disturbance attenuation level. The addressed controller design problem is transformed to an auxiliary convex optimization problem, which can be solved by a linear matrix inequality (LMI) approach. An illustrative example is provided to show the applicability of the proposed method

Intelligent control of nonlinear systems with actuator saturation using neural networks

Common actuator nonlinearities such as saturation, deadzone, backlash, and hysteresis are unavoidable in practical industrial control systems, such as computer numerical control (CNC) machines, xy-positioning tables, robot manipulators, overhead crane mechanisms, and more. When the actuator nonlinearities exist in control systems, they may exhibit relatively large steady-state tracking error or even oscillations, cause the closed-loop system instability, and degrade the overall system performance. Proportional-derivative (PD) controller has observed limit cycles if the actuator nonlinearity is not compensated well. The problems are particularly exacerbated when the required accuracy is high, as in micropositioning devices. Due to the non-analytic nature of the actuator nonlinear dynamics and the fact that the exact actuator nonlinear functions, namely operation uncertainty, are unknown, the saturation compensation research is a challenging and important topic with both theoretical and practical significance. Adaptive control can accommodate the system modeling, parametric, and environmental structural uncertainties. With the universal approximating property and learning capability of neural network (NN), it is appealing to develop adaptive NN-based saturation compensation scheme without explicit knowledge of actuator saturation nonlinearity. In this dissertation, intelligent anti-windup saturation compensation schemes in several scenarios of nonlinear systems are investigated. The nonlinear systems studied within this dissertation include the general nonlinear system in Brunovsky canonical form, a second order multi-input multi-output (MIMO) nonlinear system such as a robot manipulator, and an underactuated system-flexible robot system. The abovementioned methods assume the full states information is measurable and completely known. During the NN-based control law development, the imposed actuator saturation is assumed to be unknown and treated as the system input disturbance. The schemes that lead to stability, command following and disturbance rejection is rigorously proved, and verified using the nonlinear system models. On-line NN weights tuning law, the overall closed-loop performance, and the boundedness of the NN weights are rigorously derived and guaranteed based on Lyapunov approach. The NN saturation compensator is inserted into a feedforward path. The simulation conducted indicates that the proposed schemes can effectively compensate for the saturation nonlinearity in the presence of system uncertainty

Adaptive Neural Network Feedforward Control for Dynamically Substructured Systems

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Stability of systmes with deadzone nonlinearity

This paper studies the stabilization of control systems with deadband nonlinearity of unknown characteristics. A novel approach to treat the deadband is first proposed using techniques of saturation compensation, assuming crude estimates of gains and bounds for the saturation limiter. Stability of the compensated system is analyzed, revealing that for systems of conditional stability in the presence of deadzone nonlinearity, their stabilization is not possible for small inputs. However, proper stabilization always exists for regulatory control of large enough input magnitude. Simulated examples are given to illustrate the main results.published_or_final_versio

Improving Aeromagnetic Calibration Using Artificial Neural Networks

The Global Positioning System (GPS) has proven itself to be the single most accurate positioning system available, and no navigation suite is found without a GPS receiver. Even basic GPS receivers found in most smartphones can easily provide high quality positioning information at any time. Even with its superb performance, GPS is prone to jamming and spoofing, and many platforms requiring accurate positioning information are in dire need of other navigation solutions to compensate in the event of an outage, be the cause hostile or natural. Indeed, there has been a large push to achieve an alternative navigation capability which performs nearly as well as GPS. One navigation method which has shown promise to increase navigation performance of aircraft utilizes magnetic anomalies[1] - local variations in the Earth\u27s crust - to discern position. One significant drawback to this approach is the magnetic disturbance generated by the aircraft itself, which must be accounted for and eliminated. Current calibration procedures involve placing the magnetometer on a long stinger far from the aircraft body to minimize interference with the magnetic anomaly signal. While some aircraft permit the addition of stingers, many do not. No calibration procedure exists which satisfies potential location restraints of the magnetometer and the calibration problem for these less ideal aircraft, especially potentially magnetically noisy platforms such as an F-16. Current linear models which attempt to correct mild disturbance fields on more ideal aircraft exist. We propose that a more sophisticated model is necessary to make magnetic navigation platform agnostic. Specifically, we show that a deep learning approach and the utilization of more inputs than the current de facto calibration procedure - known in the literature as Tolles-Lawson - can achieve a 90% reduction in platform based magnetic disturbance signals

Design of a pressure control system with dead band and time delay

This paper investigates the control of pressure in a hydraulic circuit containing a dead band and a time varying delay. The dead band is considered as a linear term and a perturbation. A sliding mode controller is designed. Stability conditions are established by making use of Lyapunov Krasovskii functionals, non-perfect time delay estimation is studied and a condition for the effect of uncertainties on the dead zone on stability is derived. Also the effect of different LMI formulations on conservativeness is studied. The control law is tested in practice