Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance

This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The modified Elman neural network model is trained off-line and on-line stages to guarantee the outputs of the model accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results show the effectiveness of the proposed adaptive neural predictive control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances

Multi-objective evolutionary–fuzzy augmented flight control for an F16 aircraft

In this article, the multi-objective design of a fuzzy logic augmented flight controller for a high performance fighter jet (the Lockheed-Martin F16) is described. A fuzzy logic controller is designed and its membership functions tuned by genetic algorithms in order to design a roll, pitch, and yaw flight controller with enhanced manoeuverability which still retains safety critical operation when combined with a standard inner-loop stabilizing controller. The controller is assessed in terms of pilot effort and thus reduction of pilot fatigue. The controller is incorporated into a six degree of freedom motion base real-time flight simulator, and flight tested by a qualified pilot instructor

Sliding-mode neuro-controller for uncertain systems

In this paper, a method that allows for the merger of the good features of sliding-mode control and neural network (NN) design is presented. Design is performed by applying an NN to minimize the cost function that is selected to depend on the distance from the sliding-mode manifold, thus providing that the NN controller enforces sliding-mode motion in a closed-loop system. It has been proven that the selected cost function has no local minima in controller parameter space, so under certain conditions, selection of the NN weights guarantees that the global minimum is reached, and then the sliding-mode conditions are satisfied; thus, closed-loop motion is robust against parameter changes and disturbances. For controller design, the system states and the nominal value of the control input matrix are used. The design for both multiple-input-multiple-output and single-input-single-output systems is discussed. Due to the structure of the (M)ADALINE network used in control calculation, the proposed algorithm can also be interpreted as a sliding-mode-based control parameter adaptation scheme. The controller performance is verified by experimental results

Fault tolerant control for nonlinear aircraft based on feedback linearization

The thesis concerns the fault tolerant flight control (FTFC) problem for nonlinear aircraft by making use of analytical redundancy. Considering initially fault-free flight, the feedback linearization theory plays an important role to provide a baseline control approach for de-coupling and stabilizing a non-linear statically unstable aircraft system. Then several reconfigurable control strategies are studied to provide further robust control performance:- A neural network (NN)-based adaption mechanism is used to develop reconfigurable FTFC performance through the combination of a concurrent updated learninglaw. - The combined feedback linearization and NN adaptor FTFC system is further improved through the use of a sliding mode control (SMC) strategy to enhance the convergence of the NN learning adaptor. - An approach to simultaneous estimation of both state and fault signals is incorporated within an active FTFC system.The faults acting independently on the three primary actuators of the nonlinear aircraft are compensated in the control system.The theoretical ideas developed in the thesis have been applied to the nonlinear Machan Unmanned Aerial Vehicle (UAV) system. The simulation results obtained from a tracking control system demonstrate the improved fault tolerant performance for all the presented control schemes, validated under various faults and disturbance scenarios.A Boeing 747 nonlinear benchmark model, developed within the framework of the GARTEUR FM-AG 16 project “fault tolerant flight control systems”,is used for the purpose of further simulation study and testing of the FTFC scheme developed by making the combined use of concurrent learning NN and SMC theory. The simulation results under the given fault scenario show a promising reconfiguration performance

Tracking Control for Non-Minimum Phase System and Brain Computer Interface

For generations, humans dreamed about the ability to communicate and interact with machines through thought alone or to create devices that can peer into a person’s mind and thoughts. Researchers have developed new technologies to create brain computer interfaces (BCIs), communication systems that do not depend on the brain’s normal output pathways of peripheral nerves and muscles. The objective of the first part of this thesis is to develop a new BCI based on electroencephalography (EEG) to move a computer cursor over a short training period in real time. The work motivations of this part are to increase: speed and accuracy, as in BCI settings, subject has a few seconds to make a selection with a relatively high accuracy. Recently, improvements have been developed to make EEG more accurate by increasing the spatial resolution. One such improvement is the application of the surface Laplacian to the EEG, the second spatial derivative. Tripolar concentric ring electrodes (TCREs) automatically perform the Laplacian on the surface potentials and provide better spatial selectivity and signal-to-noise ratio than conventional EEG that is recorded with conventional disc electrodes. Another important feature using TCRE is the capability to record the EEG and the TCRE EEG (tEEG) signals concurrently from the same location on the scalp for the same electrical activity coming from the brain. In this part we also demonstrate that tEEG signals can enable users to control a computer cursor rapidly in different directions with significantly higher accuracy during their first session of training for 1D and 2D cursor control. Output tracking control of non-minimum phase systems is a highly challenging problem encountered in many practical engineering applications. Classical inversion techniques provide exact output tracking but lead to internal instability, whereas modern inversion methods provide stable asymptotic tracking but produce large transient errors. Both methods provide an approximation of feedback control, which leads to non robust systems, very sensitive to noise, considerable tracking errors and a significant singularity problem. Aiming at the problem of system inversion to the true system, the objective of the second part of this thesis is to develop a new method based on true inversion for minimum phase system and approximate inversion for non-minimum phase systems. The proposed algorithm is automatic and has minimal computational complexities which make it suitable for real-time control. The process to develop the proposed algorithm is partitioned into (1) minimum phase feedforward inverse filter, and (2) non-minimum phase inversion. In a minimum phase inversion, we consider the design of a feedforward controller to invert the response of a feedback loop that has stable zero locations. The complete control system consists of a feedforward controller cascaded with a closed-loop system. The outputs of the resulting inverse filter are delayed versions of the corresponding reference input signals, and delays are given by the vector relative degree of the closed-loop