Search Results (6)

Spinal cord injury (SCI) patients require continuous rehabilitation post-discharge to ensure optimal recovery. This study investigates the effectiveness of home-based virtual reality fitness training (VRFT) as a convenient and accessible rehabilitation method for SCI patients. This randomized, double-blind, multicenter trial will enroll 120 participants, assigning them to either an 8-week VRFT program (exercise group) or a control group engaging in regular daily activities. The outcomes measured include muscle function, cardiopulmonary fitness, body composition, and physical performance. Our study will determine the safety and feasibility of VRFT in a home setting for SCI patients and evaluate whether these patients can effectively participate in such a program post-discharge. The results of this study are expected to inform future exercise protocols for SCI rehabilitation, offering valuable insights into the utility of VRFT as a therapeutic tool. Full article

Data-driven controller synthesis methods use input/output information to find the coefficients of a proposed control architecture. Virtual Reference Feedback Tuning (VRFT) is one of the most popular frameworks due to its simplicity and one-shoot synthesis style based on open-loop system response for classic regulators such as PI or PID. This paper presents a recursive VRFT framework to extend VRFT into high-order controllers with more complex structures. The framework first defines a reference model and controller structure, then uses the open-loop data to compute the virtual reference and error signals, and, finally, uses these to find the controller parameters via an optimization algorithm. Likewise, the recursive VRFT controller performance is improved by adding a model-based feedforward loop to improve reference signal tracking. The recursive method is tested to design a temperature uniformity control system. The obtained results show that the recursive VRFT with a feedforward improves the system response while allowing more complex controller synthesis. Full article

The main objective of this work was to develop a tuning method for PID controllers suitable for use in an industrial environment. Therefore, a computationally simple tuning method is presented based on a simple experiment on the process without requiring any input from the user. Essentially, the method matches the closed-loop response to the response obtained in the steady-state change experiment. The proposed method requires no prior knowledge of the process and, in its basic form, only the measurement of the change in the steady state of the process in the manually or automatically performed experiment is needed, which is not limited to step-like process input signals. The user does not need to provide any prior information about the process or any information about the closed-loop behavior. Although the control loop dynamics is not defined by the user, it is still known in advance because it is implicitly defined by the process open-loop response. Therefore, no exaggerated control signal swings are expected when the reference signal changes, which is an advantage in many industrial plants. The presented method was designed to be computationally undemanding and can be easily implemented on less powerful hardware, such as lower-end PLC controllers. The work has shown that the proposed model-free method is relatively insensitive to process output noise. Another advantage of the proposed tuning method is that it automatically handles the tuning of highly delayed processes, since the method discards the initial process response. The simplicity and efficiency of the tuning method is demonstrated on several process models and on a laboratory thermal system. The method was also compared to a tuning method based on a similar closed-loop criterion. In addition, all necessary Matlab/Octave files for the calculation of the controller parameters are provided online. Full article

In this paper, a novel Virtual State-feedback Reference Feedback Tuning (VSFRT) and Approximate Iterative Value Iteration Reinforcement Learning (AI-VIRL) are applied for learning linear reference model output (LRMO) tracking control of observable systems with unknown dynamics. For the observable system, a new state representation in terms of input/output (IO) data is derived. Consequently, the Virtual State Feedback Tuning (VRFT)-based solution is redefined to accommodate virtual state feedback control, leading to an original stability-certified Virtual State-Feedback Reference Tuning (VSFRT) concept. Both VSFRT and AI-VIRL use neural networks controllers. We find that AI-VIRL is significantly more computationally demanding and more sensitive to the exploration settings, while leading to inferior LRMO tracking performance when compared to VSFRT. It is not helped either by transfer learning the VSFRT control as initialization for AI-VIRL. State dimensionality reduction using machine learning techniques such as principal component analysis and autoencoders does not improve on the best learned tracking performance however it trades off the learning complexity. Surprisingly, unlike AI-VIRL, the VSFRT control is one-shot (non-iterative) and learns stabilizing controllers even in poorly, open-loop explored environments, proving to be superior in learning LRMO tracking control. Validation on two nonlinear coupled multivariable complex systems serves as a comprehensive case study. Full article

This paper proposes a neural network (NN)-based control scheme in an Adaptive Actor-Critic (AAC) learning framework designed for output reference model tracking, as a representative deep-learning application. The control learning scheme is model-free with respect to the process model. AAC designs usually require an initial controller to start the learning process; however, systematic guidelines for choosing the initial controller are not offered in the literature, especially in a model-free manner. Virtual Reference Feedback Tuning (VRFT) is proposed for obtaining an initially stabilizing NN nonlinear state-feedback controller, designed from input-state-output data collected from the process in open-loop setting. The solution offers systematic design guidelines for initial controller design. The resulting suboptimal state-feedback controller is next improved under the AAC learning framework by online adaptation of a critic NN and a controller NN. The mixed VRFT-AAC approach is validated on a multi-input multi-output nonlinear constrained coupled vertical two-tank system. Discussions on the control system behavior are offered together with comparisons with similar approaches. Full article

This paper proposes the combination of two data-driven techniques, namely virtual reference feedback tuning (VRFT) and model-Free Control (MFC) in terms of the VRFT of MFC algorithms dedicated to servo systems. VRFT ensures the automatic optimal computation of the parameters of three MFC algorithms represented by intelligent proportional (iP), intelligent proportional-integral (iPI), and intelligent proportional-integral-derivative (iPID) controllers. The combination of MFC and VRFT leads to a novel mixed MFC-VRFT approach. The approach is validated by experimental results related to the angular speed control of modular servo system laboratory equipment. The performance of the control systems with the MFC algorithms (iP, iPI, and iPID controllers) tuned by the mixed MFC-VRFT approach is compared with that of control systems with MFC algorithms tuned by a metaheuristics gravitational search algorithm (GSA) optimizer, and of control systems with I, PI and PID controllers optimally tuned by VRFT and GSA in the same optimization problem. Full article