Many important engineering systems can be classified as Euler–Lagrange (EL) systems. In this work we develop a new Proportional‐Integral‐Derivative (PID) ‐based tracking control solution for uncertain EL systems subject to actuation failures and saturation. Two set of control algorithms are developed using robust adaptive and neuroadaptive methods, which are shown to exhibit several salient features: (1) the control schemes are of PID form, hence is characterized with simplicity in structure and intuition in concept; (2) the PID gains in the scheme are automatically updated by analytic algorithms with no need for manual tuning, rendering the control scheme more user‐friendly; and (3) the developed control algorithms are robust against nonvanishing disturbances, adaptive to unknown virtual parameter and immune to partial actuation effectiveness faults.

This article presents a robust adaptive control scheme for six-degrees of freedom (DOF) unmanned aerial vehicles (UAVs) in the presence of modeling uncertainties and actuation faults. The proposed control is in proportional-derivative (PD) form, and is able to tolerate actuation faults yet ensure stability and transient performance without the need for the detail model information of UAV. Furthermore, the PD control scheme exploited adaptively self-tuning PD gains, which avoids the ad-hoc and time-consuming trial and error process for gain determination as commonly required in traditional PD control, thus is design-friendly and low-cost, rendering the control algorithms easy and straightforward for programming and implementation. Both theoretical analysis and numerical simulation validate the effectiveness of the proposed control method.

This paper considers the problem of a polytopic approach for Linear parameter Variation network control system with the sensor failure case. Based on Bounded Real Lemma of control theory, the sufficient condition of robust stability of a LPV augmented network control system with tracking error and the sensor failure is addressed; Using Linear Matrix Inequality convex optimal technique, the feasible solution of state feedback controller is obtained. The simulation of a inverted pendulum model shows that the presented method is feasible and effective.

This paper presents an active vibration control scheme for uncertain structural system. The state feedback controller is designed in term of Hinf robust control theory and the optimal solu- tion is obtained by using LMI convex optimal technique. A numerical example of three-degree-of- freedom system is taken to verify the proposed approach.The simulations demonstrate the effective- ness and feasibility.

A new control algorithm combing Reduced-order observer, LQG and Fuzzy Logic Controller (RLFLC) is proposed to compromise the classical suspension conflict between riding comfort and driving safety. The RLFLC optimizes the weights of the performance indexes on line in accordance with variational suspension deflection and body acceleration to schedule the gain of LQG controller dynamically for achieving multiple control objectives. In particular, a reduced-order observer is introduced to estimate some state variables which are difficult to measure. Compared with the passive suspension and the conventional LQG control system, the simulation results show that RLFLC can be adaptive to vehicle speed and road conditions to improve not only the riding comfort at low speeds, but also driving safety at high speeds without violating the given suspension deflection limit