A novel method for optimal motion planning in the context of a class of dynamical system is proposed in this work. Our approach is based on the design of a provably safe and convergent actor structure, which is optimized via a policy iteration method. The proposed actor has wide applications, from control of mechanical systems to providing acceleration commands for more complex robotic platforms. Extra care is taken to provide theoretical guarantees, and the scheme is validated against an existing sampling-based planner.

In this work, a tube-based nearly optimal solution to motion planning in unknown workspaces is presented. The advantages of reactive motion planning are combined with a Policy Iteration Reinforcement Learning scheme to yield a novel solution for unknown workspaces that inherits provable safety, convergence and optimality. Moreover, in simply-connected workspaces, our method is proven to asymptotically provide the globally optimal path. Our method is compared against a provably asymptotically optimal RRT⋆method, as well as a relevant reactive method and provides satisfactory performance, closely matching or outperforming the former.

This work proposes a novel and provably correct method for three-dimensional optimal motion planning in complex environments. Our approach models the 3D motion planning problem by solving streamlines of the potential fluid flow, filling a gap in traditional motion planning techniques by guaranteeing a closed-loop, smooth and natural-looking navigation solution. Special emphasis is given to an inherent challenge of artificial potential field (APF) methods, namely establishing proofs of safety and stability over the entire optimization process. A model-based actor-critic reinforcement learning algorithm is introduced to approximate the optimal solution to the Hamilton-Jacobi-Bellman equation and update the controller parameters in a deterministic manner. Through a series of ROS-Gazebo software-in-the-loop simulations the proposed methodology demonstrates robustness and outperforms widely used methods such as the RRT∗, highlighting its contribution to the field of 3D optimal motion planning.

In this work, we propose an approximation-free adaptive performance control scheme for unknown high-relative degree, multi-input-multioutput (MIMO) nonlinear systems with saturation on the control input signal. We introduce a novel reconciling adaptive modification of the predefined performance specifications based on the input constraints of the controlled plant, providing the best-feasible output performance. The automatic gain tuning in combination with the simplicity of the proposed controller enhance its robustness and enable its easy deployment in practical scenarios. Notably, the introduced control methodology ensures the necessary compromise between input-output constraints on a semi-global sense for ISS systems. However, the stability attributes for general nonlinear systems are inevitably limited to compact domains due to the inherent conflict between performance demand and actuation capability. In this context, we provide a sufficient closed-loop stability criterion through Lyapunov analysis. Finally, illustrative simulation studies and experimental results clarify and verify the efficacy of the proposed controller.

In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in multiply connected manifolds, and is necessary for formulating continuous solutions. The current method generalizes and improves upon previous results, where parametrized controllers hindered the method in scope and results. Through enhancing the traditional reactive paradigm with RL, the proposed scheme is demonstrated to outperform both previous reactive methods as well as an RRT* method in path length, cost function values and execution times, indicating almost global optimality.

In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to minimize a cost function. The main novelty of the proposed scheme lies in the method that provides optimal policies for multiple final positions, in contrast to most existing methodologies that consider a single final configuration. An assessment of the optimality of our results is conducted by comparing our novel motion planning scheme against a RRT* method.

The connectivity of distributed networked multi-robot systems is a crucial operational specification, since the involved robots interact/communicate locally only with their immediate neighbors. Thus, in this work, we propose a distributed algorithm to estimate the algebraic connectivity of the underlying communication graph, which stands as a valid connectivity metric. Our method establishes robustness and fast convergence properties that can be adjusted independently via the appropriate selection of certain design parameters. Finally, we confirm the theoretical findings through simulated paradigms and verify the superiority of our method against a well-established solution of the related literature.

In this work, a novel solution to the optimal motion planning problem is proposed, through a continuous, deterministic and provably correct approach, with guaranteed safety and which is based on a parametrized Artificial Potential Field (APF). In particular, Reinforcement Learning (RL) is applied to adjust appropriately the parameters of the underlying potential field towards minimizing the Hamilton-Jacobi-Bellman (HJB) error. The proposed method, outperforms consistently a Rapidly-exploring Random Trees (RRT*) method and consists a fertile advancement in the optimal motion planning problem.

