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.

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This paper is devoted to the measurable disturbance rejection problem in Generalized Predictive Control (GPC) using an adaptive GPC controller, which combines the process of online system identification and the process of generalized predictive feedback controller design. In this work, the GPC algorithm is extended to the case when the measurable disturbances are available for feedforward control. The proposed adaptive GPC algorithm with/without future disturbance compensation is implemented in real time and experimentally demonstrated for the application of motor revolutions tracking when subjected to deterministic and pseudo-random disturbance signals. The constraint handling capabilities of the controller are considered as well.

This paper presents the application of a Multivariable Generalized Predictive Controller (MGPC) for simultaneous temperature and humidity control in a Heating, Ventilating and AirConditioning (HVAC) system. The multivariable controlled process dynamics is modeled using a set of MISO models on-line identified from measured input-output process data. The controller synthesis is based on direct optimization of selected quadratic cost function with respect to amplitude and rate input constraints. Efficacy of the proposed adaptive MGPC algorithm is experimentally demonstrated on a laboratory-scale model of HVAC system. To control the airconditioning part of system the designed multivariable predictive controller is considered in a cascade dual-rate control scheme with PID auxiliary controllers.

In this paper we present a real-time optimal control scheme of a Pendubot based on nonlinear model predictive control (NMPC) combined with nonlinear moving horizon estimation (NMHE). For the control of this fast, under-actuated nonlinear mechatronic system we utilize the ACADO Code Generation tool to obtain a highly efficient Gauss-Newton real-time iteration algorithm tailored for solving the underlying nonlinear optimization problems. To further improve the solvers' performance, we aim to parallelize particular algorithmic tasks within the estimation-control scheme. The overall control performance is experimentally verified by steering the Pendubot into its top unstable equilibrium. We also provide a computational efficiency analysis addressing different hardware/software configurations.