Trajectory planning and tracking control for autonomous bicycle robot

Control of the autonomous bicycle robot offers considerable challenges to the field of robotics due to its nonholonomic, underactuated, and nonminimum-phase properties. Furthermore, instability and complex dynamic coupling make the trajectory planning of the bicycle robot even more challenging. In this paper, we consider both trajectory planning and tracking control of the autonomous bicycle robot. The desired motion trajectory of the contact point of the bicycle’s rear wheel is constructed using the parameterized polynomial curve that can connect two given endpoints with associated tangent angles. The parameters of the polynomial curve are determined by minimizing the maximum of the desired roll angle’s equilibrium of the bicycle, and this optimization problem is solved by the particle swarm optimization algorithm. Then, a control scheme that can achieve full-state trajectory tracking while maintaining the bicycle’s balance is proposed by combining a planar trajectory tracking controller with a roll angle balance controller. Simulation results are presented to demonstrate the effectiveness of the proposed method.     

Super-twisting algorithm with time delay estimation for uncertain robot manipulators

Nonlinear Dynamics - In a small tubular neighborhood of the heteroclinic orbits, we establish a local coordinate system by using the foundational solutions of the linear variational equation of the...     

Prescribed-time adaptive neural tracking control for a class of uncertain robotic manipulators with dead zone input

This paper solves the prescribed-time control problem for a class of robotic manipulators with system uncertainty and dead zone input. To make the system stable within a given convergence time T, a novel prescribed-time adaptive neural tracking controller is proposed by using the temporal scale transformation method and Lyapunov stability theory. Unlike the finite-time and the fixed-time stability where the convergence time depends on the controller parameters, the convergence time constant T is introduced into the proposed controller so that the closed-loop system will be stable within T. To cope with the system uncertainty, radial basis function neural networks (RBFNNs) are used and only need to update one parameter online. In addition, by choosing the same structure and parameters of RBFNNs, the proposed method can shorten the convergence time of the neural networks. Finally, simulation results are presented to demonstrate the effectiveness of the prescribed-time controller.

     

Adaptive fuzzy voltage-based backstepping tracking control for uncertain robotic manipulators subject to partial state constraints and input delay

Nonlinear Dynamics - The aim of this paper is to tackle the problem of adaptive fuzzy voltage-based tracking control for uncertain electrically driven robotic manipulators subject to input delay...     

Nonlinear robust and optimal control of robot manipulators

In this paper, we propose a new optimal control method for robust control of nonlinear robot manipulators. Many industrial robot systems are required to perform relatively large angular movement with sufficient accuracy. In real circumstances, highly nonlinear manipulator dynamics and uncertainties such as unknown load placed on the manipulator, external disturbance, and joint friction make the precise control of manipulators a very challenging task. The main contribution of this work is to develop a new robust control strategy to accomplish the precise control of robot manipulators under load uncertainty using a nonlinear optimal control formulation and solution. This methodology is based on the underlying relation between the robust stability and performance optimality. A class of robust control problems can be transformed to an equivalent optimal control problem by incorporating the uncertainty bounds into the cost functional. The θ-D optimal control approach is utilized to find an approximate closed-form feedback solution to the resultant nonlinear optimal control problem via a perturbation process. Numerical simulations show that the proposed robust controller is able to control the robot manipulator precisely under large load variations.     

Discrete adaptive fuzzy control for asymptotic tracking of robotic manipulators

This paper presents a novel discrete adaptive fuzzy controller for electrically driven robot manipulators. It addresses how to overcome the nonlinearity, uncertainties, discretizing error and approximation error of the fuzzy system for asymptotic tracking control of robotic manipulators. The proposed controller is model-free in the form of discrete Mamdani fuzzy controller. The parameters of fuzzy controller are adaptively tuned using an adaptive mechanism derived by stability analysis. A robust control term is used to compensate the approximation error of the fuzzy system for asymptotic tracking of a desired trajectory. The controller is robust against all uncertainties associated with the robot manipulator and actuators. It is easy to implement since it requires only the joint position feedback. Compared with fuzzy controllers which employ all states to guarantee stability, the proposed controller is very simpler. Stability analysis and simulation results show its efficiency in the tracking control.     

Robust tracking control method based on composite nonlinear feedback technique for linear systems with time-varying uncertain parameters and disturbances

In this paper, the composite nonlinear feedback control method is considered for robust tracking and model following of uncertain linear systems. The control law guarantees that the tracking error decreases asymptotically to zero in the presence of time varying uncertain parameters and disturbances. For performance improvement of the dynamical system, the proposed robust tracking controller consists of linear and nonlinear feedback parts without any switching element. The linear feedback law is designed to allow the closed loop system have a small damping ratio and a quick response while the nonlinear feedback law increases the damping ratio of the system as the system output approaches the output of the reference model. A new collection of different nonlinear functions used in the control law are offered to improve the reference tracking performance of the system. The proposed robust tracking controller improves the transient performance and steady state accuracy simultaneously. Finally, the simulations are provided to verify the theoretical results.     

