Explore the vast range of titles available.

This paper considers a problem of tracking control design for different types of nonholonomic mobile manipulators. The mobile platform is in form of a unicycle. In the first step, an input–output decoupling controller is developed. The proposed selection of output functions is in more general form than the output functions previously introduced by others [Yamamoto and Yun]. It makes possible to move simultaneously, the mobile platform and the manipulator built on it. Regularity conditions that guarantee the existence of the input–output decoupling control law are presented. In the second step, trajectory and path tracking controllers are formulated and presented. Theoretical considerations are illustrated with simulations for the mobile manipulator consisting of a vertical, three degree of freeedom (DOF) pendulum (with holonomic or nonholonomic drives) mounted atop of a unicycle.

This study introduces a novel control strategy tailored to nonlinear systems with non-minimum phase (NMP) characteristics. The framework leverages reinforcement learning within a cascade control architecture that integrates an Actor–Critic structure. Controlling NMP systems poses significant challenges due to the inherent instability of their internal dynamics, which hinders effective output tracking. To address this, the system is reformulated using the Byrnes–Isidori normal form, allowing the decoupling of the input–output pathway from the internal system behavior. The proposed control architecture consists of two nested loops: an inner loop that applies input–output feedback linearization to ensure accurate tracking performance, and an outer loop that constructs reference signals to stabilize the internal dynamics. A key innovation in this design lies in the incorporation of symmetry principles observed in both system behavior and control objectives. By identifying and utilizing these symmetrical structures, the learning algorithm can be guided toward more efficient and generalized policy solutions, enhancing robustness. Rather than relying on classical static optimization techniques, the method employs a learning-based strategy inspired by previous gradient-based approaches. In this setup, the Actor—modeled as a multilayer perceptron (MLP)—learns a time-varying control policy for generating intermediate reference signals, while the Critic evaluates the policy’s performance using Temporal Difference (TD) learning. The proposed methodology is validated through simulations on the well-known Inverted Pendulum system. The results demonstrate significant improvements in tracking accuracy, smoother control signals, and enhanced internal stability compared to conventional methods. These findings highlight the potential of Actor–Critic reinforcement learning, especially when symmetry is exploited, to enable intelligent and adaptive control of complex nonlinear systems.

In this paper, the basic theory of the model reference adaptive control design and issues of particular relevance to control nonlinear dynamic plants with a relative degree greater than or equal to one with unknown parameters are detailed. The studied analysis was motivated through its application to a robot manipulator with six degrees of freedom. After linearization using the input-output feedback linearization and decoupling algorithm, the nonlinear Multi-input Multi-output system was transformed into six independent single-input single-output linear subsystems each one has a relative degree equal to two, the obtained results in different simulations shows that the augmented reference model adaptive controller has been successfully implemented.

This paper presents a nonlinear control strategy utilizing the linearization and input-output decoupling approach for a nonlinear dynamic model of proton exchange membrane fuel cells (PEMFCs). The multiple-input single-output (MISO) nonlinear model of the PEMFC is derived first. The dynamic model is then transformed into a multiple-input multiple-output (MIMO) square system by adding additional states and outputs so that the linearization and input-output decoupling approach can be directly applied. A PI tracking control is also introduced to the state feedback control law in order to reduce the steady-state errors due to parameter uncertainty. This paper also proposes an adaptive genetic algorithm (AGA) for the multi-objective optimization design of the tracking controller. The comprehensive results of simulation demonstrate that the PEMFC with nonlinear control has better transient and steady-state performance compared to conventional linear techniques.

In this paper, the design methods of decentralized PID controllers based on decoupled subsystems are proposed for two-input two-output systems. The higher-order decoupled subsystems are reduced into simple dynamics such as first-order plus dead-time or second-order plus dead-time and the dominant poles are placed at desired locations. The well tuned parameters of decentralized PID controllers can be obtained based on the movement of poles to get the desired closed-loop response of the system. A corollary derived from the generalized Nyquist stability theorem is used to ascertain the nominal system stability and to hold robust stability in the presence of the process multiplicative uncertainties. Finally, two simulation examples are provided for the validity and effectiveness of the proposed design methods. It can be observed that the high closed-loop performance is obtained using the proposed methods and it is comparable to recent methods available in the literature.

This paper studies the problems of modeling and input-output decoupling of generic hypersonic vehicles. Dynamical equations of hypersonic vehicles are derived using Lagrangian approach, which capture the dominating characteristics and primary interactions. Then, based on the simplified model, the original decoupling problem is reformulated as an asymptotical stability problem of the corresponding error system. The popular dynamic inversion is employed to design the decoupling controller, which can achieve steady-state decoupling. However, external disturbance will greatly destroy the effect of decoupling before the system reaches steady state. To this end, based on the error system, robust H ∞ theorem can be easily used to address this issue by reducing the impact of disturbance on error system outputs, which ultimately results in approximate decoupling. Moreover, the degree of approximate decoupling can be enhanced by choosing a small performance index γ . Simulations verify the effectiveness of proposed controllers.

This article describes how a field oriented control can provide the same performance as it is achieved by a DC motor. However, this technique requires a mechanic sensor and is very sensitive to the variation of motor parameters which results in an undesirable coupling between the flux and the torque. To solve these problems, this paper proposes a global stability and robust nonlinear controller, applied to induction motor (IM), in order to achieve an exact decoupling between speed and flux for all motor operating conditions. The induction motor is coupled with a centrifugal hydraulic pump, powered by a photovoltaic array speeding system. The proposed system is designed for usage in rural areas or remote electricity needs in absence of the grid network. A nonlinear controller adjusts the motor speed reference to attain the maximum power point (MPPT). In presence of rotor and stator resistances and irradiation disturbance the results obtained by simulations confirm the effectiveness of the proposed method.

Abstract The method introduced here is applicable for multi-input multi-output, linear, and time-invariant systems. The state and output equations of the system, which are originally expressed in the t-domain, are first transformed into the s-domain. Then, input-output decoupling is achieved by generating the actual control variables as combinations of virtual control variables in such a way that each output is controlled by only the dedicated one of the virtual control variables. As the next stage, appropriate linear controllers are designed to generate the virtual control variables with feedback, feedforward, and disturbance rejection features. The design method is algebraic and involves the assignment of not only the closed-loop poles but also the type numbers associated with the reference and disturbance inputs. The multivariable transformation filter that converts the virtual control variables into actual ones is formed properly to prevent any possibility of internal instability.

The robust Morgan's problem, via restricted static state feedback and a constant precompensator, is studied for nonsquare linear time-invariant systems with nonlinear uncertain structure. Sufficient solvability conditions are established. A class of static controller matrices, independent from the uncertainties, to solve the problem is explicitly characterized. For this class of controllers, the resulting form of the robustly decoupled closed-loop system is analytically determined. Finally, sufficient conditions are established for the solvability of the robust Morgan's problem with simultaneous robust stability, via restricted state feedback. The present results are successfully applied to control a vertical takeoff and landing aircraft.