Sliding Mode Controllers: Principles & Advances

Updated 23 June 2026

Sliding Mode Controllers (SMC) are nonlinear, discontinuous control laws that force system states onto a user-defined sliding manifold, offering finite-time reachability and robust invariance against disturbances.
SMC designs split into a reaching phase using high-gain discontinuous actions and a sliding phase with reduced-order dynamics, validated by Lyapunov-based stability proofs and convex cone arguments for uncertain gain conditions.
Chattering is mitigated through strategies such as boundary-layer smoothing, higher-order sliding mode techniques, and adaptive, data-driven methods, enhancing performance in safety-critical and discrete implementations.

Sliding mode controllers (SMC) are a class of nonlinear, discontinuous feedback control laws that induce robust invariance of system trajectories against matched uncertainties and disturbances by forcing the system state onto a user-defined sliding manifold. Once on this manifold, the resulting closed-loop reduced-order dynamics exhibit insensitivity to matched uncertainty and maintain prescribed convergence properties. SMCs are best known for their finite-time reachability, formal Lyapunov-based guarantees, and broad applicability to both finite- and infinite-dimensional systems. Modern SMC research spans higher-order SMC, adaptive and data-driven variants, chattering-reduction methodologies, and their integration in safety-critical and learning-augmented systems.

1. Mathematical Formulation and Fundamental Principles

Given a smooth, possibly uncertain dynamic system in affine form,

$\dot{x} = f(x, t) + G(x, t)\,u + n(t),$

the SMC design principle selects a sliding variable $s = o(x) \in \mathbb{R}^m$ (usually a function of state error and its derivatives), generating the sliding manifold $S = \{x : s(x, t) = 0\}$ . The controller is constructed such that:

Reaching phase: Trajectories are driven to $S$ in finite time by a discontinuous control, typically of the form $u_{\text{sw}} = -K\,\mathrm{sign}(s)$ , with switching gain $K$ chosen to overwhelm all matched uncertainties.
Sliding phase: Once $s=0$ , a reduced-order equivalent control $u_{\text{eq}}$ maintains invariance on $S$ , resulting in deterministic, disturbance-free dynamics for the sliding variable.

A canonical Lyapunov function is $V(s) = \frac{1}{2} s^T s$ , yielding finite-time convergence $s = o(x) \in \mathbb{R}^m$ 0 provided the gain in the switching term exceeds the uncertainty bounds (Rhif, 2012, Vu et al., 7 Aug 2025).

2. Existence and Uniqueness under Uncertain Gain: Affine Dynamic Systems

When the high-frequency gain matrix (HFGM) $s = o(x) \in \mathbb{R}^m$ 1 is uncertain, possibly non-deterministic and not positive definite—a scenario rarely covered by conventional SMC—the standard algebraic solution for the control law collapses. The approach in "A Novel Sliding Mode Control for a Class of Affine Dynamic Systems" formulates the SMC law as a nonlinear vector equation: $s = o(x) \in \mathbb{R}^m$ 2 where $s = o(x) \in \mathbb{R}^m$ 3 is a matrix of relative uncertainties, $s = o(x) \in \mathbb{R}^m$ 4 is a sign-matrix determined by the sliding variable, and $s = o(x) \in \mathbb{R}^m$ 5 amalgamates known and bounded uncertainty terms. The existence and uniqueness of the solution are proven using convex cone set arguments: for $s = o(x) \in \mathbb{R}^m$ 6 in any induced norm, these cones partition $s = o(x) \in \mathbb{R}^m$ 7 without overlap. The resulting controller is solved algorithmically by enumerating possible sign-matrices and selecting the solution $s = o(x) \in \mathbb{R}^m$ 8 with $s = o(x) \in \mathbb{R}^m$ 9.

