Dynamics and Motion Planning of Trident Snake Robot

The trident snake robot is a mechanical device that serves as a demanding testbed for motion planning and control algorithms of constrained non-holonomic systems. This paper provides the equations of motion and addresses the motion planning problem of the trident snake with dynamics, equipped with either active joints (undulatory locomotion) or active wheels (wheeled locomotion). Thanks to a partial feedback linearization of the dynamics model, the motion planning problem basically reduces to a constrained kinematic motion planning. Two kinds of constraints have been taken into account, ensuring the regularity of the feedback and the collision avoidance between the robot’s arms and body. Following the guidelines of the endogenous configuration space approach, two Jacobian motion planning algorithms have been designed: the singularity robust Jacobian algorithm and the imbalanced Jacobian algorithm. Performance of these algorithms have been illustrated by computer simulations.     

A spline-theoretic approach to minimum-energy control

The problem of minimum-energy control of linear systems with linear functional constraints on the output is considered from a spline-theoretic viewpoint. It is shown that constrained minimum-energy control and spline interpolation are dual projection problems: if the given system is driven by the minimum-energy control, the system output will be the spline that interpolates the output constraints. This duality result is used to obtain a simple formula for the exact optimal control under very general constraints.     

State Space Constrained Iterative Learning Control for Robotic Manipulators

Real‐life work operations of industrial robotic manipulators are performed within a constrained state space. Such operations most often require accurate planning and tracking a desired trajectory, where all the characteristics of the dynamic model are taken into consideration. This paper presents a general method and an efficient computational procedure for path planning with respect to state space constraints. Given a dynamic model of a robotic manipulator, the proposed solution takes into consideration the influence of all imprecisely measured model parameters, making use of iterative learning control (ILC). A major advantage of this solution is that it resolves the well‐known problem of interrupting the learning procedure due to a high transient tracking error or when the desired trajectory is planned closely to the state space boundaries. The numerical procedure elaborated here computes the robot arm motion to accurately track a desired trajectory in a constrained state space taking into consideration all the dynamic characteristics that influence the motion. Simulation results with a typical industrial robot arm demonstrate the robustness of the numerical procedure. In particular, the results extend the applicability of ILC in robot motion control and provide a means for improving the overall trajectory tracking performance of most robotic systems.     

Modeling and control of two constrained manipulators

The coordinated control of two manipulators in the presence of environment constraints is studied in this paper. Such a control method is needed in applications in which the two manipulators grasp a common object whose motion is constrained by environments. The two manipulators are not only constrained with each other, but also constrained by the environment in their workspace. It is realized that the motion and constraint equations obtained directly from mechanics are not suitable for the control purpose. A set of equivalent equations are derived, which are in the standard form of the nonlinear system representation with clear state equations and output equations. A nonlinear feedback is found which exactly linearizes and decouples the dynamic nonlinear system of the two constrained manipulators. The coordinated controller design is then carried out based on the linearized system by using linear system theory.     

Fast reference governors for second-order linear systems with constraints and an input time-delay

This note provides a solution to the constrained command tracking problem using reference governors for a class of continuous-time second order linear systems with an input delay and with pointwise-in-time state and control constraints. The reference governor modifies the command to a closed-loop system based on the prediction of whether the system response to constant commands violates the specified constraints. The solution relies on classical control results for second order linear systems and requires only checking whether predicted outputs violate the constraints at a small number of time instants (e.g., four time instants in the single output case). This greatly simplifies the online computation, especially when a reference governor is applied to system models that are (slowly) changing in time. The effectiveness of the proposed method is demonstrated by a numerical example.     

Contouring Control for Multi‐Axis Motion Systems With Holonomic Constraints: Theory and Application to a Parallel System

In this paper, the contouring control problem for the constrained multi‐axis motion system is studied. The method of equivalent errors, previously proposed for unconstrained motion systems, is generalized to the system with holonomic constraints. It is shown that the method can be applied to the constrained system provided that the constraints satisfy a proper condition. Because of the constraints, the states in the control law are not completely independent. The unavailable states can be estimated using linear approximation from the constraint equations. As an illustrative example, the proposed method is applied to a parallel motion system with complicated dynamics. A contouring controller is designed using the method of equivalent errors incorporated with integral sliding mode control. Simulation results for contouring circular, elliptic, and square paths verify the effectiveness of the proposed method.     