This work addresses the problem of robot navigation under timed temporal specifications in workspaces cluttered with obstacles. We propose a hybrid control strategy that guarantees the accomplishment of a high-level specification expressed as a timed temporal logic formula, while preserving safety (i.e., obstacle avoidance) of the system. In particular, we utilize a motion controller that achieves safe navigation inside the workspace in predetermined time, thus allowing us to abstract the motion of the agent as a finite timed transition system among certain regions of interest. Next, we employ standard formal verification and convex optimization techniques to derive high-level timed plans that satisfy the agent's specifications. A simulation study illustrates and clarifies the proposed scheme.

In this work, a hybrid control scheme is presented in order to address the navigation problem for a planar robotic platform of arbitrary shape that is moving inside an obstacle cluttered workspace. Given an initial and desired robot configuration, we propose a methodology based on approximate configuration space decomposition techniques that makes use of heuristics to adaptively refine a partition of the configuration space into non-overlapping, adjacent slices. Furthermore, we employ appropriate workspace transformations and adaptive potential field based control laws that integrate elegantly with the type of configuration space representation used, in order to safely navigate within a given cell and successfully cross over to the next, for almost all initial configurations, until the desired configuration is reached. Finally, we present simulation results that demonstrate the efficacy of the proposed control scheme.

In this paper, we propose a visual servoing scheme that imposes predefined performance specifications on the image feature coordinate errors and satisfies the visibility constraints that inherently arise owing to the camera's limited field of view, despite the inevitable calibration and depth measurement errors. Its efficiency is demonstrated via comparative experimental and simulation studies.

Artificial Potential Fields (APFs) constitute an intuitive tool for designing autonomous robot navigation control schemes, though they generally suffer from the existence of local minima which may trap the robot away from its desired configuration, an issue usually addressed by appropriate offline “tuning” of the potential field's parameters. On the other side, most APF based approaches rely on a diffeomorphism to sphere worlds to handle realistic scenarios, which may be either costly to compute (e.g., conformal mappings) or requires some sort of preconditioning of the workspace (e.g., decomposition of complex geometries to simple elementary components). In this work, we first propose a constructive procedure to map multiply connected compact 2D workspaces to one or more punctured disks based on harmonic maps. Subsequently, we design an APF based control scheme along with an adaptive law for its parameters that requires no offline tuning to guarantee safe convergence to its goal configuration. Finally, an extensive simulation study is conducted to demonstrate the efficacy of the proposed control scheme.

In this paper, we consider a networked multi-robot system operating in an obstacle populated planar workspace under a single leader-multiple followers architecture. We propose a decentralized reconfiguration strategy of the set of connectivity and formation specifications that assures convergence to the desired point, while guaranteeing global connectivity. In particular, we construct a low-level Decentralized Navigation Functions based controller that encodes the goals and safety requirements of the system. However, owing to topological obstructions, stable critical points other than the desired one may appear. In such case, we employ a high-level distributed discrete procedure which attempts to solve a Distributed Constraint Satisfaction Problem on a local Voronoi partition, providing the necessary reconfiguration for the system to progress towards its goal. Eventually, we show that the system either converges to the desired point or attains a tree configuration with respect to the formation topology, in which case the system switches to a novel controller based on the Prescribed Performance technique, that eventually guarantees convergence. Finally, a simulation study clarifies and verifies the approach.

This paper addresses the fault tolerant control problem for an omni-directional mobile platform with four mecanum wheels moving on a well-known flat and constrained workspace with static obstacles. As a fault, we consider the case where a wheel cannot be actuated and hence it rotates freely around its drive shaft owing to the friction with the flat surface. Depending on the multitude of the faults, a robust motion control scheme is developed that achieves any desired configuration within the operational workspace, avoids collisions with the obstacles and does not violate the workspace boundaries despite the presence of dynamic model uncertainties. The challenge with respect to the current state of the art in fault tolerant control for such mobile platforms, where only one faulty wheel has been considered (i.e., the platform still retains its full actuation capabilities), lies in completely compensating up to two faulty wheels (i.e., the model becomes underactuated in this way) despite the dynamic model uncertainty and the presence of static obstacles in the workspace. Navigation Functions are innovatively incorporated with adaptive control techniques to deal with the parametric uncertainty in the robot dynamics, extending thus greatly the current state of the art in robust motion planning and collision avoidance by studying second order dynamics with parametric uncertainty. Finally, an extensive experimental study clarifies the proposed method and verifies its efficiency in various faults.

