TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed netw...

The circuit optimization process is formulated as a dynamic controllable system. A special control vector is defined to redistribute the compute expense between a network analysis and a parametric optimization. This redistribution permits the minimization a computer time. The problem of a minimal-time circuit optimization can be formulated in this case as a classical problem of the optimal cont...

fuzzy control systems have had various applications in a wide range of science and engineering in recent years. since an unstable control system is typically useless and potentially dangerous, stability is the most important requirement for any control system (including fuzzy control system). conceptually, there are two types of stability for control systems: lyapunov stability (a special case ...

Let ẋ = f(x, u) be a general control system; the existence of a smooth control-Lyapunov function does not imply the existence of a continuous stabilizing feedback. However, we show that it allows us to design a stabilizing feedback in the Krasovskii (or Filippov) sense. Moreover, we recall a definition of a control-Lyapunov function in the case of a nonsmooth function; it is based on Clarke’s g...

This paper presents a new robust fuzzy control of energy management strategy for the stand-alone hybrid power systems. It consists of two levels named centralized fuzzy supervisory control which generates the power references for each decentralized robust fuzzy control. Hybrid power systems comprises: a photovoltaic panel and wind turbine as renewable sources, a micro turbine generator and a ba...

Stability A globally asymptotically stable equilibrium is a state with the property that all solutions converge to this state, with no large excursions. Stabilization A system is stabilizable (with respect to a given state) if it is possible to find a feedback law that renders that state a globally asymptotically stable equilibrium. Lyapunov and control-Lyapunov functions A controlLyapunov func...

Machine learning-based techniques have recently been adapted to solve control problems. The Neural Lyapunov Control (NLC) method is one such example. This approch combines Artificial Networks with Satisfiability Modulo Theories (SMT) solvers synthesise stabilising laws and prove their formal correctness. formers are trained over a dataset of state-space samples generate candidate functions, whi...

In this paper we present a theoretical and experimental result on the control of multi-agent non-holonomic systems. We design and implement a novel decentralized control scheme that achieves dynamic formation control and collision avoidance for a group of non-holonomic robots. First, we derive a feedback law using Lyapunov-type analysis that guarantees collision avoidance and tracking of a refe...

Rapid state control of quantum systems is significant in reducing the influence of relaxation or decoherence caused by the environment and enhancing the capability in dealing with uncertainties in the model and control process. Bang-bang Lyapunov control can speed up the control process, but cannot guarantee convergence to a target state. This paper proposes two classes of new Lyapunov control ...

[15] and [12], and a Lyapunov function is proposed to study the convergence of the consensus algo­ rithms. In [6] and [20], necessary and sufficient conditions for an appropriate decentralized linear stabilizing feedback are established. In [16], the problem of flocking with obstacles is addressed, where flocking is defined as achieving both structural and navigational stability. The sys­ tems ...