This framework generalizes SMC to arbitrary affine systems under relaxed parametric assumptions, with theoretical guarantees (existence, uniqueness) even in the absence of positive-definite HFGM—a case where classical designs fail. Simulation studies on spacecraft attitude and robot manipulators demonstrate substantial gains in convergence and robustness over classical SMC under extensive model uncertainty (Feng et al., 2018).

3. Chattering, Discrete Implementation, and Higher-Order SMC

A major challenge in SMC is chattering: high-frequency oscillations arising from the signum nonlinearity, exacerbated by practical constraints (finite switching frequency, actuator bandwidth, sampling effects). Chattering excites unmodeled dynamics and can lead to physical wear or loss of precision.

Mitigation strategies include:

Boundary-layer methods: Replace $S = \{x : s(x, t) = 0\}$ 0 with a smooth saturation function over a boundary layer $S = \{x : s(x, t) = 0\}$ 1, introducing a trade-off between precision and chattering amplitude.
Higher-order sliding modes (HOSM): Design the controller to enforce both $S = \{x : s(x, t) = 0\}$ 2 and $S = \{x : s(x, t) = 0\}$ 3 (and possibly higher derivatives), as in the super-twisting algorithm or its discrete/adaptive versions. This distributes the switching discontinuity to a higher derivative, resulting in a continuous control law and further reducing chattering (Amini et al., 2018, Amini et al., 2017).
Discrete-time SMC: Specialized design is required for sampled-data systems to address quantization and aliasing. Second-order discrete SMC (DSMC), with adaptive estimation and Lyapunov-Invariance principle proofs, achieves dramatic robustness and chattering reduction when compared to first-order discrete SMC—improving performance up to 90% in automotive engine control (Amini et al., 2018).

A summary comparison of SMC chattering reduction approaches is presented below:

Mitigation method	Principle	Chattering Reduction
Boundary-layer	Signum $S = \{x : s(x, t) = 0\}$ 4 saturation/smooth	O( $S = \{x : s(x, t) = 0\}$ 5) band; steady-state error possible
HOSM/Super-twisting	Enforce $S = \{x : s(x, t) = 0\}$ 6 and $S = \{x : s(x, t) = 0\}$ 7	Continuous control; superior reduction
Discrete HOSM	Second-order increments in $S = \{x : s(x, t) = 0\}$ 8	Digital implementation, minimal chattering

4. Extensions: Adaptive, Data-Driven, Learning-Augmented SMC

SMC robustness can be further extended by augmenting classical designs:

Adaptive and Estimation-Based SMC: Adaptive laws are embedded to estimate unknown plant parameters online, based on Lyapunov stability analysis. For instance, adaptive discrete SMC autonomously estimates and compensates multiplicative gain uncertainty, ensuring asymptotic convergence without a priori knowledge of uncertainty bounds (Amini et al., 2018).
Data-Driven SMC: Where model structure is partially unknown, SMCs can be synthesized using input-output data. Controller synthesis leverages robust reaching laws and stability-enforcing semidefinite programs (SDP), achieving $S = \{x : s(x, t) = 0\}$ 9-stability of the sliding-phase dynamics and finite-time reachability of the surface, even for multi-input multi-output, nonlinear, disturbance-affected systems (Lan et al., 2024).
Learning-Augmented SMC: SMC is combined with policy-gradient-based reinforcement learning, where the SMC gives the baseline robust structure and RL fills in the unknown/unmatched dynamics. This approach yields improved tracking and chattering reduction, with the SMC term imposing a provable Lyapunov certificate even under uncertain or partially-trained RL policies (Mosharafian et al., 2022, Sayyed et al., 19 Jan 2026).