In this paper, we consider the formation control problem for multi-agent systems with unknown nonlinearities and disturbances, under an undirected communication protocol. Exploiting the recently developed prescribed performance control methodology, a robust distributed control scheme of minimal complexity is proposed that achieves and maintains arbitrarily fast and accurately the desired formation. No information regarding the agents' dynamic model is employed in the design procedure. Moreover, contrary to the related works on multi-agent systems, the transient and steady state response is fully decoupled by the underlying graph topology, the control gains selection and the agents' model uncertainties. In particular, the achieved performance is a priori and explicitly imposed by certain designer-specified performance functions. Finally, the theoretical findings are clarified and verified by an extensive simulation study.

This paper proposes an autonomous landing scheme for a small-scale flybarless helicopter equipped with low-cost navigation sensors. The main contribution of this paper is the design of a model-free motion controller that guarantees autonomous landing with prescribed transient and steady state response, despite the presence of external disturbances acting on the vehicle. The proposed control scheme is of low complexity and does not require any knowledge of the helicopter dynamic parameters. Hence, it can be easily implemented in embedded control platforms integrated on small-scale helicopters. In order to provide the controller with accurate estimation of the vehicle's state vector during the landing procedure, an asynchronous sensor fusion and state estimation algorithm, based on an Unscented Kalman Filter (UKF), has been also implemented. The performance and the efficiency of the overall scheme are experimentally verified using a small-scale flybarless helicopter in a real autonomous landing process.

Grasp planning in multi-robot systems is usually studied in a centralized setting with all robots sharing common knowledge about the overall system. Relaxing this assumption would allow multiple mobile manipulators to cooperate even without strict and precise coordination. Moreover, most typical tasks for cooperative settings, such as transporting heavy objects, require certain forces/torques to be exerted along/around particular directions, for instance, compensating for the weight of the transported object. In this paper, we propose task specific multi-robot grasp planning strategies that allow decentralized planning. Each agent plans its own actions without precise information about the other's plans. The approach is based on analysing a task specific grasp quality metric in a probabilistic context, compensating thus for the incomplete knowledge. Results from simulation experiments demonstrate that task independent planning is clearly inferior when task characteristics are known and thus task specific quality measures should be used. Furthermore, the proposed decentralized planning approaches clearly outperform the baseline and show close to globally optimal performance.

This paper addresses the problem of cooperative object transportation by two nonholonomic wheeled robots, with the coordination relying exclusively on implicit communication. We implement a leader-follower scheme, considering compliant contact between the object and the follower. Only the leader has knowledge of the object's goal configuration. The follower employs force/torque measurements to keep the contact stable and align itself with the object. The control scheme of the follower is based on the prescribed performance methodology guaranteeing thus the satisfaction of certain predefined force/torque constraints. In this way, the overall system acts as a perturbed version of the nominal car-like model. As a result, the leader implements a discontinuous control scheme, that drives robustly the system arbitrarily close to the goal configuration. No explicit data is exchanged among the robots, thus reducing bandwidth and increasing robustness and stealthiness. Finally, the proposed method is experimentally validated using two Pioneer mobile robots interconnected with a rod.

In this work we study the problem of landing a quadrotor on an inclined moving platform. The aerial robot employs an forward looking on-board camera to detect and observe the landing platform, which is carried by a mobile robot moving independently on an inclined surface. The platform may also be tilted with respect to the mobile robot. The overall goal is to design the aerial robot's control inputs such that it initially approaches the platform, while maintaining it within the camera's field of view and finally lands on it, in a way that minimizes the errors in position, attitude and velocity, while avoiding collision. Owing to the inclined ground and landing surface, the desired final state of the aerial robot is not an equilibrium state, which complicates significantly the control design. In that respect, a discrete-time non-linear model predictive controller was developed that optimizes both the trajectories and the time horizon, towards achieving the aforementioned objectives while respecting the input constraints as well. Finally, an extensive experimental study, with a Pioneer mobile robot and a Parrot ARDrone quadrotor, clarifies and verifies the theoretical findings.

In this paper, we study the decentralized control problem of a two-agent system under local goal specifications given as temporal logic formulas. The agents collaboratively carry an object in a leader-follower scheme and lack means to exchange messages on-line, i.e., to communicate explicitly. Specifically, we propose a decentralized control protocol and a leader re-election strategy that secure the accomplishment of both agents' local goal specifications. The challenge herein lies in exploiting exclusively implicit inter-robot communication that is a natural outcome of the physical interaction of the robots with the object. An illustrative experiment is included clarifying and verifying the approach.

