In this article, a trajectory tracking control strategy is proposed for robot manipulators via a fully actuated system (FAS) approach, which has shown its simplicity and flexibility for most of the nonlinear controller design. However, the motion control for robot manipulators is more complicated since unknown dynamical model, external disturbances, friction forces, and various physical constraints are required to be considered. Therefore, the FAS approach cannot be straightforwardly applied. To address these challenges, the dynamic model of robot manipulators is established via model identification methods. Furthermore, based on the identified model, an FAS composite control strategy with simple structure is designed, which is achieved by integrating a high-order disturbance observer (HODO) in the inner loop, with an FAS trajectory tracking controller in the outer loop. Specifically, the HODO is utilized for handling the uncertain dynamics and external disturbances. Moreover, the controller gains are optimized using a gradient-based optimal parameter tuning method (OPTM). By imposing joint angle constraints, joint angular velocity constraints, and input torque limits into the formulation, the OPTM also ensures the satisfaction of these physical constraints. Numerical simulations and experiments are provided to validate the performance of the proposed controller.

Precise motion control for free-flying space manipulators (FFSMs) plays an important role in space missions. However, system uncertainties and various physical constraints severely degrade the trajectory tracking performance. In order to tackle these difficulties, a fully actuated system approach (FASA)-based composite controller is developed, which consists of a nonlinear disturbance observer (NDO) in the inner loop and a high-precision trajectory controller in the outer loop. More specifically, the NDO is designed for tackling system uncertainties. Moreover, a gradient-based optimal parameter tuning method is developed for tuning the control gains of the composite controller. The satisfaction of physical constraints, which include angular constraints and actuator constraints can be guaranteed by the gradient-based optimal parameter tuning method. Therefore, the high-precision trajectory tracking performance, optimal control gains, angular constraints and actuator constraints can be ensured simultaneously. Simulation results are presented to demonstrate the effectiveness of the proposed method.

This paper studies the prescribed-time tracking control problem for nonlinear systems with unknown time-varying parameters, mismatched nonvanishing uncertainties, unknown control coefficients, and potential actuator faults. The proposed control strategy employs a prescribed-time adjustment function to guarantee that state errors converge to zero within a specified time, despite the presence of nonvanishing mismatched uncertainties. The proposed controller avoids the need to use adaptive mechanisms and is therefore simple to implement. Moreover, the proposed control strategy does not require the control coefficient bounds to be known. Based on a prescribed-time scaling function and a barrier function, prescribed performance for state errors is guaranteed, which is uniform with respect to initial conditions, eliminating the need for an offline optimization algorithm to determine the controller gains. The simulation results demonstrate the effectiveness of the proposed control framework.

This paper is devoted to designing a robust model reference controller for uncertain second-order systems subject to parameter uncertainties. The system matrix of the first-order reference model is more general and the parameter uncertainties are assumed to be norm-bounded. The design of robust controller can be devided into two separate problems: problem robust stabilization and problem robust compensation. Based on the solution of generalized Sylvester matrix equations, we obtain some sufficient conditions to guarantee the complete parameterization of the controller. Then, the problem robust compensation of the closed-loop system is estimated by solving a convex optimisation problem with a set of linear matrix equations constraints. Two simulation examples are provided to illustrate the effectiveness of the proposed technique.

This paper is concerned with the model reference tracking control (MRTC) problem of discrete-time Markovian jump systems under external disturbance and input constraints. A state feedback controller is proposed to guarantee that the state vector of the system tracks precisely a given state vector of a reference model. The tracking problem is formulated as an optimisation problem and an approach to design the state feedback controller which stabilises the augmented system and rejects the external disturbance simultaneously. Sufficient conditions for stochastic stability and stabilisation of the augmented systems are derived via linear matrix inequalities. Then, under the assumption of completely unknown system modes, a common MRTC approach is developed to guarantee the stochastic stability, the disturbance rejection and the input constraints. Finally, a simulation example is provided to illustrate the effectiveness of the proposed method.