This paper considers the set-point control problem of an uncertain six-degree-of-freedom precision positioning system, where only position measurements are available. As a solution, we present an iterative output feedback control algorithm whose learning mechanism is based on the concept of contraction mapping. Besides stability and convergence, the presented analysis includes practical aspects associated with the algorithm implementation. In particular, it is shown that: 1) the system response due to the iterative process converges to an arbitrarily small neighborhood of the desired equilibrium point, and 2) the steady-state response after each iteration is bounded inside the system traveling range. The presented control algorithm is demonstrated experimentally.

This paper presents a theoretical and experimental study on controller design for the AMBs in a small-scale flywheel energy storage system, where the main goals are to achieve low energy consumption and improved rotordynamic stability. A H-infinity optimal control synthesis procedure is defined for the permanent-magnet-biased AMB-rotor system with 4 degrees of freedom. Through the choice of design weighting functions, notch filter characteristics are incorporated within the controller to reduce AMB current components caused by rotor vibration at the synchronous frequency and higher harmonics. Experimental tests are used to validate the controller design methodology and provide comparative results on performance and efficiency. The results show that the H-infinity controller is able to achieve stable rotor levitation and reduce AMB power consumption by more than 40% (from 4.80 to 2.64 Watts) compared with the conventional PD control method. Additionally, the H-infinity controller can prevent vibrational instability of the rotor nutation mode, which is prone to occur when operating with high rotational speeds.

This paper introduces an innovative approach integrating global best (gbest) and local best (lbest) PSO communication topologies. The algorithm initiates with lbest and seamlessly transitions to gbest, with the switching rate controlled by the parameter “a”. Rational values of “a” is determined through numerical experiments. A comparative methodology employing two estimation criteria is used to showcase the improved performance of the modified PSO-based algorithms. Furthermore, the efficacy of this approach is demonstrated in addressing two optimal control problems within dynamical systems. Results highlight the modified algorithms’ superiority in terms of the total number of successful runs and statistical indicators. Consequently, these advanced algorithms prove effective for applications such as artificial neural network training, controller gains determination, and similar problem domains.