J. van Amerongen

Adaptive steering of ships-A model reference approach

This paper describes the application of model reference adaptive control (MRAS) to automatic steering of ships. The main advantages in this case are the simplified controller adjustment which yields safer operation and the decreased fuel cost. After discussion of the mathematical models of process and disturbances, criteria for optimal steering are defined. Algorithms are given for direct adaptation of the controller gains, applicable after setpoint changes, as well as for identification and adaptive state estimation, to be used when the input is constant. Solutions for applying MRAS to a certain class of nonlinear systems are dealt with. Full-scale trials at sea and tests with a scale model in a towing tank are described. It is shown that the autopilot designed indeed has the desired properties. Fuel savings up to 5% in comparison to conventional PID control are demonstrated. These savings are mainly possible because of the adaptive state estimator.

Model reference adaptive control of a gantry crane scale model

A method of reference model decomposition, an extension of model reference adaptive control, is presented. The decomposition method can be regarded as a way of including knowledge about the structure and parameters of unmodeled dynamics in the adaptive system, making it possible to choose a lower order controller which is only equipped for the nominal process part. To illustrate the decomposition method, an adaptive controller for a scale model of a gantry crane is presented. Simplifying and linearizing the mathematical equations describing the crane yields a fourth-order model, of which the dynamics of the load swing take two. A standard adaptive control algorithm needs eight parameters, which in practice yields unacceptable behavior. The decomposition method allows the use of only two adjustable parameters, and real-time experiments showing practical results obtained with the method are described.<<ETX>>

Rudder roll stabilization for ships

This paper describes the design of an autopilot for rudder roll stabilization for ships. This autopilot uses the rudder not only for course keeping but also for reduction of the roll. The system has a series of properties which make the controller design far from straightforward: the process has only one input (the rudder angle) and two outputs (the heading and the roll angle); the transfer from rudder to roll is non-minimum-phase; because large and high-frequency rudder motions are necessary, the non-linearities of the steering machine cannot be disregarded; the disturbances caused by the waves vary considerably in amplitude and frequency spectrum. In order to solve these problems a new approach to the LQG method has been developed. The control algorithms were tested by means of computer simulations, scale-model experiments and full-scale trials at sea. The results indicate that a rudder roll stabilization system is able to reduce the roll as well as a conventional fin stabilization system, while it requires less investments. Based on the results obtained in this project the Royal Netherlands Navy has decided to implement rudder roll stabilization on a series of ships under construction at this moment.

Model reference adaptive control of a direct-drive DC motor

An adaptive time-optimal position controller for a direct-drive DC motor with a design based on the model reference adaptive approach is presented. The high-acceleration torque of DC motors with permanent magnets permits direct coupling of the load to the motor axis, avoiding the use of a transmission with its inherent disadvantages (such as backlash and friction). However, direct coupling induces a large sensitivity to load variations, so the desired response and the reference model in the adaptive controller are adjusted to the motor capabilities. This is achieved by estimating the load inertia by means of a least-squares method and adjusting the reference model accordingly. The controller is tested on a direct-drive motor, and the results are compared with those obtained with a fixed proportional-integral-derivative controller.<<ETX>>

Learning feedforward controller for a mobile robot vehicle

This paper describes the design and realisation of an on-line learning posetracking controller for a three-wheeled mobile robot vehicle. The controller consists of two components. The first is a constant-gain feedback component, designed on the basis of a second-order model. The second is a learning feedforward component, containing a single-layer neural network, that generates a control contribution on the basis of the desired trajectory of the vehicle. The neural network uses B-spline basis functions, enabling a computationally fast implementation and fast learning. The resulting control system is able to correct for errors due to parameter mismatches and classes of structural errors in the model used for the controller design. After sufficient learning, an existing static gain controller designed on the basis of an extensive model has been outperformed in terms of tracking accuracy.

Robust tracking control of two-degrees-of-freedom mobile robots

A robust tracking controller for a mobile robot with two degrees of freedom has been developed. It is implemented and tested on a real mobile robot. Where other controllers show decreasing performance for low reference velocities, the performance of this controller depends only on the geometry of the reference trajectory. This allows accurate positioning at low speeds, close to obstacles. The dynamics of the velocity-controlled mobile robot are considered as perturbed unity transfer from input velocity to actual velocity. It is shown that the tracking controller is robust with respect to these perturbations.

A supervisor for control of mode-switch processes

Many processes operate only around a limited number of operation points. In order to have adequate control around each operation point, and adaptive controller could be used. When the operation point changes often, a large number of parameters would have to be adapted over and over again. This makes application of conventional adaptive control unattractive, which is more suited for processes with slowly changing parameters. Furthermore, continuous adaptation is not always needed or desired. An extension of adaptive control is presented, in which for each operation point the process behaviour can be stored in a memory, retrieved from it and evaluated. These functions are co-ordinated by a ?supervisor?. This concept is referred to as a supervisor for control of mode-switch processes. It leads to an adaptive control structure which quickly adjusts the controller parameters based on retrieval of old information, without the need to fully relearn each time. This approach has been tested on experimental set-ups of a flexible beam and of a flexible two-link robot arm, but it is directly applicable to other processes, for instance, in the (petro) chemical industry.

Learning feedforward control of a flexible beam

Servo control is usually done by means of model-based feedback controllers, which has two difficulties: 1) the design of a well performing feedback controller requires extensive and time consuming modelling of the process; and 2) by applying feedback control a compromise has to be made between performance and robust stability. The learning feedforward controller (LFFC) may help to overcome these difficulties. The LFFC consists of a feedback and a feedforward controller. The feedback controller is designed such that robust stability is guaranteed, while the performance is obtained by the feedforward controller. The feedforward controller is a function approximator that is adapted on the basis of the feedback signal. The LFFC is applied to a flexible robot arm, which has complex dynamics and unknown properties, such as friction. A stability analysis of the (idealised) LFFC is presented. Simulation experiments (with a non-idealised LFFC) confirm the results of this analysis and show that without extensive modelling a good performance can be obtained.

Transputer Based Control of Mechatronic Systems

The design of a control system is not finished with the derivation of the necessary control algorithms. The system designer has to schedule all control and calculation tasks within the sampling interval of the system. Higher sampling frequencies often improve the system performance. On the other hand, more sophisticated control algorithms require more computing time thus reducing the obtainable sampling frequencies. In this paper a systematic approach to obtain a design optimum is given. The design method is illustrated with the control of a flexible robot arm.

INTELLIGENT ADAPTIVE CONTROL OF MODE-SWITCH PROCESSES

Abstract The intelligence of controllers has increased over the decades. However, the number of applications of adaptive controllers is still restricted, due to practical limits of the implemented continuous adaptation. For processes which operate only in a limited number of modes (so called mode-switch processes), constant adaptation is not needed or desired. In this paper an intelligent extension of adaptive control will be presented, in which process behaviour can be stored in a memory, retrieved from it and evaluated for each mode of operation. This intelligent memory concept leads to an adaptive control structure which, after a learning phase, quickly adjusts the controller parameters based on retrieval of old information, without the need to relearn every time. This approach has been tested on a simulation model of an assembly robot, but it is directly applicable to many processes in the (petro)chemical industry.