The paper presents a phenomenon of directional change in the case of a LQR controller applied to multivariable plants with amplitude and rate constraints imposed on the control vector, as well as the impact of the latter on control performance, with the indirect observation of the windup phenomenon effect via frequency of consecutive resat- urations. The interplay of directional change of the computed control vector with control performance has been thoroughly investigated, and it is a result of the presence of con- straints imposed on the applied control vector for different ratios of the number of control inputs to plant outputs. The impact of the directional change phenomenon on the control performance (and also on the windup phenomenon) has been defined, stating that performance deterioration is not tightly coupled with preservation of direction of the computed control vector. This conjecture has been supported by numerous simulation results for different types of plants with different LQR controller parameters.

The paper presents its contribution to tracking control design of mechanical systems in underactuated mode conditions, i.e. when the number of actuators is less than the number of possible control inputs. Fully actuated mechanical systems are quite well-researched and controller designs are well-developed for them as well. However, due to costs, weight, design, and performance regimes or due to an actuator failure, the underactuated control mode is required in applications. With the aid of the computational procedure for constrained dynamics (CoPCoD), the constrained dynamics, i.e. the reference motion dynamics, and tracking control in an underactuated mode are designed for an example of a three-link planar manipulator model with rigid and flexible links. A dynamic optimization problem is formulated in the paper to obtain optimal time courses of manipulator joint coordinates in underactuated mode conditions in order to apply them to a manipulator driving links controller.

The iterative learning fault-tolerant control strategies with non-strict repetitive initial state disturbances are studied for the linear discrete networked control systems (NCSs) and the nonlinear discrete NCSs. In order to reduce the influence of the initial state disturbance in iteration, for the linear NCSs, considering the external disturbance and actuator failure, the iterative learning fault-tolerant control strategy with impulse function is proposed. For the nonlinear NCSs, the external disturbance, packet loss and actuator failure are considered, the iterative learning fault-tolerant control strategy with random Bernoulli sequence is provided. Finally, the proposed control strategies are used for simulation research for the linear NCSs and the nonlinear NCSs. The results show that both strategies can reduce the influence of the initial state disturbance on the tracking effect, which verifies the effectiveness of the given method.