Design of an Integral Suboptimal Second-Order Sliding Mode Controller for the Robust Motion Control of Robot Manipulators

The formulation of an integral suboptimal second-order sliding mode ((ISSOSM) control algorithm, oriented to solve motion control problems for robot manipulators, is presented in this paper. The proposed algorithm is designed so that the so-called reaching phase, normally present in the evolution of a system controlled via the sliding mode approach, is reduced to a minimum. This fact makes the algorithm more suitable to be applied to a real industrial robot, since it enhances its robustness, by extending it also to time intervals during which the classical sliding mode is not enforced. Moreover, since the algorithm generates second-order sliding modes, while the model of the controlled electromechanical system has a relative degree equal to one, the control action actually fed into the plant is continuous, which provides a positive chattering alleviation effect. The assessment of the proposal has been carried out by experimentally testing it on a COMAU SMART3-S2 anthropomorphic industrial robot manipulator. The satisfactory experimental results, also compared with those obtained with a standard proportional-derivative controller and with the original suboptimal algorithm, confirm that the new algorithm can actually be used in an industrial context

Robust motion control of a robot manipulator via Integral Suboptimal Second Order Sliding modes

This paper deals with the formulation of an Integral Suboptimal Second Order Sliding Mode control algorithm oriented to solve motion control problems for robot manipulators, taking into account the presence of unavoidable modelling uncertainties and external disturbances affecting the systems. The proposed algorithm is designed so that the so-called reaching phase, normally present in the evolution of a system controlled via a Sliding Mode controller, is reduced to a minimum. Moreover, since the relative degree of the relevant system output is suitably augmented through the use of an integrator, the control action affecting the robotic system is continuous, with a significant benefit, in terms of chattering alleviation, for the overall controlled electromechanical system. The verification and validation of our proposal have been performed by simulating the motion control scheme relying on a model of the considered robot, i.e. a COMAU SMART3-S2 anthropomorphic industrial robot manipulator, identified on the basis of real data. © 2013 IEEE

Robust motion control of a robot manipulator via Integral Suboptimal Second Order Sliding modes

This paper deals with the formulation of an Integral Suboptimal Second Order Sliding Mode control algorithm oriented to solve motion control problems for robot manipulators, taking into account the presence of unavoidable modelling uncertainties and external disturbances affecting the systems. The proposed algorithm is designed so that the so-called reaching phase, normally present in the evolution of a system controlled via a Sliding Mode controller, is reduced to a minimum. Moreover, since the relative degree of the relevant system output is suitably augmented through the use of an integrator, the control action affecting the robotic system is continuous, with a significant benefit, in terms of chattering alleviation, for the overall controlled electromechanical system. The verification and validation of our proposal have been performed by simulating the motion control scheme relying on a model of the considered robot, i.e. a COMAU SMART3-S2 anthropomorphic industrial robot manipulator, identified on the basis of real data. © 2013 IEEE

MPC for Robot Manipulators with Integral Sliding Modes Generation

This paper deals with the design of a robust hierarchical multiloop control scheme to solve motion control problems for robot manipulators. The key elements of the proposed control approach are the inverse dynamics-based feedback linearized robotic multi-input-multi-output (MIMO) system and the combination of a model predictive control (MPC) module with an integral sliding mode (ISM) controller. The ISM internal control loop has the role to compensate the matched uncertainties due to unmodeled dynamics, which are not rejected by the inverse dynamics approach. The external loop is closed relying on the MPC, which guarantees an optimal evolution of the controlled system while fulfiling state and input constraints. The motivation for using ISM, apart from its property of providing robustness to the scheme with respect to a wide class of uncertainties, is also given by its capability of enforcing sliding modes of the controlled system since the initial time instant, allowing one to solve the MPC optimization problem relying on a set of linearized decoupled single-input-single-output (SISO) systems that are not affected by uncertain terms. The proposal has been verified and validated in simulation, relying on a model of a COMAU Smart3-S2 industrial robot manipulator, identified on the basis of real data

Actuator fault diagnosis with neural network-integral sliding mode based unknown input observers

This paper proposes an integral sliding mode (ISM) based unknown input observer (UIO) which is able to perform fault diagnosis (FD) in condition of lack of knowledge of the plant model. In particular, a two-layer neural network (NN) is employed to estimate online the drift term of the system dynamics needed to compute the so-called integral sliding manifold. The weights of such a NN are updated online using adaptation laws directly derived from theoretical analysis, carried out in this paper. Finally, the proposal has been assessed in simulation relying on a benchmark model of a DC motor

Integral Sliding Mode based switched structure control scheme for robot manipulators

This paper deals with the design of a switching control scheme for robot manipulators. The key elements of the proposed scheme are the inverse dynamics based centralized controller and a set of decentralized controllers. They enable to realize two possible control structures: one of centralized type, the other of decentralized type. All the controllers are based on Integral Sliding Mode (ISM), so that matched disturbances and uncertain terms, due to unmodeled dynamics or couplings effects, are suitably compensated. The idea of using ISM, apart from its feature of providing robustness in front of a wide class of uncertainties, is motivated by its capability of acting as a 'perturbation estimator', which is a clear advantage in the considered case. In fact, it allows one to define a switching rule in order to choose one of the two control structures featured in the scheme, depending on the requested performances. As a consequence, the resulting control scheme is more efficient from computational viewpoint, while maintaining the advantages in terms of stability and robustness of the conventional standalone control schemes. In addition, the scheme can accommodate a variety of velocity and acceleration requirements, in contrast with the capability of the genuine decentralized or centralized control structures. The verification and the validation of our proposal have been carried out in simulation, relying on a model of an industrial robot manipulator COMAU SMART3-S2, with injected noise to better emulate a realistic setup

Design of Adaptive Sliding Mode Fuzzy Control for Robot Manipulator Based on Extended Kalman Filter

In this work, a new adaptive motion control scheme for robust performance control of robot manipulators is presented. The proposed scheme is designed by combining the fuzzy logic control with the sliding mode control based on extended Kalman filter. Fuzzy logic controllers have been used successfully in many applications and were shown to be superior to the classical controllers for some nonlinear systems. Sliding mode control is a powerful approach for controlling nonlinear and uncertain systems. It is a robust control method and can be applied in the presence of model uncertainties and parameter disturbances, provided that the bounds of these uncertainties and disturbances are known. We have designed a new adaptive Sliding Mode Fuzzy Control (SMFC) method that requires only position measurements. These measurements and the input torques are used in an extended Kalman filter (EKF) to estimate the inertial parameters of the full nonlinear robot model as well as the joint positions and velocities. These estimates are used by the SMFC to generate the input torques. The combination of the EKF and the SMFC is shown to result in a stable adaptive control scheme called trajectory-tracking adaptive robot with extended Kalman (TAREK) method. The theory behind TAREK method provides clear guidelines on the selection of the design parameters for the controller. The proposed controller is applied to a two-link robot manipulator. Computer simulations show the robust performance of the proposed scheme