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We use non-binary coding, elitist strategy, increasing mutation rate, extinction, and immigration strategy to improve the simple genetic algorithms in this study. We expect that the search technique can avoid falling into the local optimum due to the premature convergence, and purse the chance that finding the near-optimal parameters in the larger searching space could be obviously increased. We utilize SIRMs(Single Input Rule Modules) fuzzy controller for the stabilization control of inverted pendulum systems, and the dynamic importance degrees are built such that the angular control of the pendulum takes priority over the position control of the cart. We utilize modified genetic algorithms(MGA) to automatically tuning scaling factors of SIRMs fuzzy controller. From computer simulations, the pendulum control and the cart position control can fastly be stabilized.

The thesis deals with the planar problem regarding balancing of an inverted pendulum whose real model is situated in the laboratory A1/731a. The goal of this thesis is to build up the simulation model in the program Matlab Simulink and compare the attributes of the model with the real pendulum. The next step is to prove a regulation of the model in Matlab Simulink and find the way of controlling the real model by PLC on the basis of results found within the simulation.

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This thesis focuses on implementing a program, using Python and the symbolic package SymPy, to evaluate balancing of a humanoid robot modelled as inverted pendulums. The balancing algorithm used to evaluate the program is the feedback controller LQR. The program has successfully implemented a working LQR algorithm together with features such as underactuation and a tilting plane as disturbance. We have shown that the energy is conserved for the falling pendulums and that it is possible to predict the behavior for certain parameter values of the pendulums, thus confirming that the program is working correctly. Furthermore we have shown that a fully-actuated system is more controllable than an under-actuated system, and for each actuator that is removed, the system becomes less controllable. Finally we discuss the program performance where some concern is given toward the seemingly poor execution time of the program. The program has been tested for up to five pendulums with successful results. Most of the results however, are revolving around three pendulum systems.

This thesis focuses on implementation of the swing-up, switching and stabilizing controllers for the rotary inverted pendulum. An energy based method to swing-up the pendulum and a state feedback controller to keep the pendulum in the upright position are employed. The mixed H2/H∞; state feedback controller is used to stabilize the pendulum with reduced oscillations. The results have been compared with the standard full state feedback and LQR. The Quanser rotary inverted pendulum is used as the testbed. All controllers are implemented in real-time using dSPACE 1104 rapid prototyping system. Microstick II with dsPIC33FJ128MC802 and Simulink embedded target for Microchip® is used as a standalone way to implement the controllers.

Self-balancing robots are unique mobile platforms that stay upright on two wheels using a closed-loop control system. They can turn on the spot using differential steering and have compact form factors that limit their required floor space. However they have major limitations keeping them from being used in real world applications: they cannot stand-up on their own, climb stairs, or overcome obstacles. They can fall easily if hit or going onto a slippery surface because they rely on friction for balancing. The first part of this research proposes a novel design to address the above mentioned issues related to stair-climbing, standing-up, and obstacles. A single revolute joint is added to the centre of a four-wheel drive robot onto which an arm is attached, allowing the robot to successfully climb stairs and stand-up on its own from a single motion. A model and simulation of the balancing and stair-climbing process are derived, and compared against experimental results with a custom robot prototype. The second part, a control system for an inverted pendulum equipped with a gyroscopic mechanism, was investigated for integration into self-balancing robots. It improves disturbance rejection during balance, and keeps equilibrium on slippery surfaces. The model of a gyroscope mounted onto an actuated gimbal was derived and simulated. To prove the concept worked, a custom-built platform showed it is possible for a balancing robot to stay upright with zero traction under the wheels.

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In this paper, the use of reinforcement learning (RL) in control systems is investigated using a rotatory inverted pendulum as an example. The control behavior of an RL controller is compared to that of traditional LQR and MPC controllers. This is done by evaluating their behavior under optimal conditions, their disturbance behavior, their robustness and their development process. All the investigated controllers are developed using MATLAB and the Simulink simulation environment and later deployed to a real pendulum model powered by a Raspberry Pi. The RL algorithm used is Proximal Policy Optimization (PPO). The LQR controller exhibits an easy development process, an average to good control behavior and average to good robustness. A linear MPC controller could show excellent results under optimal operating conditions. However, when subjected to disturbances or deviations from the equilibrium point, it showed poor performance and sometimes instable behavior. Employing a nonlinear MPC Controller in real time was not possible due to the high computational effort involved. The RL controller exhibits by far the most versatile and robust control behavior. When operated in the simulation environment, it achieved a high control accuracy. When employed in the real system, however, it only shows average accuracy and a significantly greater performance loss compared to the simulation than the traditional controllers. With MATLAB, it is not yet possible to directly post-train the RL controller on the Raspberry Pi, which is an obstacle to the practical application of RL in a prototyping or teaching setting. Nevertheless, RL in general proves to be a flexible and powerful control method, which is well suited for complex or nonlinear systems where traditional controllers struggle.

There is a need for smaller and more economic transportation systems. Two-wheeled inverted-pendulum machines, such as the Segway, have been proposed to address this need. However, the Segway places the operator on top of a naturally unstable platform that is stabilized by means of a control system. The control stability of the Segway can be severely affected when minor disturbances or unanticipated conditions arise. In this thesis, a dynamic model of a Segway is developed and used in simulations of various conditions that can arise during normal use. The dynamic model of a general two-wheeled inverted pendulum and human rider is presented. Initial estimates of the parameters were calculated or obtained from other references. The results from numerous experiments are presented and used to develop a better understanding of the dynamics of the vehicle. The experimental data was then used to adjust the model parameters to match the dynamics of a real Segway Human Transporter. Finally, the model was used to simulate various failure conditions. The simulations provide a better understanding of how these conditions arise, and help identify which parameters play an important role in their outcome.