Adaptive path tracking control of robotic manipulators with unknown payload dynamics

In this study, we consider the output feedback position tracking control of robot manipulator via a fuzzy logic adaptation compensation method which enables the use of adaptive controller techniques for the full order but not fully linearly parameterized (LP) robot dynamics. Specifically we propose an output feedback position tracking controller that achieves semi-globally ultimately bounded link position tracking despite the lack of link velocity measurements and the non-LP uncertainties associated with the robot dynamics. Simulation results are presented to illustrate the performance of the controller

A neural network (NN)-based adaptive control law is proposed for the tracking control of an n-link robot manipulator with unknown dynamic nonlinearities. Basis-function-like networks are employed to approximate the plant nonlinearities, and the bound on the NN reconstruction error is assumed to be unknown. The proposed NN-based adaptive control approach integrates the NN approach and an adaptive implementation of the discrete variable structure control, with a simple estimation mechanism for the upper bound on the NN reconstruction errors and an additional control input as a function of the estimate. Lyapunov stability theory is used to prove the uniform ultimate boundedness of the tracking error, and simulation results demonstrate the applicability of the proposed method to achieve desired performance.

The path tracking control problem of robot manipulators carrying unknown payloads is studied on the basis of the payload-dependent robot model and its structural properties. The nonlinear payload-dependent dynamic model of a robot manipulator is derived, and several structure properties of this model are investigated. On the basis of these properties, a path tracking control strategy is presented. It is shown that the presented strategy not only compensates for the effects of couplings, nonlinearities, and payload uncertainty but also guarantees tracking convergence.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A novel approach is presented for adaptive trajectory tracking of robot manipulators using a three-stage hierarchical neural network model involving Support Vector Machines (SVM) and an adaptive unsupervised Neural Network. It involves a novel adaptive Self Organizing feature map (SOFM) in the first stage which aims at clustering the input variable space into smaller sub-spaces representative of the input space probability distribution and preserving its original topology, while rapidly increasing, on the other hand, cluster distances. Moreover, its codebook vector adaptation rule involves m-winning neurons dynamics and not the winner takes all approach. During convergence phase of the map a group of Support Vector Machines, associated with its codebook vectors, is simultaneously trained in an online fashion so that each SVM learns to positively respond when the input data belong to the topological sub-space represented by its corresponding codebook vector, taking into account similarity with that codebook vector. Moreover, it learns to negatively respond to input data not belonging to such a previously mentioned corresponding topological sub-space. The proposed methodology is applied, with promising results, to the design of a neural-adaptive trajectory tracking controller, by involving the computer-torque approach, which combines the proposed three-stage neural network model with a classical servo PD feedback controller. The results achieved by the suggested hierarchical SVM approach are favorably compared to the ones obtained by traditional (PD) and non-hierarchical neural network based controllers.

Adaptive control of robot manipulators based on the use of a sliding variable and the passivity property of the dynamic equations was originally designed and successfully applied to task tracking in joint space few decades ago. Surprisingly, no extension is available to date for the multi-task case such that the controller tends towards enforcing strict task priorities as the parameters tend to their real values. Given the importance of both multi-task control and adaptive algorithms, an approach that deals with this situation has an important impact in the robotic field. This letter provides a solution to this problem leveraging on our recent formulation of hierarchical multi-task impedance control for trajectory tracking and on geometric methods for model identification. Experiments are used to validate the stability analysis.

The trajectory tracking problem of a class of robot manipulators is investigated by a simpler design adaptive neural network(NN) in this paper. The Radial Basis Function(RBF) NN is utilized to handle the uncertainties of the dynamics. Compared with the traditional schemes, the dimension of the input vectors of RBFNN is decrease from $4n$ to $3n$ but it have equal tracking and approximation performances. The output feedback control is considered when the velocity information cannot be obtained. Moreover, the weights of RBFNN converge to its optimal value by using the auxiliary filter to estimate weights error. The robot manipulator system is semi-globally and uniformly bounded which is proved by Lyapunov's theory. Simulation results demonstrate that the simpler controller has the same capability compared with the non-simplified method.

In this paper, a controller for robot manipulators is proposed to guarantee the tracking error of the systems bounded by predefined decreasing boundary. In this control scheme, a multi-layer neural network is used to approximate the unknown non-linear items, and the robustifying control term is used to compensate the approximation errors. The adaptive laws of weights of the neural network and robustifying control term are derived based on the Lyapunov stability analysis, so that, under appropriate assumptions, the transient and steady-state error bounds can be guaranteed. Compared with the existing work, the adaptable parameters in the proposed method do not need an off-line training procedure for better approximation. Simulations performed on a two-link robot manipulator illustrate the developed controller and demonstrate its performance.

A switched adaptive controller is designed for robot manipulators with friction and changing loads. The nonlinear friction is depicted by a nonlinear friction model, and a switched nonlinear system is used to model the parameter jump caused by load change. Hyperstability theory is used in the designing procedure, which provides more options for adaptive laws than Lyapunov theory. In the presence of friction and changing loads, asymptotic tracking is achieved under arbitrary switching, which is not able to accomplish by a non-switched adaptive controller. The proposed method is validated by a simulation of a 2 degree of freedom manipulator.

