Intelligent controllers for velocity tracking of two wheeled inverted pendulum mobile robot

Velocity tracking is one of the important objectives of vehicle, machines and mobile robots. A two wheeled inverted pendulum (TWIP) is a class of mobile robot that is open loop unstable with high nonlinearities which makes it difficult to control its velocity because of its nature of pitch falling if left unattended. In this work, three soft computing techniques were proposed to track a desired velocity of the TWIP. Fuzzy Logic Control (FLC), Neural Network Inverse Model control (NN) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) were designed and simulated on the TWIP model. All the three controllers have shown practically good performance in tracking the desired speed and keeping the robot in upright position and ANFIS has shown slightly better performance than FLC, while NN consumes more energy

Forward and backward motion control of wheelchair on two wheels

The challenge in designing wheelchair on two wheels involves the design and implementation of suitable control strategies for a two wheeled wheelchair to perform comparably similar to a normal four wheeled wheelchair. It is important to note that a wheelchair on two wheels is expected not to take much space during mobility as compared to when it is on four wheels. Moreover, disabled people are encouraged and expected to perform most activities that others can do and hence lead an independent life. Thus, wheelchairs on two wheels are needed for disabled persons to perform some of the essential tasks in their living and work environments. In this research a model of the standard wheelchair is developed as a test and verification platform using Visual Nastran software. Novel fuzzy logic control strategies are designed for lifting up the chair transforming a four-wheeled wheelchair to a two-wheeled wheelchair) and maintaining stability and balance while on two wheels. Furthermore, position control for forward and backward mobility of the wheelchair on two wheels is developed using fuzzy logic control. Simulation results of the proposed control strategy are presented and discussed

Discrete-time neural network based state observer with neural network based control formulation for a class of systems with unmatched uncertainties

An observer is a dynamic system that estimates the state variables of another system using noisy measurements, either to estimate unmeasurable states, or to improve the accuracy of the state measurements. The Modified State Observer (MSO) is a technique that uses a standard observer structure modified to include a neural network to estimate system states as well as system uncertainty. It has been used in orbit uncertainty estimation and atmospheric reentry uncertainty estimation problems to correctly estimate unmodeled system dynamics. A form of the MSO has been used to control a nonlinear electrohydraulic system with parameter uncertainty using a simplified linear model. In this paper an extension of the MSO into discrete-time is developed using Lyapunov stability theory. Discrete-time systems are found in all digital hardware implementations, such as that found in a Martian rover, a quadcopter UAV, or digital flight control systems, and have the added benefit of reduced computation time compared to continuous systems. The derived adaptive update law guarantees stability of the error dynamics and boundedness of the neural network weights. To prove the validity of the discrete-time MSO (DMSO) simulation studies are performed using a two wheeled inverted pendulum (TWIP) robot, an unstable nonlinear system with unmatched uncertainties. Using a linear model with parameter uncertainties, the DMSO is shown to correctly estimate the state of the system as well as the system uncertainty, providing state estimates orders of magnitude more accurate, and in periods of time up to 10 times faster than the Discrete Kalman Filter. The DMSO is implemented on an actual TWIP robot to further validate the performance and demonstrate the applicability to discrete-time systems found in many aerospace applications. Additionally, a new form of neural network control is developed to compensate for the unmatched uncertainties that exist in the TWIP system using a state variable as a virtual control input. It is shown that in all cases the neural network based control assists with the controller effectiveness, resulting in the most effective controller, performing on average 53.1% better than LQR control alone --Abstract, page iii

Modified Particle Swarm Optimization Based PID for Movement Control of Two-Wheeled Balancing Robot

Two-wheeled balancing robot is a mobile robot that has helped various human’s jobs such as the transportations. To control stability is still be the challenges for researchers. Three equations are obtained by analyzing the dynamics of the robot with the Newton approach. To control three degrees of freedom (DOF) of the robot, PIDs is tuned automatically and optimized by multivariable Modified Particle Swarm Optimization (MPSO). Some parameters of the PSO process are modified to be a nonlinear function. The inertia weight and learning factor variable on PSO are modified to decreasing exponentially and increasing exponentially, respectively. The Integral Absolute Error (IAE) and Integral Square Error (ISE) evaluate the error values. The performances of MPSO and PSO classic are tested by several Benchmark functions. The results of the Benchmark Function show that Modified PSO proposed to produce less error and overshoot. Therefore, the MPSO purposed are implemented to the plant of balancing robot to control the angle, the position, and the heading of the robot. The result of the simulation built shows that the MPSO – PID can make the robot moves to the desired positions and maintain the stability of the angle of the robot. The input of distance and angle of the robot are coupling so MPSO needs six variable to optimize the PID parameters of balancing and distance control