A Proposed Control Strategy for Processing Industries in Ghana

Industrial processes in Ghana are set of interrelated elements that act together to achieve desired values. These processes are nonlinear, mostly in liquid form in the continuous stirred tank reactor. Temperature and concentration are the common nonlinearities of these processes which pose serious control problems to these processes. According to experiments and theories, adaptive control mechanisms will solve the problems of these nonlinearities. Industrial practitioners must be encouraged to use adaptive control mechanisms and the gap between industry and academia must be close. Keywords: Nonlinear, Temperature, Stirred-tank, Adaptive, Process

A Nonlinear System Identification Method Based on Adaptive Neural Network

Nonlinear system identification (NSI) is of great significance to modern scientific engineering and control engineering. Despite their identification ability, the existing analysis methods for nonlinear systems have several limitations. The neural network (NN) can overcome some of these limitations in NSI, but fail to achieve desirable accuracy or training speed. This paper puts forward an NSI method based on adaptive NN, with the aim to further improve the convergence speed and accuracy of NN-based NSI. Specifically, a generic model-based nonlinear system identifier was constructed, which integrates the error feedback and correction of predictive control with the generic model theory. Next, the radial basis function (RBF) NN was optimized by adaptive particle swarm optimization (PSO), and used to build an NSI model. The effectiveness and speed of our model were verified through experiments. The research results provide a reference for applying the adaptive PSO-optimized RBFNN in other fields

Adaptive Method for the Experimental Detection of Instabilities

Motivated by numerical bifurcation detection, we present a methodology for the direct location of bifurcation points in nonlinear dynamic laboratory experiments. The procedure involves active, adaptive use of the bifurcation parameter(s) as control variable(s), coupled with the on-line identification of low-order nonlinear dynamic models from experimental time-series data. Application of the procedure to such “hard” transitions as saddle-node and subcritical Hopf bifurcations is demonstrated through simulated experiments of lumped as well as spatially distributed systems

Development of adaptive control methodologies and algorithms for nonlinear dynamic systems based on u-control framework

Inspired by the U-model based control system design (or called U-control system design), this study is mainly divided into three parts. The first one is a U-model based control system for unstable non-minimum phase system. Pulling theorems are proposed to apply zeros pulling filters and poles pulling filters to pass the unstable non-minimum phase characteristics of the plant model/system. The zeros pulling filters and poles pulling filters derive from a customised desired minimum phase plant model. The remaining controller design can be any classic control systems or U-model based control system. The difference between classic control systems and U-model based control system for unstable non-minimum phase will be shown in the case studies.Secondly, the U-model framework is proposed to integrate the direct model reference adaptive control with MIT normalised rules for nonlinear dynamic systems. The U-model based direct model reference adaptive control is defined as an enhanced direct model reference adaptive control expanding the application range from linear system to nonlinear system. The estimated parameter of the nonlinear dynamic system will be placement as the estimated gain of a customised linear virtual plant model with MIT normalised rules. The customised linear virtual plant model is the same form as the reference model. Moreover, the U-model framework is design for the nonlinear dynamic system within the root inversion.Thirdly, similar to the structure of the U-model based direct model reference adaptive control with MIT normalised rules, the U-model based direct model reference adaptive control with Lyapunov algorithms proposes a linear virtual plant model as well, estimated and adapted the particular parameters as the estimated gain which of the nonlinear plant model by Lyapunov algorithms. The root inversion such as Newton-Ralphson algorithm provides the simply and concise method to obtain the inversion of the nonlinear system without the estimated gain. The proposed U-model based direct control system design approach is applied to develop the controller for a nonlinear system to implement the linear adaptive control. The computational experiments are presented to validate the effectiveness and efficiency of the proposed U-model based direct model reference adaptive control approach and stabilise with satisfied performance as applying for the linear plant model

Task oriented nonlinear control laws for telerobotic assembly operations

The goal of this research is to achieve very intelligent telerobotic controllers which are capable of receiving high-level commands from the human operator and implementing them in an adaptive manner in the object/task/manipulator workspace. Initiatives by the authors at Integrated Systems, Inc. to identify and develop the key technologies necessary to create such a flexible, highly programmable, telerobotic controller are presented. The focus of the discussion is on the modeling of insertion tasks in three dimensions and nonlinear implicit force feedback control laws which incorporate tool/workspace constraints. Preliminary experiments with dual arm beam assembly in 2-D are presented

Experiments in Nonlinear Adaptive Control of Multi-Manipulator, Free-Flying Space Robots

Sophisticated robots can greatly enhance the role of humans in space by relieving astronauts of low level, tedious assembly and maintenance chores and allowing them to concentrate on higher level tasks. Robots and astronauts can work together efficiently, as a team; but the robot must be capable of accomplishing complex operations and yet be easy to use. Multiple cooperating manipulators are essential to dexterity and can broaden greatly the types of activities the robot can achieve; adding adaptive control can ease greatly robot usage by allowing the robot to change its own controller actions, without human intervention, in response to changes in its environment. Previous work in the Aerospace Robotics Laboratory (ARL) have shown the usefulness of a space robot with cooperating manipulators. The research presented in this dissertation extends that work by adding adaptive control. To help achieve this high level of robot sophistication, this research made several advances to the field of nonlinear adaptive control of robotic systems. A nonlinear adaptive control algorithm developed originally for control of robots, but requiring joint positions as inputs, was extended here to handle the much more general case of manipulator endpoint-position commands. A new system modelling technique, called system concatenation was developed to simplify the generation of a system model for complicated systems, such as a free-flying multiple-manipulator robot system. Finally, the task-space concept was introduced wherein the operator's inputs specify only the robot's task. The robot's subsequent autonomous performance of each task still involves, of course, endpoint positions and joint configurations as subsets. The combination of these developments resulted in a new adaptive control framework that is capable of continuously providing full adaptation capability to the complex space-robot system in all modes of operation. The new adaptive control algorithm easily handles free-flying systems with multiple, interacting manipulators, and extends naturally to even larger systems. The new adaptive controller was experimentally demonstrated on an ideal testbed in the ARL-A first-ever experimental model of a multi-manipulator, free-flying space robot that is capable of capturing and manipulating free-floating objects without requiring human assistance. A graphical user interface enhanced the robot usability: it enabled an operator situated at a remote location to issue high-level task description commands to the robot, and to monitor robot activities as it then carried out each assignment autonomously