Robust output feedback model predictive control of constrained linear systems

This paper provides a solution to the problem of robust output feedback model predictive control of constrained, linear, discrete-time systems in the presence of bounded state and output disturbances. The proposed output feedback controller consists of a simple, stable Luenberger state estimator and a recently developed, robustly stabilizing, tube-based, model predictive controller. The state estimation error is bounded by an invariant set. The tube-based controller ensures that all possible realizations of the state trajectory lie in a simple uncertainty tube the ‘center’ of which is the solution of a nominal (disturbance-free) system and the ‘cross-section’ of which is also invariant. Satisfaction of the state and input constraints for the original system is guaranteed by employing tighter constraint sets for the nominal system. The complexity of the resultant controller is similar to that required for nominal model predictive control.     

Improving the Interoperability in the Digital Home Through the Automatic Generation of Software Adapters from a SysML Model

This paper addresses the motion planning problem of nonholonomic robotic systems. The system’s kinematics are described by a driftless control system with output. It is assumed that the control functions are represented in a parametric form, as truncated orthogonal series. A new motion planning algorithm is proposed based on the solution of a Lagrange-type optimisation problem stated in the linear approximation of the parametrised system. Performance of the algorithm is illustrated by numeric computations for a motion planning problem of the rolling ball.     

State-space realizations for output feedback control of linear differential-algebraic-equation systems

This work addresses the derivation of state-space realizations for the output feedback control of linear, high-index differential-algebraic-equation systems that are not controllable at infinity and for which the control inputs appear explicitly in the underlying algebraic constraints. The constrained state space of such systems depends on the control inputs, and thus, a state-space realization cannot be derived independently of the controller design. Motivated by this, initially a dynamic output feedback compensator is designed that yields a modified system for which the algebraic constraints are independent of the new control inputs. For this feedback modified system, a state-space realization is then derived which can be used for output feedback controller synthesis.     

Output feedback stabilization for uncertain systems: constrainedRiccati approach

In this paper, the problem of output feedback stabilization of linear systems with mismatched uncertainties is investigated. A new approach to the design of output feedback controller is proposed, and the respective output feedback gains are obtained through the solution of a Riccati equation with linear constraints. Solvability conditions for such a constrained Riccati problem are derived, and a systematic algorithm for obtaining an output feedback controller, which guarantees the system state is globally exponentially stable, is provided     

Necessary conditions for a class of optimal multiprocess with state constraints

In this article, we derive a maximum principle for a special class of free end time optimal control of multiprocesses involving a family of control systems acting in different regions defined by state constraints. We are mainly interested in problems with contiguous time intervals. The main feature of our maximum principle is that it covers the case where some of the regions considered may not be visited. This means that the intervals where the corresponding control systems are active may be reduced to a single point. The derivation of our maximum principle is done by reformulating the optimal multiprocess problem as an equivalent fixed time state constrained optimal control problem. This reformulated problem is also of interest since it provides the means to solve optimal multiprocess problems numerically via the direct method. We illustrate our findings with an example concerning the path planning of an autonomous underwater vehicle (AUV) using a simple kinematic model derived for simulations. We use simplified point‐mass model for the motion of an AUV in a horizontal plane and we assume that the ocean currents are known. We recast this problem as the multiprocess optimal control problem of interest and we study it via simulations presenting computational results partially validated by the maximum principle.     

A Systematic Approach for Designing Analytical Dynamics and Servo Control of Constrained Mechanical Systems

A systematic approach for designing analytical dynamics and servo control of constrained mechanical systems is proposed. Fundamental equation of constrained mechanical systems is first obtained according to Udwadia-Kalaba approach which is applicable to holonomic and nonholonomic constrained systems no matter whether they satisfy the D'Alember's principle. The performance specifications are modeled as servo constraints. Constraint-following servo control is used to realize the servo constraints. For this inverse dynamics control problem, the determination of control inputs is based on the Moore-Penrose generalized inverse to complete the specified motion. Secondorder constraints are used in the dynamics and servo control. Constraint violation suppression methods can be adopted to eliminate constraint drift in the numerical simulation. Furthermore, this proposed approach is applicable to not only fully actuated but also underactuated and redundantly actuated mechanical systems. Two-mass spring system and coordinated robot system are presented as examples for illustration.      

