An introduction to heat transfer

Adaptive control processes

This paper first offers a transversal view through microfluidics theory and applications, starting from a brief overview on microfluidic systems and related theoretical issues, covering different kinds of phenomena, from continuous to multiphase flow. Multidimensional models, from lumped parameters to numerical models and computational solutions, are then considered as preliminary tools for the characterization of spatio-temporal dynamics in microfluidic flows. Following these, experimental approaches through original monitoring opto-electronic interfaces and systems are discussed. Finally, a vision of two phase microfluidic phenomena is given through nonlinear analyses applied to experimental time series.

Reinforcement Learning and Adaptive Dynamic Programming for Feedback Control

An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS) The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers

Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay.

The control of indoor thermal comfort conditions: introducing a fuzzy adaptive controller

In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control.

Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

Active vibration control of a flexible one-link manipulator using a multivariable predictive controller

This paper presents a unique approach for active vibration control of a one-link flexible manipulator. The method combines a finite element model of the manipulator and an advanced model predictive controller to suppress vibration at its tip. This hybrid methodology improves significantly over the standard application of a predictive controller for vibration control. The finite element model used in place of standard modelling in the control algorithm provides a more accurate prediction of dynamic behavior, resulting in enhanced control. Closed loop control experiments were performed using the flexible manipulator, instrumented with strain gauges and piezoelectric actuators. In all instances, experimental and simulation results demonstrate that the finite element based predictive controller provides improved active vibration suppression in comparison with using a standard predictive control strategy.

Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator

A simple, yet robust and stable alternative to proportional, integral, derivative (PID) gain scheduling is developed using fuzzy logic. This fuzzy gain scheduling allows simple online duplication of PID control and the online improvement of PID control performance. The method is demonstrated with a physical model where PID control performance is improved to levels comparable to model predictive control. The fuzzy formulation is uniquely characterized by; (i) one fuzzy input variable involving the PID manipulated variable, (ii) two parameters to be tuned, while previously tuned PID parameters are retained, and (iii) a gain scheduling differential equation which relates the fuzzy and conventional PID manipulated variables and enables fuzzy gain scheduling.

PID gain scheduling using fuzzy logic.

In this paper, a singularity-free adaptive fuzzy backstepping control (AFBC) scheme is presented for uncertain nonlinear SISO systems with triangular structure by using dynamic surface control (DSC) design technique. As the intelligent approximator, fuzzy logic systems are employed to approximate the continuous nonlinear unknown functions in the uncertain systems. By utilizing DSC design technique into AFBC design framework, the proposed intelligent-based adaptive control system can overcome the problem of “explosion of complexity” inherent in the backstepping design method. Moreover, the further benefits of the proposed intelligent-based adaptive control method are that the control singularity problem is completely prevented and the upper bounds of the time differential of the gain functions are no longer needed during the control scheme design. Based on the backstepping technique, it can be proven that the closed-loop system is stable in the sense that all the variables are guaranteed to be bounded. The simulation examples demonstrate and validate the effectiveness of the proposed intelligent-based adaptive control method.

Rickey Dubay

Papers

Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

Active vibration control of a flexible one-link manipulator using a multivariable predictive controller

Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator

PID gain scheduling using fuzzy logic.

Adaptive fuzzy backstepping control for a class of uncertain nonlinear strict-feedback systems based on dynamic surface control approach