Optimal robust voltage control of electrically driven robot manipulators

This paper develops a novel robust optimal voltage control of electrically driven robot manipulators. The whole robotic system including the robot manipulator and motors is considered in the control problem. Particle Swarm Optimization (PSO) is used to optimize the control design parameters, thus the performance of control system is highly improved. Beside this, we use Voltage Control Strategy (VCS) which is more robust, faster, less coupled, and less computational compared with the common strategy called as Torque Control Strategy (TCS). To state these advantages, it is reasoning that the TCS is dependent on the manipulator dynamics whereas the VCS can be free from it. The robust optimal voltage control is verified by convergence analysis. A comparative study between the VCS and the TCS confirms superiority of the VCS to the TCS. Simulation results present effectiveness of the proposed methods applied on a spherical robot manipulator driven by permanent magnet dc motors.     

Trajectory tracking control for tractor-trailer vehicles: a coordinated control approach

Nonlinear Dynamics - This article presents a coordinated control approach for a tractor-trailer vehicle such that a satisfactory trajectory tracking performance can be achieved, simultaneously...     

Adaptive synchronization for uncertain complex dynamical network using fuzzy disturbance observer

This paper proposes a robust adaptive controller design method for synchronization of a complex dynamical network with uncertainty and disturbance. A fuzzy disturbance observer is used to estimate the overall disturbances without any prior knowledge about them. The proposed control method globally asymptotically synchronizes the network using adaptation laws obtained by using Lyapunov stability theory. The proposed method is applied to two chaotic systems and the results show the effectiveness of the approach.     

Distributed adaptive tracking control for multi-agent systems with uncertain dynamics

Nonlinear Dynamics - In this paper, we investigate a general integrable three-component coupled nonlocal nonlinear Schrödinger system with the parity-time symmetry. The general Nth Darboux...     

Approximate continuous fixed-time terminal sliding mode control with prescribed performance for uncertain robotic manipulators

This paper studies an approximate continuous fixed-time terminal sliding mode control (CFTSMC) with prescribed performance for uncertain robotic manipulators. A transformation concerning tracking error using a fixed-time prescribed performance function is proposed to guarantee the transient and steady-state performance of trajectory tracking control for uncertain robotic manipulators within fixed time. Utilizing the transformed error, a smooth fixed-time sliding mode surface is designed. Then, based on the proposed sliding mode surface, an approximate CFTSMC scheme is presented to achieve inherent chattering-free control for uncertain robotic manipulators. According to the Lyapunov stability theory, it is proved that the position tracking error can be bounded in the prescribed performance boundaries and globally converges to a defined small region within fixed time and then approaches exponentially to the origin. Several numerical simulation results demonstrate the effectiveness and superiority of the proposed control strategy for uncertain robotic manipulators.

     

Experimental validation of internal model approach for tracking control of a MEMS micromirror without angular velocity measurement

Nonlinear Dynamics - This paper addresses the angular tracking control for an electromagnetic MEMS micromirror. The problem is formulated in the output regulation framework for output feedback...     

Trajectory tracking of a class of under-actuated thrust-propelled vehicle with uncertainties and unknown disturbances

This paper deals with the problem of designing a controller for a thrust-propelled vehicle which steers the vehicle to track a 3D spatial path, while effective compensation for both time-varying disturbances and uncertainties is achieved as well. Taking advantage of extraction algorithm, we separate the design for the translational and rotational dynamics. A back-stepping-based controller and a sliding mode controller are, respectively, designed for the translational and rotational dynamics in succession. The stability of the control framework is established through Lyapunov analysis. A numerical simulation is also included in the paper to render the effectiveness of the proposed control scheme.     

Observer-based event-triggered tracking control for large-scale high order nonlinear uncertain systems

Nonlinear Dynamics - The event-triggered tracking control for large-scale high order nonlinear uncertain systems, whose state information is immeasurable, is investigated via an observer-based...     

A new algorithm for workspace analysis for robot manipulators

Summary A new algorithm to determine the workspace of a robot manipulator is presented in this paper. The method is based on the concept of dividing the robot structure into a regional structure and an orientational structure and is much more efficient than many methods currently available. Another advantage is that the method is expedient to plot the contours of the workspace of a robot manipulator on an arbitrarily defined plane or determine the extreme points along a general direction. The problem of determining subspaces of different joint configurations is also addressed and a method to compute them is proposed. Since for an ordinary industrial robot the regional structure is usually solvable to the first three joint variables, the method proposed is efficient computationally. The applicability of the method is demonstrated by some numeric examples.

---

Sommario Nel lavoro viene presentato un nuovo algoritmo per determinare il campo di lavoro di un manipolatore robotico. Il metodo utilizza il concetto di dividere la struttura del manipolatore ad una struttura regionale ed una struttura orientamentale ed è molto più efficace di molti metodi esistenti. Un altro vantaggio del metodo è che esso è conveniente per disegnare i contorni del campo di lavoro su un piano specificato arbitrariamente oppure per determinare i punti estremi lungo una direzione generale. Nel lavoro è studiato anche il problema di determinare i sottospazi di configurazioni diverse dei giunti e viene presentato un metodo per calcolarli. Siccome per un robot industriale la struttura regionale è normalmente risolubile per le prime tre variabili dei giunti, il metodo presentato è molto efficace computazionalmente. L'applicabilità del metodo è dimostrata da qualche esempio numerico.