Suppressing non-periodically repeating disturbances in mechanical servo systems

Data-based design of high-performance motion controllers

“…Within the selected class (4), one is interested in the tuning (set of θ ) that suitably realizes the given control objective. By virtue of (3), an adequate controller tuning is obtained by minimizing the model-based (MB) cost function: T , which balances importance of all frequency regions in the cost (6).…”

“…Here, L is a stable filter which purpose is to ensure the equivalence between the MB and DB costs (6) and (10), respectively. The condition of equivalence can be derived using the same strategy as in the VRFT method [7][8][9], and it will be done later on in this subsection.…”

“…Roughly speaking, in DB techniques controllers are designed without explicitly making use of parametric models, but merely based on measured signals. Here we refer to a few DB methods: DB LQG control [1], unfalsified control [2], simultaneous perturbation stochastic approximation [3], iterative feedback tuning [4], disturbance-based control [5,6], virtual reference feedback tuning [7][8][9], etc.…”

Data-based adaptive critic design for discrete-time zero-sum games using output feedback

“…Data-based approaches have been widely applied to solve industrial and real-life problems and also studied in their theoretical aspects by the research community, which incudes data-based predictive control [23], unfalsified control [22], Markov data-based linear quadratic Gaussian control [11], disturbance-based control [24], simultaneous perturbation stochastic approximation [12], virtual reference feedback tuning [25], pulse response based control [26]. In [14], databased optimal control was investigated.…”

Data-based optimal control

“…Moreover, even complex models will not cover all system dynamics, and adaptive and robust feedback control methods have not completely overcome the problem of modelling errors. The problems inherent to plant modelling are the motivation of research in the field of data-based control techniques, such as: unfalsified control [1], iterative feedback tuning [2], pulse response based control [3], Markov data-based LQG control [4], data-based predictive control [5], virtual reference feedback tuning [6], disturbance-based control [7], and simultaneous perturbation stochastic approximation [8]. In the above techniques, the plant modelling step is circumvented, and the control design is only based on input/output data measured from the plant.…”