This paper addresses the problem of cooperative object manipulation with the coordination relying solely on implicit communication. We consider a decentralized leader-follower architecture where the leading robot, that has exclusive knowledge of the object's desired trajectory, tries to achieve the desired tracking behavior via an impedance control law. On the other hand, the follower estimates the leader's desired motion via a novel prescribed performance estimation law, that drives the estimation error to an arbitrarily small residual set, and implements a similar impedance control law. Both control schemes adopt feedback linearization as well as load sharing among the robots according to their specific payload capabilities. The feedback relies exclusively on each robot's force/torque, position as well as velocity measurements and apart from a few commonly predetermined constant parameters, no explicit data is exchanged on-line among the robots, thus reducing the required communication bandwidth and increasing robustness. Finally, a comparative simulation study clarifies the proposed method and verifies its efficiency.

In this paper, we consider the two dimensional (2-D) predecessor-following control problem for a platoon of unicycle vehicles moving on a planar surface. More specifically, we design a decentralized kinematic control protocol, in the sense that each vehicle calculates its own control signal based solely on local information regarding its preceding vehicle, by its on-board camera, without incorporating any velocity measurements. Additionally, the transient and steady state response is a priori determined by certain designer-specified performance functions and is fully decoupled by the number of vehicles composing the platoon and the control gains selection. Moreover, collisions between successive vehicles as well as connectivity breaks, owing to the limited field of view of cameras, are provably avoided. Finally, an extensive simulation study is carried out in the WEBOTSTM realistic simulator, clarifying the proposed control scheme and verifying its effectiveness.

The majority of the works on grasping consider both object as well as robot hand parameters to be accurately known and do not take into account the constraints imposed by the robot hand. In this paper, a complete methodology is proposed that handles the grasping problem under a wide range of uncertainties. Initially, we search for an acceptable posture that provides robustness against positioning inaccuracies and maximizes the ability of the robot hand to exert forces on the object. Subsequently, in order to secure the grasp stability, we also deal with the determination of sufficient contact forces. Finally, an appropriate tactile sensor setup, mounted on the robot hand, allow us to reduce the magnitude of uncertainty regarding the grasping parameters. The efficiency of our approach is validated through extensive experimental paradigms using a 15 DoF DLR/HIT II robotic hand attached at the end effector of a 7 DoF Mitsubishi PA10 robotic manipulator.

This paper describes the design and implementation of a robust position tracking control scheme for an Autonomous Underwater Vehicle (AUV). The proposed controller does not require knowledge of the vehicle's dynamic parameters and guarantees prescribed transient and steady state performance despite the presence of external disturbances acting on the vehicle. The resulting scheme is of low complexity and computational cost and thus can be easily integrated to an embedded control platform of an AUV. The proposed control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using the Girona500 AUV into two different missions: a) navigation and stabilization to a specific configuration, b) meandrus-like trajectory tracking. In both cases the vehicle was under the influence of time-varying external disturbances caused by a high-pressure water jet installed on the Girona500 manipulator.

In this paper, we address the attitude and vertical stabilization problem for small-scale helicopters. An emergency controller that would successfully stabilize the helicopter in a safe flight mode when a pilot/autopilot fails to control it, owing to unexpected reasons, is of outmost importance in flight control systems. In this direction, we propose a low complexity nonlinear control scheme that drives the angles and the vertical speed to zero with prescribed transient and steady state response, without incorporating any knowledge of the dynamic model parameters in the control design. The stereographic coordinates were employed to model the attitude state of the helicopter in an attempt to guarantee the safe stabilization for every possible initial orientation without introducing any representation singularities as in the Euler angles representation or increasing complexity as in conventional four element quaternions. Moreover, the transient and steady state performance of the proposed scheme is a priori determined even in the presence of external disturbances. Furthermore, the overall control scheme can be easily implemented on embedded flight systems equipped with low-cost sensors. Finally, simulation and experimental results on a realistic platform verify the efficacy of the proposed method.