Hybrid threshold strategy‐based adaptive tracking control for nonlinear stochastic systems with full‐state constraints

This article focuses on the adaptive tracking control problem for a class of interconnected nonlinear stochastic systems under full‐state constraints based on the hybrid threshold strategy. Different from the existing works, we propose a novel pre‐constrained tracking control algorithm to deal with the full‐state constraint problem. First, a novel nonlinear transformation function and a new coordinate transformation are developed to constrain state variables, which can directly cope with asymmetric state constraints. Second, the hybrid threshold strategy is constructed to provide a reasonable way in balancing system performance and communication constraints. By the use of dynamic surface control technique and neural network approximate technique, a smooth pre‐constrained tracking controller with adaptive laws is designed for the interconnected nonlinear stochastic systems. Moreover, based on the Lyapunov stability theory, it is proved that all state variables are successfully pre‐constrained within asymmetric boundaries. Finally, a simulation example is presented to verify the effectiveness of proposed control algorithm.     

A GGP approach to solve non convex min-max predictive controller for a class of constrained MIMO systems described by state-space models

This paper proposes a new method to solve non convex min-max predictive controller for a class of constrained linear Multi Input Multi Output (MIMO) systems. A parametric uncertainty state space model is adopted to describe the dynamic behavior of the real process. Moreover, the output deviation method is used to design the j-step ahead output predictor. The control law is obtained by the resolution of a non convex min-max optimization problem under input constraints. The key idea is to transform the initial non convex optimization problem to a convex one by means of variable transformations. To this end, the Generalized Geometric Programming (GGP) which is a global deterministic optimization method is used. An efficient implementation of this approach will lead to an algorithm with a low computational burden. Simulation results performed on Multi Input Multi Output (MIMO) system show successful set point tracking, constraints satisfaction and good non-zero disturbance rejection.     

Optimal Control of Underactuated Nonholonomic Mechanical Systems

In this paper, we use an affine connection formulation to study an optimal control problem for a class of nonholonomic, underactuated mechanical systems. In particular, we aim to minimize the norm-squared of the control input to move the system from an initial to a terminal state. We consider systems evolving on general manifolds. The class of nonholonomic systems we study in this paper includes, in particular, wheeled-type vehicles, which are important for many robotic locomotion systems. The two special aspects of this optimal control problem are the nonholonomic constraints and underactuation. Nonholonomic constraints restrict the evolution of the system to a distribution on the manifold. The nonholonomic connection is used to express the constrained equations of motion. Many robotic systems are underactuated since control inputs are usually applied through the robot's internal configuration space only. While we do not consider symmetries with respect to group actions in this paper, the fact that the system is underactuated is taken into account in our problem formulation. This allows one to compute reaction forces due to any inputs applied in directions orthogonal to the constraint distribution. We illustrate our ideas by considering a simple example on a three-dimensional manifold, including obstacle avoidance using the method of navigation functions.     

Regulator problem for linear systems with constraints on control and its increment or rate

This paper discusses the problem of constraints on both control and its rate or increment, for linear systems in state space form, in both the continuous and discrete-time domains. Necessary and sufficient conditions are derived for autonomous linear systems with constrained state increment or rate (for the continuous-time case), such that the system evolves respecting incremental or rate constraints. A pole assignment technique is then used to solve the inverse problem, giving stabilizing state feedback controllers that respect non-symmetrical constraints on both control and its increment or rate. An illustrative example shows the application of the method on the double integrator problem.     

On the dynamics and Lyapunov stability of constrained and embedded rigid bodies

Lyapunov stability of constrained and embedded rigid bodies is considered. The constraints are of the equality type where the desired motion is to take place on an a priori defined submanifold of movement. Special and augmented state spaces for the representation of systems of rigid bodies are presented. A systematic method of stabilizing these augmented systems and a procedure for constructing Lyapunov functions are presented. The representation is applicable to augmented as well as reduced state spaces of the system defined by the constraints. The augmented state space results from the embedding of the free rigid body system in the larger state space of free rigid body and position control states, and in which the Lyapunov function is constructed. The reduced state space results when the system is restricted and is reduced to lie on the submanifold of movement. It is shown that, for the class of rigid bodies and the physical constraints considered, the projected feedback structures, and the reduced Lyapunov function constitute appropriate stabilizing structures for the constrained system. It is shown that the method applies equally to holonomically constrained and visco-elastically coupled rigid bodies. Digital computer simulations of one single rigid body system are presented to demonstrate the feasibility and effectiveness of the method. Applications to natural systems and the role of cartilage, ligaments and muscles in maintaining the integrity and stability of the joints are noted.