Robust Set-point Tracking Research Articles

A Two-Degree-of-Freedom PID Integral Super-Twisting Controller Based on Atom Search Optimizer Applied to DC-DC Interleaved Converters for Fuel Cell Applications

This paper focuses on the real-time implementation of an optimal high-performance control applied to an interleaved nonisolated DC/DC converter designed for fuel cell applications. Three-phase interleaved boost converters are utilized to minimize input current undulation, increase efficiency, and provide a high output voltage in order to ensure the performance of the FC stack. The proposed control strategy contains an outer loop that generates the reference current based on a two-degree-of-freedom PID controller. This controller provides a robust setpoint tracking and disturbance rejection, which improves the system’s response and efficiency. A fast inner regulation loop based on a super-twisting integral sliding mode (STISM) algorithm is developed to achieve a fixed converter output voltage, equitable phase current sharing, and fast regulation against load disturbances in failure operation. The STISM algorithm exhibits a rapid convergence property of the sliding mode and effectively avoids the chattering phenomena frequently observed in conventional sliding modes. The proposed controller’s gains are determined using the atom search optimization algorithm, which ensures exceptional reliability and a high degree of robustness and stability of the controllers under a variety of operational conditions. This method is inspired from the behavior of atoms and their electrons during the excitation process leading to a one-of-a-kind optimization technique which contributes to the controller’s reliability. Using Matlab-Simulink simulation tools, the efficacy and performance of the designed control have first been evaluated and assessed and compared with other optimization algorithms, and then with a dual loop based on a PID controller. Then, they have been verified by real-time hardware implementation on a 1.2 KW prototype FC converter driven by the dSPACE-1104 card under a variety of tests. The suggested approach offers impressive experimental results in dynamic and steady states.

Design and Analyse of Structured H-infinity Controller for Level Control of Nonlinear Quadruple Tank Systems

Industrial process systems operate by interacting with each other to fulfil an essential industrial task. The majority of processes have nonlinear dynamics and multiple-input multiple-output systems which make them even more difficult to design control schemes. In industrial process applications, level control is one of the important problems. This study computes a structured H-infinity controller for the level control of a nonlinear quadruple tank system. This study aims to design a simple (low-order) robust controller and compare its performance to the performance of the classical Proportional-Integral-Derivative (PID) controller. PID controllers are mostly used in industrial processes due to their simplicity of implementation. However, PID controllers have some disadvantages. One of these disadvantages is that PID controllers offer lower robustness than robust control schemes when the industrial process operates in the presence of disturbances. Simulations are conducted in MATLAB\Simulink environment. Furthermore, the performance of the proposed controller is compared with the PID controller in terms of error-dependent performance indices and time-domain specifications. The simulation results have shown that the proposed controller provides robust set-point tracking, good disturbance rejection, and handling of the parametric uncertainty properties for nonlinear quadruple tank systems in industrial processes.

L₁-Adaptive Robust Control Design for a Pressurized Water-Type Nuclear Power Plant

This work proposes adaptive control-based design strategies to control a pressurized water reactor (PWR) nuclear power plant (NPP). An L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -adaptive-based state-feedback control technique is proposed using the linear quadratic Gaussian control and projection-based adaptation laws. The control scheme possesses good robustness capabilities in handling disturbances and uncertainties. A robust L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -adaptive control technique is also proposed by combining the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -adaptive control with the loop transfer recovery (LTR) technology. The framework hence gives the strengthened robust set-point tracking performance given the matched and unmatched uncertainties and disturbances. The NPP model employed in this article is defined by five inputs, five outputs, and 38 state variables. A linear model for controller design is obtained by linearizing the nonlinear NPP model at operating conditions. Various simulations are carried out on subsystems of the NPP to verify the effectiveness of the proposed scheme. Numerical and statistical measures are computed for quantitative analysis of the controllers' performance. Several classical control design techniques are also implemented, and their performance is compared with the proposed adaptive control techniques.

Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control

Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction. The presented robust MPC scheme constitutes a one-layer approach that unifies the often separated planning and control layers, by directly computing the control command based on a reference and possibly obstacle positions. As a separate contribution, we show how the computation time of the MPC can be drastically reduced by approximating the MPC law with a NN controller. The NN is trained and validated from offline samples of the MPC, yielding statistical guarantees, and used in lieu thereof at run time. Our experiments on a state-of-the-art robot manipulator are the first to show that both the proposed robust and approximate MPC schemes scale to real-world robotic systems.

Gain Scheduling of a Robust Setpoint Tracking Disturbance Rejection and Aggressiveness Controller for a Nonlinear Process

In this paper, a robust setpoint tracking disturbance rejection and aggressiveness (RTD-A) controller is designed and developed to control the liquid level of a conical tank process. Meta-heuristic algorithms like grey wolf optimization and the genetic algorithm are used to tune the parameters of the RTD-A controller. Its performance is later compared with that of the conventional standard proportional integral derivative controller. The gain scheduled RTD-A controller is designed and implemented on a nonlinear conical tank process. Also, various performances attributes such as the integral square error, integral absolute error, integral time absolute error, rise time, and settling time are calculated for the first-order process and conical tank process. The servo responses with RTD-A are also compared against the responses recorded from the conventional control schemes.

Towards Optimal Tuning of Robust Output Feedback MPC

Optimal design and tuning of model predictive controllers is important as it significantly influences the achievable performance. We consider the problem of tuning a tube based robust predictive output feedback controller, which utilizes a linear feedback - the tube controller - to take the uncertainties into account. The design and the tuning of this linear feedback has a big influence on the tube and thus the resulting overall performance. We propose a tuning exploiting the Youla parametrization leading to a convex optimization problem. Conditions are provided to guarantee robust constraint satisfaction and robust set-point tracking. The results are illustrated by examples.

Robust output tracking control for a class of uncertain nonlinear systems using extended state observer

This paper investigates the global robust output tracking control problem via output feedback for a class of nonlinear uncertain systems with the integral input-to-state stable (iISS) dynamic uncertainties. By performing the coordinates transformation and employing an extended state observer (ESO), it can be seen that the robust setpoint tracking control is well solved. The proposed control strategy simplifies the control design procedure. This control scheme finds its application in the mass-spring mechanical system. It is shown that the asymptotic tracking control for any desired displacement can be achieved in the mass-spring mechanical system in the case of unknown parameters. The simulation results demonstrate that the proposed control scheme has a better tracking performance. This verifies the effectiveness of the developed method.

Singular perturbation methods in the design of tracking systems incorporating error-actuated controllers for plants with explicit actuator dynamics

Singular perturbation methods are used to provide a basis for the design of robust set-point tracking systems incorporating error-actuated controllers for plants with explicit actuator dynamics. The conditions are identified under which the common practice of neglecting actuator dynamics is justified in the design of both continuous-time and discrete-time tracking systems.