This paper proposes a novel adaptive path tracking and stability control strategy for autonomous vehicles based on intelligent tires technology that is introduced to estimate the tire-road contact states based on the in-tire sensor signals. Firstly, a tire finite element model was established based on ABAQUS software. On this basis, the Spearman correlation analysis method was used to acquire the optimal sensitive positions of the in-tire acceleration sensors. The feature values of the inputs of the tire-road contact states estimation model were determined through the PLS-VIP analysis method. Then, the tire slip angle and lateral force estimation models whose inputs are the in-tire sensor signals was constructed using the LSTM neural network. Subsequently, an adaptive MPC path tracking control strategy in which the tire cornering stiffness is calculated in real-time by the intelligent tires model was designed. Finally, an adaptive NFTSM lateral stability control strategy which uses the tire lateral force estimated by the intelligent tires model was designed to determine the direct yaw moment, and an efficient and concise algorithm was then designed to achieve torque distribution. The simulation results indicate that the proposed control strategy provides better path tracking accuracy and stability control effect in emergency lane change and high-speed evasive maneuvers under different road conditions.

In this brief, a novel direct adaptive fuzzy tracking control (DAFTC) scheme for marine vehicles with fully unknown parametric dynamics and uncertainties is proposed. The significant contributions of the DAFTC approach are as follows. First, in the backstepping framework, fully unknown parametric dynamics and uncertainties are encapsulated into a lumped nonlinearity function encompassing system states and virtual control signals. Second, the integrated nonlinearity function is further identified online by an adaptive fuzzy approximator that synthesizes a model-free control scheme (termed DAFTC) without requiring any a priori knowledge of the model. Third, tracking errors are proven to be uniformly ultimately bounded (UUB) and can converge to an arbitrarily small neighborhood of zero in a finite time. Simulation studies and comprehensive comparisons demonstrate that the proposed DAFTC scheme has remarkable performance and is superior in both tracking accuracy and unknown parametric dynamics compensation.

In this paper, the receding horizon near-optimal tracking control problem about a class of continuous-time nonlinear systems with fully unknown dynamics is considered. The main challenges of this problem lie in two aspects: 1) most existing systems only restrict their considerations to the state feedback part while the input channel parameters are assumed to be known. This paper considers fully unknown system dynamics in both the state feedback channel and the input channel and 2) the optimal control of nonlinear systems requires the solution of nonlinear Hamilton-Jacobi-Bellman equations. Up to date, there are no systematic approaches in the existing literature to solve it accurately. A novel model-free adaptive near-optimal control method is proposed to solve this problem via utilizing the Taylor expansion-based problem relaxation, the universal approximation property of sigmoid neural networks, and the concept of sliding mode control. By making approximation for the performance index, it is first relaxed to a quadratic program, and then, a linear algebraic equation with unknown terms. An auxiliary system is designed to reconstruct the input-to-output property of the control systems with unknown dynamics, so as to tackle the difficulty caused by the unknown terms. Then, by considering the property of the sliding-mode surface, an explicit adaptive near-optimal control law is derived from the linear algebraic equation. Theoretical analysis shows that the auxiliary system is convergent, the resultant closed-loop system is asymptotically stable, and the performance index asymptomatically converges to optimal. An illustrative example and experimental results are presented, which substantiate the efficacy of the proposed method and verify the theoretical results.

In this paper, the global adaptive neural control with finite-time (FT) convergence learning performance for a general class of nonlinear robot manipulators has been investigated. The scheme proposed in this paper offers a subtle blend of neural controller with robust controller, which palliates the limitation of neural approximation region to ensure globally uniformly ultimately bounded (GUUB) stability by integrating a switching mechanism. Morever, the proposed scheme guarantees the estimated neural weights converging to optimal values in finite time by embedding an adaptive learning algorithm driven by the estimated weights error. The optimal weights obtained through the learning process of the neural networks (NNs) will be reused next time for repeated tasks, and can thus reduce computational load, improve transient performance and enhance robustness. The simulation studies have been carried out to demonstrate the superior performance of the controller in comparison to the conventional methods.

In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

This paper presents a novel adaptive fast-terminal neuro-sliding mode control (AFTN-SMC) for a two-link robot manipulator with unknown dynamics and external disturbances. The proposed controller is chattering-free and adaptive to the time-varying system uncertainties. Furthermore, the radial base function neural network (RBFNN) is employed to approximate the unknown state dynamics. The simulations have been completed in MATLAB, which illustrates the successful implementation of the proposed controller. The results showcased the effectiveness of the AFTN-SMC in achieving accurate tracking and stability, even in the presence of uncertainties and parameter variations. The incorporation of the RBFNN in the controller proved to be a valuable tool for approximating the unknown dynamics, enabling accurate estimation and control of the manipulator’s behavior. The research presented in this paper contributes to the advancement in control techniques for robot manipulators in diverse industrial and automation applications.

This paper presented a novel adaptive cascade nonlinear trajectory tracking control scheme of over-actuated autonomous electric vehicles involving input saturation. First, a nonlinear vehicle dynamic model with input saturation is established, which can accurately describe the features of uncertainties and coupling of autonomous electric vehicles, and the hyperbolic tangent function is designed to estimate the saturation function for dealing with the input saturation problem. Then, a novel adaptive cascade trajectory tracking control scheme is designed. An adaptive neural network-based terminal sliding control law is proposed for producing the generalized force/moment in real-time, the asymptotic stability of this adaptive control system is proven by Lyapunov theory, and a quasi-newton distribution law is designed to determine the optimum tire forces that guarantee the actual generalized forces/moment are close to the desired values. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.