5. SMC Design for Safety-Critical and Constrained Systems

Modern SMC architectures address broader requirements, including formal safety constraints, collision avoidance, and strict actuator limitations:

Control Barrier Function (CBF) Integration: SMCs are incorporated with high-order control barrier functions (HOCBFs) or real-time CBF-QP filters, ensuring forward invariance of safety sets (e.g., collision cones in mobile robots and marine vessels). Computationally efficient projection methods allow SMC to degrade gracefully only when safety is at risk, preserving sliding-mode robustness otherwise (Syntakas et al., 30 Dec 2025, Sawarkar et al., 27 Apr 2026).
Hybrid Feedforward-Feedback Architectures: In complex systems, such as flight control, deep RL policies provide sophisticated feedforward commands for nonlinear or underactuated behaviors, while SMC layers guarantee constraint satisfaction and “core” robustness to disturbances/adversarial effects. Sliding-mode feedback is scaled and tuned via explicit Lyapunov-based bounds linked to anticipated RL error and actuator limits (Sayyed et al., 19 Jan 2026).

6. Application Domains and Experimental Validation

SMC methodologies have achieved widespread experimental validation and industrial deployment across domains, including:

Electric drives: SMC (integral, terminal, high-order, adaptive, and fractional-order) has been benchmarked for speed and precision regulation of permanent-magnet synchronous motors. Super-twisting and adaptive SMCs consistently offer optimal compromise between error, chattering, and computational cost (Aremu et al., 7 Dec 2025).
Motion control and robotics: Hybrid SMC-PID and discrete/higher-order SMC laws outperform classical PID designs in DC motors and piezoelectric actuators, with experimentally validated improvements in speed, setpoint tracking, and disturbance rejection (Vu et al., 7 Aug 2025, Rhif, 2012, Yan et al., 2020).
Aerospace: SMC controllers for reentry, descent, and launch vehicle attitude management exploit sliding-mode invariance principles and adaptive/higher-order structures to counteract severe, rapidly varying perturbations, as implemented in open-source design toolboxes (Kode et al., 2021).
Underactuated and nonholonomic vehicles: Discontinuous SMC structures have been tailored for mixed-braking actuation, Ackermann steering, and mobile robot navigation, with formal guarantees and real-world verification (Nikshi et al., 2019, Sawarkar et al., 27 Apr 2026).

7. Theoretical and Practical Considerations

The SMC paradigm is underpinned by rigorous Lyapunov-theoretic analysis, convex geometry (uniqueness in uncertain gain settings), and reachability arguments. Nonetheless, practical implementation of SMCs is sensitive to the fidelity of disturbance bounds, system relative degree, digital sampling constraints, and actuator nonidealities. Design guidelines specify gains just above conservative disturbance estimates and recommend partial-model feedforward compensation to reduce required switching authority, directly impacting chattering. Recent advances emphasize adaptive, observer-based, and boundary-layer strategies for reducing conservatism without loss of robustness.

Key SMC issues and corresponding methodologies are summarized below:

Issue	Representative SMC solution	Reference
Uncertain/non-positive gain	Nonlinear vector equation, convex cone analysis	(Feng et al., 2018)
Chattering	Boundary-layer smoothing, super-twisting HOSM	(Amini et al., 2018)
Digital/sampled data	Adaptive second-order discrete SMC	(Amini et al., 2018, Amini et al., 2017)
Partial/unknown model	Data-driven robust SMC via SDP	(Lan et al., 2024)
Learning augmentation	SMC–RL hybrid control, Lyapunov-robust feedback	(Mosharafian et al., 2022, Sayyed et al., 19 Jan 2026)
Safety under constraints	SMC with CBFs/HOCBFs and fast projection/filter	(Syntakas et al., 30 Dec 2025, Sawarkar et al., 27 Apr 2026)

Sliding mode control has thus expanded from its origins in signum-based robust feedback to a comprehensive set of methodologies supporting adaptation, data-driven design, learning integration, and formal safety assurances, with a mature framework for both theoretical analysis and experimental deployment across diverse domains (Feng et al., 2018, Amini et al., 2018, Syntakas et al., 30 Dec 2025, Sayyed et al., 19 Jan 2026, Lan et al., 2024).