Design and Implementation of Model Predictive Control for a Gyroscopic Inverted Pendulum

Abstract

This study proposes the design of an active stabilizing system (ASAS) for a single-track vehicle. Using the gyroscopic effects of two flywheels, this system can generate control torque to stabilize the vehicle in cases where there is centrifugal force of turning. To control the flywheel gimbals to generate stabilizing torque, a model predictive controller (MPC) is applied to control the system. For the controller design and performance evaluations, a model of a gyroscopic inverted pendulum is developed. Control strategies are proposed to stabilize the vehicle in the cases of straight running, circular motion, and path following. The results of the proposed stratgies when controlling the gyroscopic inverted pendulum showed good performance even with physical limitations of the control torques. In order to evaluate the real-time performance and the feasibility of the MPC, a real-time simulator is employed, which includes two embedded STM32F407 boards. The dynamic system and the control algorithms are respectively embedded into two STM32F407 boards for real-time simulation. Implementations of the MPC in this study demonstrate that the proposed controllers are feasible for real-time applications.

Applications of Gyroscopic Effect in Transportation

“…The design of an active stabilizing system (ASAS) for a singletrack vehicle is presented in the study [3]. Using the gyroscopic effects of two gyroscopes, this system can generate control torque to stabilize the vehicle in cases where there is centrifugal force of turning.…”

“…Larmor precession in atomism) and astronomy (lunisolar precession of Earth), in technology (e.g. gyroscopic effect in transportation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. ), in military (stabilization of missiles and bullets) and also in sport (flight disk), in energetics (kinetic energy accumulation by turbines, mechanical batteries).…”

Experimental Comparison of Velocity Observers: A Scissored Pair Control Moment Gyroscope Case Study

“…We denote by q a the angle of rotation between the ''north'' direction and the body A; the angle of the body B with respect to the vertical is denoted as q b ; the angle of the bodies C 1 and C 2 with respect to the body B are denoted as q 1 c and q 2 c ; and the angle of the bodies D 1 and D 2 with respect to the body C 1 and C 2 are denoted as q c = q 2 c = π/2. Note that the equilibrium position of an actual robot depends on the configuration of the legs and may be subject to external disturbances; therefore, for our cube we model the equilibrium point as q b = −π/4−e, where e is the unknown (small) bias.…”

“…widely used technological device that uses the reaction of a spinning wheel to external torques. Due to the advantages of a large ratio of produced torque to control torque and relatively low power consumption, CMGs have a wide range of applications, including bicycle stabilization [1], vessel stabilisation [2], motorcycle and robot balancing [3], balancing aid for humans and bipedal exoskeletons [4], [5], attitude steering system for the satellites [6] and underwater vehicles [7].…”

Dynamic Response of an Inverted Pendulum System in Water under Parametric Excitations for Energy Harvesting: A Conceptual Approach

“…There are a number of standard control techniques that exist in the control engineering area, which have been tested on an inverted pendulum model prior to their implementation on the real systems [4,[7][8][9][10][11]. For example, the design of an active stabilizing system for a single-track vehicle system was studied [12], and an intelligent control and balancing technique for a robotics system has been formulated [13]. A fuzzy controller has been suggested to solve the trajectory tracking problem of the inverted pendulum attached to a cart system [14], while a particle swarm optimization-based neural network controller has been designed for solving a real world unstable control challenge [15].…”