Lukasz Hladowski

Multidimensional control systems: case studies in design and evaluation

Multidimensional control systems have been the subject of much productive research over more than three decades. In contrast to standard control systems, there has been much less reported on applications where the multidimensional setting is the only possible setting for design or produces implementations that perform to at least the same level. This paper addresses the latter area where case studies focusing on control law design and evaluation, including experimental results in one case, are reported. These demonstrate that movement towards the actual deployment of multidimensional control systems is increasing.

Output Information based Iterative Learning Control Law Design with Experimental Verification

This paper considers iterative learning control law design using the theory of linear repetitive processes. This setting enables trial-to-trial error convergence and along-the-trial performance to be considered simultaneously in the design. It is also shown that this design extends naturally to include robustness to unmodeled plant dynamics. The results from experimental application of these laws to a gantry robot performing a pick and place operation are given, together with a discussion of the positioning of this approach relative to alternatives and possible further research.

Using 2D systems theory to design output signal based iterative learning control laws with experimental verification

In this paper we use a 2D systems setting to develop new results on iterative learning control for linear plants, where it is well known in the subject area that a trade-off exists between speed of convergence and the response along the trials. Here we give new results by designing the control scheme using a strong form of stability for repetitive processes/2D linear systems known as stability along the pass (or trial). The resulting design computations are in terms of Linear Matrix Inequalities (LMIs) and they are also experimentally validated on a gantry robot. The control laws only use plant output information and hence the use of a state observer is avoided.

A 2D Systems Approach to Iterative Learning Control with Experimental Validation

In this paper we use a 2D systems setting to develop new results on iterative learning control for linear plants. It is well known in the subject area that a trade-off exists between speed of convergence and transient response. Here we give new results in this area by designing the control scheme using a strong form of stability for repetitive processes/2D linear systems known as stability along the pass (or trial). The resulting design computations are in terms of Linear Matrix Inequalities (LMIs) and they are also experimentally validated on a gantry robot.

A 2D systems approach to iterative learning control for discrete linear processes with zero Markov parameters

In this article a new approach to iterative learning control for the practically relevant case of deterministic discrete linear plants with uniform rank greater than unity is developed. The analysis is undertaken in a 2D systems setting that, by using a strong form of stability for linear repetitive processes, allows simultaneous consideration of both trial-to-trial error convergence and along the trial performance, resulting in design algorithms that can be computed using linear matrix inequalities (LMIs). Finally, the control laws are experimentally verified on a gantry robot that replicates a pick and place operation commonly found in a number of applications to which iterative learning control is applicable.

Repetitive process based iterative learning control designed by LMIs and experimentally verified on a gantry robot

In this paper we use a 2D systems setting to develop new results on iterative learning control for linear single-input single-output (SISO) plants, where it is well known in the subject area that a trade-off exists between speed of convergence and the response along the trials. Here we give new results by designing the control scheme using a strong form of stability for repetitive processes/2D linear systems known as stability along the pass (or trial). The design computations are in terms of Linear Matrix Inequalities (LMIs) and results from experimental verification on a gantry robot are also given.

A new iterative learning control scheme for linear time-varying discrete systems

Abstract In this paper we use a repetitive process setting to develop a new iterative learning control algorithm for plants modelled by a discrete linear time-varying state space model. As the next step in evaluating its performance, the results of a simulation based study are given where the plant model used is that obtained from frequency responses tests on a gantry robot.

Relaxed pass profile controllability of discrete linear repetitive processes

Repetitive processes are a distinct class of 2D systems (i.e. information propagation in two independent directions) of both systems theoretic and applications interest. They cannot be controlled by direct extension of existing techniques from either standard (termed 1D here) or 2D systems theory. In this paper we develop significant new results on controllability of so-called discrete linear repetitive processes. The end result is necessary and sufficient conditions for this property in terms of matrix rank based tests. The application of these tests is illustrated by a numerical example.

SCILAB compatible software for analysis and control of repetitive processes

In this paper the development of a SCILAB compatible software package for the analysis and control of repetitive processes is described. The core of the package consists of a simulation tool which enables the user to inspect the process dynamics with or without control laws applied. Reliable and numerically efficient algorithms for stability analysis and the control law design have been included. Illustrative examples are also given and areas of ongoing development are discussed

Iterative learning control laws with full dynamics

Iterative learning control can be applied to systems that execute the same finite duration task over and over again. The distinguishing feature is the use of information from previous executions to construct the input to the next one in the sequence, including time domain information that would be non-causal in standard control systems. Many algorithms or laws have been developed for an ever increasing range of applications. This paper develops a new law which is fully dynamic, not static, when implemented. Experimental verification results are also given.