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

Adaptive Sliding Mode Control of Mobile Manipulators with Markovian Switching Joints

The hybrid joints of manipulators can be switched to either active (actuated) or passive (underactuated) mode as needed. Consider the property of hybrid joints, the system switches stochastically between active and passive systems, and the dynamics of the jump system cannot stay on each trajectory errors region of subsystems forever; therefore, it is difficult to determine whether the closed-loop system is stochastically stable. In this paper, we consider stochastic stability and sliding mode control for mobile manipulators using stochastic jumps switching joints. Adaptive parameter techniques are adopted to cope with the effect of Markovian switching and nonlinear dynamics uncertainty and follow the desired trajectory for wheeled mobile manipulators. The resulting closed-loop system is bounded in probability and the effect due to the external disturbance on the tracking errors can be attenuated to any preassigned level. It has been shown that the adaptive control problem for the Markovian jump nonlinear systems is solvable if a set of coupled linear matrix inequalities (LMIs) have solutions. Finally, a numerical example is given to show the potential of the proposed techniques

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

Stability analysis of non-holonomic inverted pendulum system

The inverted pendulum is doubtlessly one of the most famous control problems found in most control text books and laboratories worldwide. This popularity comes from the fact that the inverted pendulum exhibits nonlinear, unstable and non-minimum phase dynamics. The basic control objective of the study is to design a controller in order to maintain the upright position of the pendulum while also controlling the position of the cart. In our study we explored the relationship that the tuning parameters (weight on the position of the car and the angle that the pendulum makes with the vertical) of a classical inverted pendulum on a cart has on the pole placement and hence on the stability of the system. We then present a family of curves showing the local root-locus and develop relationships between the weight changes and the system performance. We describe how these locus trends provide insight that is useful to the control designer during the effort to optimize the system performance. Finally, we use our general results to design an effective feedback controller for a new system with a longer pendulum, and present experiment results that demonstrate the effectiveness of our analysis. We then designed a simulation-based study to determine the stability characteristics of a holonomic inverted pendulum system. Here we decoupled the system using geometry as two independent one dimensional inverted pendulum and observed that the system can be stabilized using this method successfully with and without noise added to the system. Next, we designed a linear system for the highly complex inverted pendulum on a non-holonomic cart system. Overall, the findings will provide valuable input to the controller designers for a wide range of applications including tuning of the controller parameters to design of a linear controller for nonlinear systems

Adaptive Sliding-Mode Tracking Control for a Class of Nonholonomic Mechanical Systems

This paper investigates the problem of finite-time tracking control for nonholonomic mechanical systems with affine constraints. The control scheme is provided by flexibly incorporating terminal sliding-mode control with the method of relay switching control and related adaptive technique. The proposed relay switching controller ensures that the output tracking error converges to zero in a finite time. As an application, a boat on a running river is given to show the effectiveness of the control scheme

Modelling and simulation of double-link scenario in a two-wheeled wheelchair

Wheelchairs on two wheels are essential part of life for disabled persons. But designing control strategies for these wheelchairs is a challenging task due to the fact that they are highly nonlinear and unstable systems. The subtle design of the system mimics the inverted pendulum with a double-link scenario. This forms an example of multi degree of freedom system where there are three actuators, one on each wheel, and one for position between the two links. The system starts to work with lifting the front wheels (casters) to the upright position and further on stabilizing in the upright position. The challenge resides in the design, modelling and control of the two-wheeled wheelchair to perform comparably similar to normal four-wheeled wheelchair. This paper is aimed to model the highly nonlinear and complex two-wheeled wheelchair system using two different approaches. A state-space model is obtained from the linearised mathematical model as an initial attempt for control design investigation. Then a complex visualized mathematical model is developed, which proves as a good technique for prediction and simulation of the two-wheeled wheelchair