This paper deals with certain options on controlling an inverted rotary pendulum also known as the Furuta pendulum. Controlling an inverted pendulum involves two stages. The first stage is the swing up of the pendulum and the second stage is its balancing in the up-right position. The paper describes two possibilities on swinging up the pendulum. First one is the classical approach based on comparing the current total (potential and kinetic) energy of the system with the energy in its stabilized up-right position. The second option uses an exponentiation operation over the pendulum position since the trend of power law function is very convenient for determining the amount of required energy to be delivered to the system. For the purposes of balancing the pendulum in the up-right position a predictive controller based on optimal control law with perturbation was proposed, which is an LQ controller with control signal corrections when constraints are exceeded. The results are illustrated by real-time experiments on a laboratory rotary inverted pendulum setup.

This paper presents an efficient real-time implementation of embedded model predictive control, adopted in the context of active vibration control with the objective of minimizing the tip deflection of lightly damped cantilever beams. In particular, we focus on memory and time-efficient explicit solutions of the associated constrained optimal control problems that are easily implementable on low-end embedded hardware. To this end, we exploit the concept of convex lifting and show how it can be used to devise low-complexity, regionless piecewise affine controllers without any loss of optimality and performance. The efficiency of this constructive procedure is quantified via an extensive complexity analysis, evidenced by a successful practical deployment and optimal vibration control performance using a family of 32-bit ARM Cortex-M-based microcontroller platforms.

This paper presents a unified control scheme of the Pendubot based on nonlinear model predictive control (NMPC) and nonlinear moving horizon estimation (NMHE) with the objective of point-to-point tracking its unstable unforced and ultimately forced equilibrium positions. In order to implement it on this fast, underactuated mechatronic system, we employ the Gauss–Newton real-time iteration scheme tailored to obtain the efficient solution of the underlying nonlinear optimization problems via sequential quadratic programming. The control performance is experimentally assessed on a real-world laboratory setup featuring an execution timing analysis and hints how to further improve the computational efficiency of the proposed nonlinear estimation control scheme. Even nowadays, the number of practical NMPC applications in the millisecond range is still rather limited, and the presented NMHE-based NMPC of the Pendubot thus also represents a unique case study for control practitioners.

In this paper, the application of an input disturbance observer (IDO)-based control, based on a simple input observer previously proposed and used for engine control, is demonstrated in two case studies. The first case study is longitudinal aircraft control with unmodeled aerodynamic nonlinearities satisfying matching conditions. The second case study is the control of an inverted pendulum on a cart which corroborates the ease of integration of IDO-based control into more complex controllers in situations when the matching condition is not satisfied. Improved robustness is demonstrated on an experimental system including changing the pendulum weight which is shown to have no effect on the overall control performance. In both case studies, if the IDO is not applied, the control performance is poor and leads to unstable operation.