Adaptive robust control of the mobile manipulator based on neural network

This paper addresses the trajectory tracking problem for an redundantly-actuated omnidirectional mobile manipulator system with uncertainties and disturbances. The proposed algorithm is robust adaptive control strategy and the parameter estimates are tuned online. First, for designing controller, the conservative upper-bounded function of dynamic model of omnidirectional mobile manipulator system is derived based on the dynamic structure properties. Then, a robust adaptive control scheme is presented to ensure trajectory tracking effect of this closed-loop system. The asymptotical stability is verified a Lyapunov method. Finally, simulation examples are given to demonstrate the proposed approach can guarantee the whole system converge to the desired manifold with prescribed performance.

Adaptive neural control for mobile manipulator systems based on adaptive state observer

Mobile manipulators have attracted a lot of interest recently. This paper discusses the development of mobile manipulators in recent years in the aspect of motion planning and coordinated control between mobile platform and manipulator. An intelligent controller based on RBF neural network is introduced for the coordinating control of the mobile manipulator, which mimics human behavior. Simulation results was given out and the method has been applied on the Hebut-II mobile robot platform. Experimental results verified the validity of the method.

Hydraulic system is widely used in industry where large actuation forces are needed such as electro-hydraulic positioning system, active suspension control, and so on. One of the main problem in hydraulic system is the nontriviality, for instance, the deadbands in valve operation, frictions of links, saturations, pressure dynamics, etc. both system unknown nonlinearities and external disturbances should be considered in controller design. For underwater hydraulic manipulator, because of the environment of sea and the large nonlinearities of the hydraulic manipulator system, both system unknown nonlinearities and external disturbances should be considered in controller design. A lot of control methods have been proposed to solve the problem properly, such as sliding mode control, adaptive control, fuzzy adaptive control, adaptive robust control, ect. However, due to limited knowledge about hydraulic manipulator system, it is hard to precisely estimate the nonlinearities for a possible higher performance. In this paper, a neural network adaptive robust control method is proposed to help control an underwater hydraulic manipulator. In order to achieve better performance using adaptive robust control strategy, unknown nonlinearities in the system are approximated by the outputs of radial basis function neural network (RBFNNs). A projection law has been used to bound the NN weights when they are tuning on-line to avoid possible adaptive divergence, and the tracking transient performance is guaranteed by robust control law. Simulation results illustrate that manipulator has a good tracking performance.

This paper discusses the modeling and control of a spatial mobile manipulator which consists of a robotic manipulator mounted upon a wheeled mobile platform. By neglecting slip of the wheeled platform’s tires, nonholonomic constraints are introduced into the equations of motion which complicates the control problem. The dynamic equations of the mobile manipulator are derived using the Lagrange-d’Alembert formulation. In this formulation, the equations of motion are projected onto the subspace of allowable motions. Then, the solvability of tracking problems for the mobile manipulator is investigated by using input-output feedback linearization. The feasibility of the control approach is demonstrated through computer simulation.

A torque controller based on adaptive robust control is proposed for trajectory tracking of nonholonomic wheeled mobile robot with kinematic and dynamic uncertainties. In this controller, the parametric uncertainties are estimated by the adaptive control, and the non-parametric uncertainties are compensated by the adaptive robust control. The operation efficiency of the system is improved by distinguishing the differences between parametric and non-parametric uncertainties. Furthermore, the transient performance of adaptive control is optimized by the adaptive robust control and the measurements of uncertainties bounds are avoided, this strengthened the applicability of the controller. The uniformly ultimately bounded of closed-loop control system is proved by the Lyapunov theory. Simulations show that the adversely impacts of complex system uncertainties can be eliminated effectively and the high-precision trajectory tracking is guaranteed.

This paper presents an embedded adaptive robust controller for trajectory tracking and stabilization of an omnidirectional mobile platform with parameter variations and uncertainties caused by friction and slip. Based on a dynamic model of the platform, the adaptive controller to achieve point stabilization, trajectory tracking, and path following is synthesized via the adaptive backstepping approach. This robust adaptive controller is then implemented into a high-performance field-programmable gate array chip using hardware/software codesign technique and system-on-a-programmable-chip design concept with a reusable user intellectual property core library. Furthermore, a soft-core processor and a real-time operating system are embedded into the same chip for realizing the control law to steer the mobile platform. Simulation results are conducted to show the effectiveness and merit of the proposed control method in comparison with a conventional proportional-integral feedback controller. The performance and applicability of the proposed embedded adaptive controller are exemplified by conducting several experiments on an autonomous omnidirectional mobile robot.

This research is concerned with a control method of mobile manipulators using a neural network for traveling operations on a waved road surface. When the mobile manipulator travels on an unknown terrain, the mobile manipulator is subjected to the effect of the disturbance torques. In this research, the controller is proposed to compensate the disturbance torques. First, an exact mathematical mobile manipulator model traveling on a waved road surface is proposed with the pitching motion along to the road. Second, the neuro adaptive controller with the compensator using the neural network is proposed to improve the trajectory-tracking control performances. Third, the control performance of the adaptive controller is examined, and the tracking error depending on the amplitude of the waved road and the traveling speed are discussed. Finally, it is clarified through simulation experiments that the traveling-performance are improved by the our proposed neuro adaptive controller.

