In this paper, an independent joint position and stiffness adaptive control for a robot arm actuated by McKibbenartificial muscles is reported. In particular, {\em muscular} nd {\em dynamic} parameters of the system are supposed unknown. Adaptive control performance is tested in a one degree of freedom experimental setup and compared with PID control performance. The adaptive control scheme is then applied to a robot arm that is conceived to perform tasks in an anthropic environment. The adaptive control developed is such that performance of the robot arm is very similar to this of human arm. Experimental results are reported.
In this paper, the problem of the stabilization of a discrete-time linear system subject to a fixed and uniformly quantized control set is considered. It is well known that, working with quantized inputs, the states of the system (except for a negligible set of initial conditions) cannot reach asymptotically the equilibrium point. Our aim is then to find an invariant and attractive neighborhood of the equilibrium and provide with a controller which steers the system into it. We construct a continuous and increasing family of invariant sets including one which is, in a specific sense, minimal. The invariance and attractivity properties of such sets are revised in the finite control set case: we propose a family of controllers taking on a finite number of values and ensuring the system convergence to the minimal invariant set. Some consequences of our technique are underlined with particular regard to the usage of Model Predictive Control tools. In the last section an example which shows the effectiveness of our results is presented.
In this paper we consider the problem of optimal control (specifically, minimum-time steering) for systems with quantized inputs. In particular, we propose a new approach to the solution of the optimal control problem for an important class of nonlinear systems, i.e. chained-form systems. By exploiting results on the structure of the reachability set of these systems under quantized control, the optimal solution is determined solving an integer linear programming problem. Our algorithm represents an improvement with respect to classical approaches in terms of exactness, as it does not resort to any a priori state-space discretization. Although the computational complexity of the problem in our formulation is still formally exponential, it lends itself to application of Branch and Bound techniques, which substantially cuts down computations in many cases, as it has been experimentally observed.
The problem of stabilization to the trivial equilibrium of a system with communication constraints is addressed. The communication constraints are related to the fact that commands are issued to different groups of actuators through a shared resource. We tackle the problem by using a Model Predictive Control scheme, which,at every step, decides the allocation of the bus {\em and} he control command values. After discussing two different alternatives for dealing with the scheduling constraints, we develop a formulation based on the generalized linear complementarity problem, which enables the application of efficient numerical solutions. Finally, we give some preliminary result on the parametric dependence of the problem's solution from the system's state.
In this paper we consider policies for free-flight management of air traffic. We consider instantaneous and bounded heading angle deviation as conflict avoidance maneuvers. The corresponding model, resulting in a Mixed Integer Linear Programming (MILP) problem allow to solve both conflict detection and conflict resolution problems. The developed algorithm proved successful in a centralized implementation with a large number of cooperating aircraft. However, the application of such algorithm to a Free Flight environment, where cooperation can only be expected from neighboring aircraft, poses many challenges. We consider a model of the decentralized conflict resolution strategy that is based on a hybrid system, and sufficient conditions under which a 3-aircraft Free Flight MILP-based scheme guarantees safety of flight are provided.
In this paper we consider the reachability problem for quantized control systems, i.e. systems that take inputs from a finite set of symbols. Previous work addressed this problem for linear systems and for some specific classes of nonlinear driftless systems. In this paper we attack the study of more general nonlinear systems. To do so we find it useful to pose the problem in more abstract terms, and make use of the wealth of tools available in group theory, which enables us to proceed in our agenda of better understanding effects of quantization of inputs on dynamic systems.
In this paper, we consider planning motions of objects of regular shape rolling on a plane among obstacles. Theoretical foundations and applications of this type of operations in robotic manipulation and locomotion have been discussed elsewhere. In this paper, we propose a novel algorithm that improves upon existing techniques in that: i) it is finitely computable and predictable (an upper bound on the computations necessary to reach a given goal within a tolerance can be given), and ii) it possesses a topological (local-local) property which enables obstacles and workspace limitations to be dealt with in an effective way.
In this paper we present an innovative application of magnetorheological (MR) fluids to haptic interfaces. These materials consist of a suspension of a micron-sized, magnetizable particles in a synthetic oil. Exposure to an external magnetic field induces in the fluid a change in rheological behaviour turning it into a near-solid in few milliseconds. Just as quickly, the fluid can be returned to its liquid state by the removal of the field. MR fluids are already present on the market, used in devices such as valves, brakes, clutches, and dampers. In this paper we investigate the possibility of using MR fluids to mimic the compliance, damping, creep (in other terms, the rheology) of materials in order to realize a haptic display and we propose two different implementations. Here we only outline the first scheme, whose experimental results have been reported in our previous work, and will describe the second one. In this latter scheme we set up a psychophysical protocol where a group of volunteers were asked to interact with the MR fluid duly excited and qualitative results are discussed.
In this paper we consider the problem of optimal control (specifically, minimum-time steering) for systems with quantized inputs. In particular, we propose a new approach to the solution of the optimal control problem for an important class of nonlinear systems, i.e. chained-form systems. By exploiting results on the structure of the reachability set of these systems under quantized control, the optimal solution is determined solving an integer linear programming problem. Our algorithm represents an improvement with respect to classical approaches in terms of exactness, as it does not resort to any a priori state-space discretization. Although the computational complexity of the problem in our formulation is still formally exponential, it lends itself to application of Branch and Bound techniques, which substantially cuts down computations in many cases, as it has been experimentally observed.
In this paper the problem of stabilizing a wheeled vehicle of unicycle type to a set point, using only visual feedback, is considered. The practically most relevant problem of keeping the tracked features in sight of the camera while maneuvering to park the vehicle is taken into account. This constraint, often neglected in the literature, combines with the nonholonomic nature of the vehicle kinematics in a challenging controller design problem. We provide an effective solution to such problem by using a combination of previous results on non-smooth control synthesis, and recently developed hybrid control techniques. Simulations and experimental results on a laboratory vehicle are reported, showing the practicality of the proposed approach.
This paper considers the problem of solving conflicts arising among several aircraft that are assumed to move in a shared airspace. Aircraft can not get closer to each other than a given safety distance in order to avoid possible conflicts between different airplanes. For such system of multiple aircraft, we consider the path planning problem among given waypoints avoiding all possible conflicts. In particular we are interested in optimal paths, i.e. we want to minimize the total flight time. We propose two different formulations of the multi-aircraft conflict avoidance problem as a mixed-integer linear program: in the first case only velocity changes are admissible maneuvers, in the second one only heading angle changes are allowed. Due to the linear formulation of the two problems, solutions may be obtained quickly with standard optimization software, allowing our approach to be implemented in real time.
In this paper we study control systems whose input sets are quantized, i.e. finite or regularly distributed on a mesh. We specifically focus on problems relating to the structure of the reachable set of such systems, which may turn out to be either dense or discrete. We report results on the reachable set of linear quantized systems, and on a particular but interesting class of nonlinear systems, i.e. nonholonomic chained-form systems. For such systems, we provide a complete characterization of the reachable set, and, in the case the set is discrete, a computable method to completely and succinctly describe its structure. Implications and open problems in the analysis and synthesis of quantized control systems are addressed.