Adaptive Neural Network Fixed-Time Control Design for Bilateral Teleoperation With Time Delay.

In this article, subject to time-varying delay and uncertainties in dynamics, we propose a novel adaptive fixed-time control strategy for a class of nonlinear bilateral teleoperation systems. First, an adaptive control scheme is applied to estimate the upper bound of delay, which can resolve the predicament that delay has significant impacts on the stability of bilateral teleoperation systems. Then, radial basis function neural networks (RBFNNs) are utilized for estimating uncertainties in bilateral teleoperation systems, including dynamics, operator, and environmental models. Novel adaptation laws are introduced to address systems' uncertainties in the fixed-time convergence settings. Next, a novel adaptive fixed-time neural network control scheme is proposed. Based on the Lyapunov stability theory, the bilateral teleoperation systems are proved to be stable in fixed time. Finally, simulations and experiments are presented to verify the validity of the control algorithm

Observer-Based Adaptive Control

The work present in this master thesis relates to output feedback adaptive control and observer design of nonlinear systems, and in particular of robot manipulators. A continuous-time velocity observer and a discrete-time adaptive velocity observer for robots are shown, and an observer backstepping controller is also proposed, which can be used together with both the observers. The resulting closed-loop system is proven to be semiglobally asymptotically stable with respect to both the velocity observation error and the tracking error, and stable with respect to the parameter estimation error. Furthermore an on-line parameter estimation method for a class of nonlinear system is presented, which can be easily extended for the robot equation. Unfortunately the way to use it in combination with the previous observer-controller has not been found and it has not been used in the experiments. In the Appendix A some technical details about the al-gorithm implementation are included, and in the Appendix B a paper already submitted to the 2002 Conference in Decision and Control is included, in which the adaptive output-feedback control scheme is extended for ship control. All the work has been conducted in the Department of Automatic Control, Lund Institute of Technology, Lund University

Modeling Human Control Behavior in Command-following Tasks

Humans interact with a variety of complex dynamic systems on a daily basis. However, they are often the lesser understood component of human-in-the-loop (HITL) systems. In this dissertation, we present the results of two HITL experiments to investigate the control strategies that humans use when performing command-following tasks. The first experiment is designed to investigate the control strategies that humans use to interact with nonlinear dynamic systems. Two groups of human subjects interact with a dynamic system and perform a command-following task. One group interacts with a linear time-invariant (LTI) dynamic system and the other group interacts with a Wiener system, which consists of the same LTI dynamics cascaded with a static output nonlinearity. In the second experiment, we examine the impacts of a relaxed command-following control objective on the control strategies used by humans. Two groups of human subjects interact with the same dynamic system and perform a command-following task; however, the groups have different control objectives. One group\u27s control objective is to follow the reference command as closely as possible at all times, while the other group\u27s control objective is to follow the reference command with some allowable error. We develop and utilize a new subsystem identification (SSID) algorithm to model control behavior of the human subjects participating in these HITL experiments. This SSID algorithm can identify the feedback and feedforward controllers used by human subjects, and is applicable to both linear and nonlinear dynamic systems. The SSID results of the first experiment indicate that adaptive feedforward inversion is the main control strategy used by human subjects for both linear and nonlinear plants. The results of the second experiment suggest that not all the human subjects who are instructed to perform a relaxed command-following task adopt adaptive feedforward inversion as their primary control strategy. The control behavior of those human subjects contains significant nonlinearities, which cannot be captured by a LTI control model. We present a nonlinear feedforward control architecture that can model several aspects of their control behavior

Adaptive control of a parallel robot via backstepping technique

Parallel robots have attracted more and more attention in recent years due to their kinematical and mechanical advantages. However the complicated high nonlinear model with unknown parameters and singularities make the control of a parallel robot much more difficult than a serial robot. Nonlinear control has been made great progress since backstepping technique was developed. Backstepping technique is a recursive design procedure and feasible for lower triangular nonlinear systems. Moreover, the adaptive backstepping is able to handle nonlinear systems with unknown parameters, which turns out to be a suitable control design methodology for parallel robots. The adaptive backstepping technique is applied to set point and tracking control of a planar parallel robot in this thesis. The dynamic model of the robot is characterized by a set of differential algebraic equations (DAEs) and further reduced to a set o f ordinary differential equations (ODEs). The inverse kinematics is also under investigation. For set point control, a model-based adaptive controller is designed based on backstepping technique, and an adaptive PD controller is also constructed for comparison. For tracking control, adaptive backstepping controller is designed based on the model with unknown parameters. The adaptive PD controller is also implemented for comparison. The performances o f the controllers are tested by experiments. Desired trajectories such as circle, line, and square are tracked in experiments for two cases: with no load and with load at the end effector. It is shown that adaptive controllers can achieve less steady state errors in set point control, and smaller tracking errors in tracking control than non-adaptive controllers, especially when there is a load attached to the end effector