In this paper, we propose an optimization scheme for deriving task-specific force closure grasps for underactuated robot hands. Motivated by recent neuroscientific studies on the human grasping behavior, a novel grasp strategy is built upon past analysis regarding the task-specificity of human grasps, that also complies with the recent soft synergy model of underactuated hands. Our scheme determines an efficient force closure grasp (i.e., configuration and contact points/forces) with a posture compatible with the desired task, taking into consideration the mechanical and geometric limitations imposed by the design of the hand and the object shape. The efficiency of the algorithm is verified through simulated paradigms on a hypothetical underactuated hand with the kinematic model of the DLR/HIT II five fingered robot hand.

In this paper, we propose a robust model free control scheme of minimal complexity (it is a static scheme involving very few and simple calculations to output the control signal) for robotic manipulators, capable of achieving prescribed transient and steady state performance. No information regarding the robot dynamic model is employed in the design procedure. Moreover, the tracking performance of the developed scheme (i.e., convergence rate and steady state error) is a priori and explicitly imposed by a designer-specified performance function, and is fully decoupled by both the control gains selection and the robot dynamic model. In that respect, the selection of the control gains is only confined to adopting those values that lead to reasonable control effort. Finally, two experimental studies in the joint and the Cartesian workspace clarify the design procedure and verify its performance and robustness against external disturbances.

In this paper, we propose a complete methodology for deriving task-specific force closure grasps for multifingered robot hands under a wide range of uncertainties. Given a finite set of external disturbances representing the task to be executed, the concept of Q distance is introduced in a novel way to determine an efficient grasp with a task compatible hand posture (i.e., configuration and contact points). Our approach takes, also, into consideration the mechanical and geometric limitations imposed by the robotic hand design and the object to be grasped. In addition, incorporating our recent results on grasping [1], the ability of the robot hand to exert the required contact forces is maximized and robustness against positioning inaccuracies and object uncertainties is established. Finally, the efficiency of our approach is verified through an experimental study on the 15 DoF DLR/HIT II robotic hand attached at the end effector of the 7 DoF Mitsubishi PA10 robotic manipulator.

In this paper, we propose a novel image based visual servoing scheme that imposes prescribed transient and steady state response on the image feature coordinate errors and satisfies the visibility constraints that inherently arise owing to the limited field of view (FOV) of cameras. Visualizing the aforementioned performance specifications as error bounds, the key idea is to provide an error transformation that converts the original constrained problem into an equivalent unconstrained one, the stabilization of which proves sufficient to achieve prescribed performance guarantees and satisfy the inherent visibility constraints. The performance of the developed scheme is a priori and explicitly imposed by certain designer-specified performance functions, and is fully decoupled by the control gains selection, thus simplifying the control design. Moreover, its computational complexity proves significantly low. It is actually a static scheme involving very few and simple calculations to output the control signal, which enables easily its implementation on fast embedded control platforms. Finally, real-time experiments using an eye-in-hand robotic system verify the theoretical findings.

Tracking an underwater chain using an autonomous vehicle can be a first step towards more efficient solutions for cleaning and inspecting mooring chains. We propose to use a forward looking sonar as a primary perception sensor to enable the vehicle operation in limited visibility conditions and overcome the turbidity arisen during marine growth removal. Despite its advantages, working with acoustic imagery raises additional challenges to the involved image processing and control methodologies. In this paper we present a robust framework to perform chain following, combining perception, planning and control disciplines. We first introduce a detection system that exploits the sonar's high frame rate and applies local pattern matching to handle the complexity of detecting link chains in acoustic images. Then, a planning system deals with the dispersed detections and determines the link waypoints that the vehicle should reach. Finally, the vehicle is guided through these waypoints using a high level controller that has been tailored to simultaneously traverse the chain and keep track of upcoming links. Experiments on real data demonstrate the capability of autonomously follow a chain with sufficient accuracy to perform subsequent cleaning or inspection tasks.

In this work, we present a novel concept in the area of optimal grasp synthesis, confronting both geometric and mechanical constraints. Initializing from a locally optimal force distribution on some predefined feasible contact points, our method improves gradually the grasp quality avoiding simultaneously singularities and mechanical limitations. The proposed scheme implements sequential perturbations on the contact points and the wrist's position/orientation incorporating a post-optimality method in an iterative process to derive the consecutive optimal states. The main novelty of this work lies in the fact that only local information of the object's surface is required, which can be provided for instance by an appropriate tactile sensor suite. Finally, a simulation study on the DLR/HIT Hand II clarifies and verifies the efficiency of the approach.

