Robust Adaptive Control

This paper addresses the trajectory tracking control of a nonholonomic wheeled mobile manipulator with parameter uncertainties and disturbances. The proposed algorithm adopts a robust adaptive control strategy where parametric uncertainties are compensated by adaptive update techniques and the disturbances are suppressed. A kinematic controller is first designed to make the robot follow a desired end-effector and platform trajectories in task space coordinates simultaneously. Then, an adaptive control scheme is proposed, which ensures that the trajectories are accurately tracked even in the presence of external disturbances and uncertainties. The system stability and the convergence of tracking errors to zero are rigorously proven using Lyapunov theory. Simulations results are given to illustrate the effectiveness of the proposed robust adaptive control law in comparison with a sliding mode controller.

For a single machine infinite power system with thyristor controlled series compensation (TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network (RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error, which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function, which can guarantee the uniform ultimate boundedness (UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.

This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for motion control of multilink robot manipulators. The proposed controller has the following salient features: (1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically according to their significance to the control system and the complexity of the mapped system and no predefined fuzzy

This paper presents a new approach to robust adaptive control, using fractional order systems as parallel feedforward in the adaptation loop. The problem is that adaptive control systems may diverge when confronted with finite sensor and actuator dynamics, or with parasitic disturbances. One of the classical robust adaptive control solutions to these problems makes use of parallel feedforward and simplified

Simple adaptive control procedures that do not require explicit parameter identification have been shown to be fit for control of large systems such as flexible structures. It is of interest, however, to test their robustness when fast adaptation is needed or when the resulting adaptive control system must confront finite sensor and actuator dynamics or parasitic disturbances. Flexible structures case

In this paper, we consider the problem of controlling chaos in the well-known Lorenz system. Firstly we show that the Lorenz system can be transformed into a kind of nonlinear system in the so-called general strict-feedback form. Then, adaptive backstepping design is used to control the Lorenz system with three key parameters unknown. By exploiting the property of the system, the resulting controller is singularity free, and the closed-loop system is stable globally. Simulation results are conducted to show the effectiveness of the approach.

For a class of single-input, single-output (SISO), continuous-time nonlinear systems, a neural network-based controller is presented that feedback linearizes the system. Control action is used to achieve tracking performance for a state-feedback linearizable, but unknown nonlinear system. A global stability proof is given in the sense of Lyapunov. It is shown that all the signals in the closed-loop system and the control action are GUUB. No learning phase requirement is needed and initialisation of the network is straightforward

The application of novel adaptive predictive optimal controllers of low order, that involve a multi-step cost index and future set-point knowledge, is considered. The usual predictive controller is of high order and the aim is to utilise simpler structures, for applications where PID controllers might be employed for example. A non-linear system is assumed to be represented by multiple linear discrete-time state-space mod- els, where n of these models are linearisations of the underlying non-linear system at an operating point, determined o-line. One extra model is identied on-line. The optimisation is then performed across this range of Nf + 1 models to produce a single low order control law. One advantage of this approach is that it is very straightforward to generate a much lower order predictive controller and thereby simplify implementa- tion. Also, with respect to the adaptive nature of the algorithm, the solution is rather cautious. Each new update of the controller involves ...

— In this paper, adaptive state feedback actuator failure compensator for networked control systems (NCSs) with state dependent disturbances are developed. The obstacles of NCSs such as; transmission delays and data-packets dropout induced by the insertion of data networks in the feedback adaptive control loops are also considered. Adaptive laws are designed for updating the controller parameters. Closed-loop stability is ensured. Simulation results show that the desired performance is achieved with the developed adaptive actuator failure compensation control designs. Simulation results are given to illustrate the effectiveness of our design approach.