Model-Free Plant Tuning

Given a static plant described by a differentiable input-output function, which is completely unknown, but whose Jacobian takes values in a known polytope in the matrix space, this paper considers the problem of tuning (i.e., driving to a desired value) the output, by suitably choosing the input. It is shown that, if the polytope is robustly nonsingular (or has full rank, in the nonsquare case), then a suitable tuning scheme drives the output to the desired point. The proof exploits a Lyapunov-like function and applies a well-known game-theoretic result, concerning the existence of a saddle point for a min-max zero-sum game. When the plant output is represented in an implicit form, it is shown that the same result can be obtained, resorting to a different Lyapunov-like function. The case in which proper input or output constraints must be enforced during the transient is considered as well. Some application examples are proposed to show the effectiveness of the approach

Frit-based controller tuning of a dc-dc boost converter

This report presents a Fictitious Reference Iterative Tuning design for a DC-DC boost converter based on a Model-Free approach. A Fictitious Reference Iterative Tuning is a data-driven controller tuning technique that uses one-shot experimental data to construct the input controller of an undefined plant model. Fictitious Reference Iterative Tuning ensures that the plant output fits the reference model output by optimizing the performance index, which comprises a fictional reference output calculated from oneshot experimental input-output results. A DC-DC boost converter is a step-up converter with an output voltage higher than the input voltage. This converter system has a nonlinear dynamic behaviour, as it works in switch mode. The modelling of a Boost converted system is first provided to form data collection and fictitious reference signal derivation. The configuration of a nonlinear system discussed here is assumed to be known, but the parameters remain unknown. Design and simulation analyses using MATLAB software have been conducted for results validation and verification. Furthermore, we formulate the algorithm for determining the optimal controller parameters based on the Model-Free approach. Lastly, we verify and compare the proposed tuning technique’s output with any controller design techniques

Model-free two-step design for improving transient learning performance in nonlinear optimal regulator problems

Reinforcement learning (RL) provides a model-free approach to designing an optimal controller for nonlinear dynamical systems. However, the learning process requires a considerable number of trial-and-error experiments using the poorly controlled system, and accumulates wear and tear on the plant. Thus, it is desirable to maintain some degree of control performance during the learning process. In this paper, we propose a model-free two-step design approach to improve the transient learning performance of RL in an optimal regulator design problem for unknown nonlinear systems. Specifically, a linear control law pre-designed in a model-free manner is used in parallel with online RL to ensure a certain level of performance at the early stage of learning. Numerical simulations show that the proposed method improves the transient learning performance and efficiency in hyperparameter tuning of RL

Internal Model Control (IMC) - Neural Network (NN) Gain Scheduling Untuk Pengendalian Kolom Distilasi

This research is develop the alternative control algorithm using Internal Model Control - Neural Network Gain Scheduling (IMC-NNGS) to control mole fraction of methanol-water distillation column. Distillation column with L-V control strategy has pairing Xd-L and Xb-Qr. IMC performances depend on only ? tuning value or filter time constant. With ? tuning value manipulating IMC could be nonlinear control, where ? tuning value is outputs of NN that had been trained by using error variable, process variable, manipulated variable, and set point variable from plant. Gain scheduling using NN could be increase control system performance and product quality. The best IAE changing value shown at mole fraction feed increase. There are IAE equal with 0,234799 for IMC and IAE equal with 0, 00042 for IMC-NNGS. In other word IMCGS has IAE 559 times better than IMC. Beside that IMC-NNGS has faster response, offset free and robust to overcome set-point and disturbance changes

Model-free cable robot control

This paper proposes a technique to control a cable robot in the total absence of a model and its parameters. The cable robot is actuated by three motors whose data, including exact positions, pulley diameters, and nominal cable length, are unknown. We just assume to have a very rough knowledge of lower and upper bounds for the partial derivatives of the relation between the cable lengths and the end-effector space coordinates. A structured-light sensor measures the end-effector position, and the goal is to drive it to a designated point. An algorithm is proposed with guaranteed convergence based on the so-called model-free plant tuning approach. No learning stage is required. Experimental results are reported

Improved cascade control structure for enhanced performance

In conventional single feedback control, the corrective action for disturbances does not begin until the controlled variable deviates from the set point. In this case, a cascade control strategy can be used to improve the performance of a control system particularly in the presence of disturbances. In this paper, an improved cascade control structure and controller design based on standard forms, which was initially given by authors, is suggested to improve the performance of cascade control. Examples are given to illustrate the use of the proposed method and its superiority over some existing design methods

A model-free control strategy for an experimental greenhouse with an application to fault accommodation

Writing down mathematical models of agricultural greenhouses and regulating them via advanced controllers are challenging tasks since strong perturbations, like meteorological variations, have to be taken into account. This is why we are developing here a new model-free control approach and the corresponding intelligent controllers, where the need of a good model disappears. This setting, which has been introduced quite recently and is easy to implement, is already successful in many engineering domains. Tests on a concrete greenhouse and comparisons with Boolean controllers are reported. They not only demonstrate an excellent climate control, where the reference may be modified in a straightforward way, but also an efficient fault accommodation with respect to the actuators

Temperature control in transport delay systems

A control architecture is proposed for temperature control in manufacturing applications based on the internal model principle. It is applied to a problem where the material exit temperature is to be controlled by changing the transportation speed to influence the amount of heat loss. The internal model is used to achieve a fast response with minimal overshoot. The controller tuning is carried out using constraints on the sensitivity function to map out the controller parameter region to achieve this performance. The robustness of the controller to parametric uncertainty is also considered. Results are shown from the application of this controller to the temperature controller for a hot strip rolling mill

Self-tuning routine alarm analysis of vibration signals in steam turbine generators

This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm and the applicability of systems using such techniques