This paper describes the design and implementation of a visual servo control scheme for an Autonomous Underwater Vehicle (AUV). The purpose of the control scheme is to navigate and stabilize the vehicle towards a visual target. The controller does not utilize the vehicle's dynamic model parameters and guarantees prescribed transient and steady state performance despite the presence of external disturbances representing ocean currents and waves. The proposed control scheme is of low complexity and can be easily integrated to an embedded control platform of an Autonomous Underwater Vehicle (AUV) with limited power and computational resources. Moreover, through the appropriate selection of certain performance functions, the proposed scheme guarantees that the target lies inside the onboard camera's field of view for all time. The resulting control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using the Girona 500 AUV.

This paper describes the design and implementation of a model-based sonar servoing control scheme for Autonomous Underwater Vehicles (AUVs). The proposed controller is designed for autonomous surveillance of underwater structures and it is robust against external disturbances and parametric uncertainties in the AUV dynamic model. The sensor suite includes a Multi-beam Imaging Sonar which provides measurements to a RANSAC-based algorithm for structure detection and pose estimation of the vehicle with respect to the structure. The sonar-based pose estimation is properly fused with the rest of the state measurements provided by a navigation module and the resulted state vector is incorporated as feedback to the controller. The proposed control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using the Nessie VI AUV in the presence of external disturbances (medium height waves).

We describe the design and implementation of an on-line identification scheme for Autonomous Underwater Vehicles (AUVs). The proposed method estimates the dynamic parameters of the vehicle based on a global derivative-free optimization algorithm. It is not sensitive to initial conditions, unlike other on-line identification schemes, and does not depend on the differentiability of the model with respect to the parameters. The identification scheme consists of three distinct modules: a) System Excitation, b) Metric Calculator and c) Optimization Algorithm. The System Excitation module sends excitation inputs to the vehicle. The Optimization Algorithm module calculates a candidate parameter vector, which is fed to the Metric Calculator module. The Metric Calculator module evaluates the candidate parameter vector, using a metric based on the residual of the actual and the predicted commands. The predicted commands are calculated utilizing the candidate parameter vector and the vehicle state vector, which is available via a complete navigation module. Then, the metric is directly fed back to the Optimization Algorithm module, and it is used to correct the estimated parameter vector. The procedure continues iteratively until the convergence properties are met. The proposed method is generic, demonstrates quick convergence and does not require a linear formulation of the model with respect to the parameter vector. The applicability and performance of the proposed algorithm is experimentally verified using the AUV Girona 500.

A neuroadaptive control scheme for strict feedback systems is designed, which is capable of achieving prescribed performance guarantees for the output error while keeping all closed-loop signals bounded, despite the presence of unknown system nonlinearities and external disturbances. The aforementioned properties are induced without resorting to a special initialization procedure or a tricky control gains selection, but addressing through a constructive methodology the longstanding problem in neural network control of a priori guaranteeing that the system states evolve strictly within the compact region in which the approximation capabilities of neural networks hold. Moreover, it is proven that robustness against external disturbances is significantly expanded, with the only practical constraint being the magnitude of the required control effort. A comparative simulation study clarifies and verifies the approach.

In this paper, we address unresolved issues in robot force/position tracking including the concurrent satisfaction of contact maintenance, lack of overshoot, desired speed of response, as well as accuracy level. The control objective is satisfied under uncertainties in the force deformation model and disturbances acting at the joints. The unknown nonlinearities that arise owing to the uncertainties in the force deformation model are approximated by a neural network linear in the weights and it is proven that the neural network approximation holds for all time irrespective of the magnitude of the modeling error, the disturbances, and the controller gains. Thus, the controller gains are easily selected, and potentially large neural network approximation errors as well as disturbances can be tolerated. Simulation results on a 6-DOF robot confirm the theoretical findings.

A control law is proposed that achieves predefined performance indices regarding the speed of response, the steady state and the allowed overshoot of the robot force/position tracking errors, ensuring no loss of contact of the robot end effector. The controller incorporates a transformed error, which includes the performance indices. The control objective is satisfied under parametric uncertainties in the robot dynamics and the elasticity model constant. Simulation results confirm the theoretical findings and compare the proposed controller with a conventional one.