A Look-Up Table Based Fractional Order Composite Controller Synthesis Method for the PMSM Speed Servo System

Considering the performance requirements in actual applications, a look-up table based fractional order composite control scheme for the permanent magnet synchronous motor speed servo system is proposed. Firstly, an extended state observer based compensation scheme was adopted to suppress the motor parametric uncertainties and convert the speed servo plant into a double-integrator model. Then, a fractional order proportional-derivative (PDμ) controller was adopted as the speed controller to provide the optimal step response performance for the servo system. A universal look-up table was established to estimate the derivative order of the PDμ controller, according to the optimal samples collected by an improved differential evolution algorithm. With the look-up table, the optimal PDμ controller can be tuned analytically. Simulation and experimental results show that the servo system using the composite control scheme can achieve optimal tracking performance and has robustness to the motor parametric uncertainties and disturbance torques.

Adequacy of Effective Born for electroweak effects and TauSpinner algorithms for high energy physics simulated samples

Matching and comparing the measurements of past and future experiments call for consistency checks of electroweak (EW) calculations used for their interpretation. On the other hand, new calculation schemes of the field theory can be beneficial for precision, even if they may obscure comparisons with earlier results. Over the years, concepts of Improved Born, Effective Born, as well as of effective couplings, in particular of $$\sin ^2\theta _W^{{\textit{eff}}}$$ sin 2 θ W eff mixing angle for EW interactions, have evolved. In our discussion, we use four versions of EW library for phenomenology of practically all HEP accelerator experiments over the last 30 years. We rely on the codes published and archived with the Monte Carlo program for $$e^+e^- \rightarrow f {\bar{f}} n(\gamma )$$ e + e - → f f ¯ n ( γ ) and available for the as well. re-weighs generated events for introduction of EW effects. To this end, is first invoked, and its results are stored in data file and later used. Documentation of upgrade, to version 2.1.0, and that of its new arrangement for semi-automated benchmark plots are provided. In our paper, focus is placed on the numerical results, on the different approximations introduced in Improved Born to obtain Effective Born, which is simpler for applications of strong or QED processes in pp or $$e^+e^-$$ e + e - colliders. The $$\tau $$ τ lepton polarization $$P_{\tau }$$ P τ , forward–backward asymmetry $$A_{{\textit{FB}}}$$ A FB and parton-level total cross section $$\sigma ^{{\textit{tot}}}$$ σ tot are used to monitor the size of EW effects and effective $$\sin ^2\theta _W^{{\textit{eff}}}$$ sin 2 θ W eff picture limitations for precision physics. Collected results include: (i) Effective Born approximations and $$\sin ^2\theta _W^{{\textit{eff}}}$$ sin 2 θ W eff , (ii) differences between versions of EW libraries and (iii) parametric uncertainties due to, for example, $$m_t$$ m t or $$\Delta \alpha _h^{(5)}(s)$$ Δ α h ( 5 ) ( s ) . These results can be considered as benchmarks and also allow to evaluate the adequacy of Effective Born with respect to Improved Born. Definitions are addressed too.

Robust Stabilization via Super-Stable Systems Techniques

In this paper, we propose a direct method for the synthesis of robust systems operating under parametric uncertainty of the control plant model. The developed robust control procedures are based on the assumption that the structural properties of the nominal system are conservated over the entire range of parameter changes. The invariant-to-parametric-uncertainties transformation of the initial model to a regular form makes it possible to use the concept of super-stable systems for the synthesis of a stabilizing feedback. It is essential that the synthesis of super-stable systems is carried out not on the basis of assigning eigenvalues to the matrix of the close-loop system, but in terms of its elements. The proposed approach is applicable to a wide class of linear systems with parametric uncertainties and provides a given degree of stability.

Safe Model-Based Reinforcement Learning for Systems With Parametric Uncertainties

Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/or execution phases. In general, safety guarantees are critical in reinforcement learning when the system is safety-critical and/or task restarts are not practically feasible. In optimal control theory, safety requirements are often expressed in terms of state and/or control constraints. In recent years, reinforcement learning approaches that rely on persistent excitation have been combined with a barrier transformation to learn the optimal control policies under state constraints. To soften the excitation requirements, model-based reinforcement learning methods that rely on exact model knowledge have also been integrated with the barrier transformation framework. The objective of this paper is to develop safe reinforcement learning method for deterministic nonlinear systems, with parametric uncertainties in the model, to learn approximate constrained optimal policies without relying on stringent excitation conditions. To that end, a model-based reinforcement learning technique that utilizes a novel filtered concurrent learning method, along with a barrier transformation, is developed in this paper to realize simultaneous learning of unknown model parameters and approximate optimal state-constrained control policies for safety-critical systems.