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Two memoryless adaptive observers are proposed for systems with unknown time-delays and nonlinearities, in which time-delays are not used in the observer. Using high order neural networks (HONN), the precise system model, the Lipschitz, linear-in-parameter or norm-bounded assumptions of nonlinear functions are not needed. A robust term based on the matching condition is introduced in the first observer,...
Fed-batch fermentation processes are common methods of producing biological recombinant from different microorganisms. Model-based control of bioprocesses is a difficult task due to the challenges associated with bioprocess modeling. The paper deals with the multilayer perceptron neural network modeling of fed-batch cultivation of E. coli BL21 (DE3) [pET3a-ifn??] under maximum attainable specific...
In this paper, a general design approach is proposed to derive the feedforward control law in feedback-feedforward control systems. This design approach is based on the concept of `control equilibrium point'. In this design approach, the feedback controller generates the transient control command and the feedforward controller generates the steady state one. Using the proposed feedforward controller,...
Nowadays, many new polymer materials have been widely used in different chemical production areas. The polymerization temperature is an important parameters of process control, but has characteristics of time-delay and inertial, so the ordinary PID control is difficult to obtain good effect. In order to control the system steadily and accurately, we suggest a compound control method, which combines...
An improved minimal resource allocating network (IMRAN) learning algorithm is developed for constructing radial basis function (RBF) network. The RBF network is adjusted on-line for both network structure and connecting parameters. Based on the proposed sequential learning algorithm, a direct inverse control strategy is introduced and applied to ship course-keeping control. Simulation results of ship...
This paper proposes a neural network based traffic signal controller, which eliminates most of the problems associated with TRPS mode of the closed loop system. Instead of storing timing plans for different traffic scenarios, which requires clustering and threshold calculations, the proposed approach uses an ANN model that produces optimal plans based on optimized weights obtained through its learning...
The miniature unmanned helicopter exhibits a complex and nonlinear dynamic behavior, open- loop unstable and a high degree of inter-axis coupling. This paper describes a hybrid PID velocity control method based on single neuron for an unmanned helicopter. This method possesses both the simple structure of PID controller, and adjusting papameters on-line by intelligent technique. The application results...
This paper presents a new approach for on-line identification of an exact affine model for single-input, single- output (SISO) processes with nonlinear and time-varying behaviors. For this purpose, a modified growing and pruning algorithm for radial basis function (MGAP-RBF) neural network is used for affine modeling of the SISO nonlinear and time-varying processes. The extended Kalman filter (EKF)...
The Archimedes wave swing (AWS) is a a fully- submerged wave energy converter (WEC), that is to say, a device that converts the kinetic energy of sea waves into electricity. A first prototype of the AWS has already been built and tested. This paper presents simulation results of the performance of several control strategies applied to this device, including PID control, reactive control, phase and...
This paper deals with intelligent controller design using artificial neural networks (ANN) in the role of feedback controllers. Neural controllers are built up and trained as inverse neural process models. Their performance and robustness are, gradually, improved and augmented by introducing, first, an adaptive simple integrator and, then, a controller with fuzzy integrator part. The proposed ANN...
In recent years there has been a great effort to convert the existing air traffic control system into a novel system known as Free Flight. Free Flight is based on the concept that increasing international airspace capacity will grant more freedom to individual pilots during the enroute flight phase, thereby giving them the opportunity to alter flight paths in real time. Under the current system pilots...
An adaptive output feedback neural network controller is designed, capable of rendering affine in the control uncertain MIMO nonlinear systems passive. Consequently, a linear output feedback is employed to stabilize the system. The controlled system need not be in normal form, or have a well defined relative degree. Under a zero state detectability assumption, uniform ultimate boundedness of the system's...
In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a kinematic neural controller (KNC) and a torque neural controller (TNC) is proposed, where both the kinematic and dynamic models contains parametric and nonparametric uncertainties. The proposed neural controller (PNC) is constituted of the KNC and the TNC, and designed by use of a modeling technique...
In this paper we present a robust adaptive controller based on a neural network (NN) for a variable stiffness actuator (VSA). The controller is able to independently set the mechanical stiffness and position at the joint shaft to guarantee robustness with respect to slowly time-varying and unmodeled friction coefficients affecting the dynamics of the actuator. The lumped uncertainties of the VSA including...
The problem of time optimal magnetic attitude control is treated and an open loop solution is first obtained using a variational approach. In order to close the control loop, a neural network with time varying weights is proposed as a feedback optimal controller applicable to the time varying nonlinear system. The good robustness and low real-time computational burden of the proposed neuro-controller...
Neural network-based soft sensors are developed for quality estimation of kerosene, a refinery crude distillation unit side product. Based on temperature and flow measurements two soft sensors serve as the estimators for the kerosene distillation end point (95%) and freezing point. The neural networks are trained by the adaptive gradient method using cascade learning. Research results show possibilities...
This paper deals with model predictive control (MPC) of chemical exothermic semi-batch reactor. A first order chemical reaction is considered to be running in the reactor. The reaction is strongly exothermic so the in-reactor temperature is rising very fast due to reaction component dosing. Thus, the temperature control is necessary. The simulation model of the plant was developed in the MATLAB/Simulink...
The regulation of unknown nonlinear dynamical systems using an indirect adaptive control technique is considered in this paper. The proposed scheme uses the concept of Fuzzy Dynamical Systems (FDS) operating in conjunction with High Order Neural Network Functions (F-HONNFs). Since the plant is considered unknown, we first propose its approximation by a special form of a fuzzy dynamical system (FDS)...
In this paper, an online fault diagnosis for a complex dynamical systems integrating adaptive neuro-fuzzy inference system (ANFIS) and using independent component analysis (ICA) for feature extracting is presented. In this approach, using ICA provide salient features selected from raw measured data sets. Subsequently, the most superior extracted features are fed into multiple ANFIS in order to identify...
A complex system of coal and methane outbursts has the characteristics coupled, randomized and abrupt change for the system variant, which is a difficult problem to predict coal and methane outbursts using the accurate and effective approach. We proposed a novel generalized quantum neural network called GQNN which can be used to predict coal and methane outbursts. First, we give the influence factors...
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