In this article, motion/force control problem of a class of constrained mobile manipulators with unknown dynamics is considered. The system is subject to both holonomic and nonholonomic constraints. An adaptive recurrent neural network controller is proposed to deal with the unmodelled system dynamics. The proposed control strategy guarantees that the system motion asymptotically converges to the desired manifold while the constraint force remains bounded. In addition, an adaptive method is proposed to identify the contact surface. Simulation studies are carried out to verify the validation of the proposed approach.

In this article, a robust adaptive self-structuring fuzzy control (RASFC) scheme for the uncertain or ill-defined nonlinear, nonaffine systems is proposed. The RASFC scheme is composed of a robust adaptive controller and a self-structuring fuzzy controller. In the self-structuring fuzzy controller design, a novel self-structuring fuzzy system (SFS) is used to approximate the unknown plant nonlinearity, and the SFS can automatically grow and prune fuzzy rules to realise a compact fuzzy rule base. The robust adaptive controller is designed to achieve an L 2 tracking performance to stabilise the closed-loop system. This L 2 tracking performance can provide a clear expression of tracking error in terms of the sum of lumped uncertainty and external disturbance, which has not been shown in previous works. Finally, five examples are presented to show that the proposed RASFC scheme can achieve favourable tracking performance, yet heavy computational burden is relieved.

In this paper, robust adaptive control strategies are designed for Underwater Remotely Operated Vehicles (ROVs) with velocity constraints. First, robust control strategies are investigated for under-water ROVs, and then adaptive robust control strategies are further developed with online parameter estimation. To prevent the velocity constraint violation, the Barrier Lyapunov Function (BLF) is employed in Lyapunov synthesis. By ensuring the boundedness of the BLF, we also guarantee that the velocity constraints are not transgressed. The stability analysis of the closed-loop system is provided and all closed-loop signals are ensured to be bounded. Simulation results for 5 degree-of-freedom (DOF) underwater ROV demonstrate the effectiveness of the proposed approach.

SummaryThis article addresses a novel multilateral teleoperation control scheme for single‐master multiple slave systems, which can be extended to n masters and n slaves without the loss of generality, where the master is a m degrees of freedom (DOF) manipulator arm and the slaves are m DOF mobile manipulators. The human operator operates the master robot to remotely control the slaves handling a target object. The master position signal is transmitted to the slave side to generate a desired object trajectory as well as the reference mobile base velocity. An adaptive robust controller is designed for the slaves to follow the desired trajectory from the master, which not only provides the excellent trajectory tracking but also optimize the internal force distribution of the object. A null space controller is designed for the mobile platforms of the mobile manipulators to achieve the velocity consensus while achieving the main task of object transportation. The novel control design uses the transmission of the environmental force feedback over the communication channel by the estimated parameters of the environment, which helps retain the stability of the overall system. The environmental force is predicted on the master side based on the estimated environmental parameters. The proposed control design can simultaneously achieve the objectives of stability, synchronization, and optimal internal force distribution. The simulation results of a single‐master and three slaves teleoperation system validate the efficacy of the proposed control algorithm.

A pneumatic parallel platform driven by an air cylinder and three circumambient pneumatic muscles was considered. Firstly, a mathematical model of the pneumatic servo system was developed for the MIMO nonlinear model-based controller designed. The pneumatic muscles were controlled by three proportional position valves, and the air cylinder was controlled by a proportional pressure valve. As the forward kinematics of this structure had no analytical solution, the control strategy should be designed in joint space. A cross-coupling integral adaptive robust controller (CCIARC) which combined cross-coupling control strategy and traditional adaptive robust control (ARC) theory was developed by back-stepping method to accomplish trajectory tracking control of the parallel platform. The cross-coupling part of the controller stabilized the length error in joint space as well as the synchronization error, and the adaptive robust control part attenuated the adverse effects of modelling error and disturbance. The force character of the pneumatic muscles was difficult to model precisely, so the on-line recursive least square estimation (RLSE) method was employed to modify the model compensation. The projector mapping method was used to condition the RLSE algorithm to bound the parameters estimated. An integral feedback part was added to the traditional robust function to reduce the negative influence of the slow time-varying characteristic of pneumatic muscles and enhance the ability of trajectory tracking. The stability of the controller designed was proved through Laypunov’s theory. Various contrast controllers were designed to testify the newly designed components of the CCIARC. Extensive experiments were conducted to illustrate the performance of the controller.

Uncertainties are prevalent in real-world applications of bipedal walking robots, which may deteriorate the robot’s locomotion performance and even cause instability. However, designing controllers to address uncertainties for bipedal robotic walking is challenging mainly due to the high complexity of the hybrid walking dynamics under uncertainties. In this paper, an adaptive robust control strategy is proposed by combining control Lyapunov functions and the construction of multiple Lyapunov functions for provably guaranteeing the stability and trajectory tracking performance of fully actuated bipedal robots in the presence of uncertainties such as modeling errors and unmodeled disturbances. Simulation results on a three-dimensional bipedal robot with nine revolute joints were conducted to illustrate that the proposed adaptive robust control law can ensure stability and satisfactory tracking performance in the presence of parametric modeling uncertainties and unmodeled nonlinearities.

Robust adaptive integral backstepping control for opto-electronic tracking system based on modified LuGre friction model